| [ | |
| { | |
| "Name": "adaptive_block_size", | |
| "Title": "Adaptive Block Size: Dynamic Context Window Adjustment for Efficient Training", | |
| "Experiment": "Modify the model to dynamically adjust its block size during training, starting with a smaller block size and gradually increasing it. This could potentially lead to faster initial training and better long-range dependency learning.", | |
| "Interestingness": 6, | |
| "Feasibility": 4, | |
| "Novelty": 4, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "layerwise_learning_rates", | |
| "Title": "Layer-wise Learning Rate Adaptation: Optimizing Training Dynamics in Transformer Models", | |
| "Experiment": "Implement layer-wise learning rates, where each transformer layer has its own learning rate. Modify the configure_optimizers function to assign different learning rates to different layers, with deeper layers having lower learning rates. Compare the training dynamics, convergence speed, and final performance with the baseline model.", | |
| "Interestingness": 4, | |
| "Feasibility": 6, | |
| "Novelty": 2, | |
| "novel": false | |
| }, | |
| { | |
| "Name": "power_of_two_sparse_attention", | |
| "Title": "Power-of-Two Sparse Attention: Enhancing Efficiency in Transformer Models", | |
| "Experiment": "Modify the CausalSelfAttention class to implement a power-of-two sparse attention pattern. Create a boolean mask where each token attends to previous tokens at indices 2^n away (1, 2, 4, 8, 16, etc.). Update the forward method to apply this mask during attention computation, setting attention scores to -inf for masked positions before softmax. Compare training speed (tokens/second), peak memory usage, and validation perplexity against the baseline full attention model across all three datasets.", | |
| "Interestingness": 8, | |
| "Feasibility": 8, | |
| "Novelty": 7, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "hybrid_char_bigram_tokenization", | |
| "Title": "Hybrid Character-Bigram Tokenization: Combining Fine-grained and Coarse-grained Input Representations", | |
| "Experiment": "Modify the data loading process to create both character-level and bigram-level representations. Update the GPT model to have two parallel embedding layers: one for characters and one for bigrams. Concatenate these embeddings in the forward pass before the transformer layers. Compare this hybrid model's training speed, inference speed, and validation perplexity against the baseline character-level model on all three datasets.", | |
| "Interestingness": 8, | |
| "Feasibility": 7, | |
| "Novelty": 7, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "simplified_sam_optimization", | |
| "Title": "Simplified Sharpness-Aware Minimization for Improved Generalization in Small Language Models", | |
| "Experiment": "Implement a simplified version of Sharpness-Aware Minimization (SAM) by adding a single additional gradient computation step to the existing optimizer. Modify the training loop to perform this extra step every N iterations. Compare training loss, validation loss, and the difference between them (as a measure of generalization) with the baseline optimizer. Experiment with different values of the SAM step size (\u03c1) and frequency of SAM steps.", | |
| "Interestingness": 8, | |
| "Feasibility": 8, | |
| "Novelty": 7, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "char_level_transfer_learning", | |
| "Title": "Character-Level Transfer Learning: Investigating Cross-Domain Adaptation in Small Language Models", | |
| "Experiment": "Modify the training script to support two-phase training: (1) pre-training on a source dataset, (2) fine-tuning on a target dataset, using the same model architecture throughout. Implement functions to save the best model from pre-training and load it for fine-tuning. Compare performance (validation loss, perplexity, and inference speed) of the fine-tuned model against a model trained from scratch on the target dataset. Analyze adaptation speed by comparing validation loss curves. Focus on two specific transfer scenarios: shakespeare_char to enwik8 (literary to general web text) and enwik8 to text8 (unfiltered to filtered web text). Examine learned character embeddings and attention patterns to identify transferable knowledge.", | |
| "Interestingness": 8, | |
| "Feasibility": 9, | |
| "Novelty": 7, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "char_aware_initialization", | |
| "Title": "Character-Aware Initialization for Improved Training of Small Language Models", | |
| "Experiment": "Modify the _init_weights method in the GPT class to support three initialization schemes: standard PyTorch init (baseline), scaled normal init (adapted from GPT-2), and a novel character-aware init. Implement the character-aware init by initializing the embedding layer weights based on character frequency in the training data. Add a new hyperparameter to select the initialization scheme. Run experiments with each scheme on all three datasets, tracking training loss, validation loss, convergence speed, and final performance. Analyze the impact of each scheme on model components, particularly the embedding and first few layers. Compare training stability and generalization across schemes. Visualize embedding spaces and attention patterns to understand how different initializations affect the model's internal representations.", | |
| "Interestingness": 8, | |
| "Feasibility": 9, | |
| "Novelty": 7, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "gradient_noise_scaling", | |
| "Title": "Gradient Noise Scaling: Enhancing Generalization in Small Character-Level Language Models", | |
| "Experiment": "Modify the training loop to add Gaussian noise to gradients after the backward pass but before the optimizer step. Implement noise scaling factor \u03b7(t) = \u03b7\u2080 / (1 + t)^0.55, where \u03b7\u2080 is the initial noise level and t is the current iteration. Add hyperparameters for \u03b7\u2080 and the decay power (0.55). Compare models trained with and without gradient noise across all datasets, measuring: 1) validation perplexity, 2) generalization gap, 3) inference speed (tokens/second), and 4) sample quality via human evaluation. Analyze the impact of noise on learned embeddings and attention patterns. Experiment with \u03b7\u2080 \u2208 {0.01, 0.1, 0.5} and decay powers {0.55, 0.65} to find optimal settings.", | |
| "Interestingness": 8, | |
| "Feasibility": 9, | |
| "Novelty": 7, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "model_scaling_study", | |
| "Title": "Scaling Laws for Small Character-Level Language Models: Optimizing Performance and Efficiency Across Diverse Datasets", | |
| "Experiment": "Modify the training script to support multiple model sizes. Create a grid of GPT configurations: n_layer [2, 4, 6, 8, 12], n_embd [128, 256, 384, 512, 768], n_head [2, 4, 6, 8, 12]. Train models for each configuration on all three datasets (shakespeare_char, enwik8, text8). Track validation perplexity, training time, inference speed (tokens/second), and peak memory usage for each configuration. Implement early stopping with a patience of 5 epochs. Plot performance metrics against model size (number of parameters) for each dataset. Analyze the efficiency frontier by identifying models with the best performance-to-size ratio. Examine how different architectural aspects (depth vs. width) affect performance across datasets. Provide recommendations for optimal model sizes for different computational budgets and dataset characteristics.", | |
| "Interestingness": 9, | |
| "Feasibility": 9, | |
| "Novelty": 8, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "char_level_activation_study", | |
| "Title": "Optimizing Activation Functions for Character-Level Language Models: A Comparative Study", | |
| "Experiment": "Modify the MLP class to support three activation functions: ReLU (baseline), GELU, and Mish. Add a hyperparameter for activation function selection. Train models with each activation on all three datasets (shakespeare_char, enwik8, text8). Track validation perplexity, training time, inference speed (tokens/second), and loss curve smoothness. Analyze performance across different sequence lengths to understand how activations affect short-range vs. long-range dependencies. Examine learned character embeddings to identify how activations influence the model's character-level representations. Compare the frequency of saturation (near-zero gradients) for each activation during training. Provide guidelines for choosing activations based on dataset characteristics and model size.", | |
| "Interestingness": 8, | |
| "Feasibility": 9, | |
| "Novelty": 7, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "multi_scale_char_attention", | |
| "Title": "Multi-Scale Character Attention: Enhancing Hierarchical Learning in Small Language Models", | |
| "Experiment": "Modify the CausalSelfAttention class to implement multi-scale attention with three fixed scales: single character, trigram, and 5-gram. Update the forward method to compute attention scores for each scale in parallel, then combine them using learnable weights. Implement efficient masking for different scales using torch.tril with appropriate offsets. Compare this model against the baseline on all three datasets, measuring validation perplexity, inference speed, and perplexity on held-out samples of varying lengths (to test both short and long-range coherence). Analyze attention patterns and learned combination weights across scales to understand what the model learns at each level. Conduct ablation studies by training models with different combinations of scales.", | |
| "Interestingness": 9, | |
| "Feasibility": 8, | |
| "Novelty": 8, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "adaptive_char_curriculum", | |
| "Title": "Adaptive Character-Level Curriculum Learning: Optimizing Sequence Length and Vocabulary Complexity", | |
| "Experiment": "Modify get_batch to accept current_seq_length and current_vocab_size parameters. Implement an adaptive curriculum that adjusts these parameters based on validation loss improvement rate. Start with seq_length=64 and top-50% most frequent characters (determined by counting occurrences in the training data), gradually increasing to full length (256) and full vocabulary. Update the training loop to use this adaptive curriculum, adjusting every 100 iterations based on average validation loss improvement. Compare against baseline model on all datasets, tracking: training/validation loss, convergence speed, and final performance. Analyze attention patterns and embedding spaces at different curriculum stages. Examine how curriculum affects learning of common vs. rare characters and short vs. long-range dependencies. Consider potential challenges such as curriculum pacing and impact on model's ability to handle out-of-distribution sequences.", | |
| "Interestingness": 9, | |
| "Feasibility": 7, | |
| "Novelty": 8, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "hierarchical_char_positional_encoding", | |
| "Title": "Hierarchical Character Positional Encoding: Enhancing Multi-scale Structure Learning in Small Language Models", | |
| "Experiment": "Modify the GPT class to implement a hierarchical positional encoding scheme. Create a new function to generate encodings that combine: (1) character position within a 5-char window, (2) 5-char window position within a 25-char window, (3) 25-char window position within the full sequence. Sum these encodings with the token embeddings before feeding into the transformer layers. Compare this model against an identical architecture using standard positional encoding across all datasets, measuring: validation perplexity, inference speed, and performance on a custom task of predicting the next character given (a) the previous character, (b) the previous 5 characters, and (c) the previous 25 characters. Analyze attention patterns at different layers to understand how the model utilizes the hierarchical information.", | |
| "Interestingness": 8, | |
| "Feasibility": 9, | |
| "Novelty": 8, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "adaptive_cyclic_char_encoding", | |
| "Title": "Adaptive Cyclic Character Encoding: Enhancing Word Boundary Learning in Character-Level Models", | |
| "Experiment": "1. Modify the GPT class to implement an adaptive cyclic positional encoding function. Calculate the average word length in the training data and use this as the initial cycle length. 2. Update the forward method to use this adaptive encoding scheme. 3. Implement a mechanism to gradually adjust the cycle length during training based on the model's performance. 4. Train models with adaptive cyclic and standard positional encodings on all datasets, comparing validation perplexity, inference speed, and generated sample quality. 5. Analyze attention patterns and learned embeddings to understand how the model utilizes the adaptive cyclic structure. 6. Evaluate the model's ability to handle OOV words by testing on a held-out set of rare words and comparing perplexity with the baseline model. 7. Examine how the optimal cycle length varies across different datasets and its correlation with linguistic properties.", | |
| "Interestingness": 9, | |
| "Feasibility": 8, | |
| "Novelty": 8, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "context_aware_soft_boundary", | |
| "Title": "Context-Aware Soft Boundary Attention: Enhancing Linguistic Structure Learning in Character-Level Models", | |
| "Experiment": "1. Implement a small neural network (e.g., 2-layer MLP) that predicts boundary scores based on a window of surrounding characters. 2. Modify the CausalSelfAttention class to use these predicted boundary scores as attention biases. 3. Train models with this new attention mechanism on all three datasets, comparing against the baseline on validation perplexity, inference speed, and generated sample quality. 4. Analyze learned boundary patterns across different datasets and their correlation with linguistic structures. 5. Evaluate model performance on tasks requiring different levels of linguistic understanding: character prediction, subword completion, and next word prediction. 6. Examine attention patterns and their changes across model layers to understand how the soft boundaries influence hierarchical learning. 7. Conduct ablation studies to quantify the impact of the context window size on boundary prediction and overall model performance.", | |
| "Interestingness": 9, | |
| "Feasibility": 8, | |
| "Novelty": 8, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "dual_resolution_char_encoding", | |
| "Title": "Dual-Resolution Character Encoding: Enhancing Character-Level Language Models with N-gram Information", | |
| "Experiment": "1. Modify the GPT class to implement a dual-resolution character encoding scheme, combining character-level and n-gram level information. 2. Create a single embedding layer that encodes both individual characters and their n-gram context (e.g., current char + previous (n-1) chars). 3. Implement a function to generate n-gram representations on-the-fly during data loading. 4. Adjust the model's input layer to accept the combined character and n-gram embeddings. 5. Train models with this new encoding scheme on all three datasets, comparing against the baseline on validation perplexity, inference speed, and generated sample quality. 6. Analyze the learned embeddings to understand how character-level and n-gram information is captured. 7. Evaluate model performance on tasks such as next-character prediction and rare word handling. 8. Experiment with different values of n (2, 3, 4) to find the optimal n-gram size for each dataset.", | |
| "Interestingness": 8, | |
| "Feasibility": 8, | |
| "Novelty": 7, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "cyclic_attention_bias", | |
| "Title": "Cyclic Attention Bias: Implicit Sentence Structure Learning in Character-Level Models", | |
| "Experiment": "1. Modify the CausalSelfAttention class to incorporate a fixed-length cyclic attention bias. 2. Implement a sinusoidal function to generate this bias, with a cycle length of 50 characters (approximating average sentence length). 3. Add the cyclic bias to the attention scores before softmax. 4. Train models with this new attention mechanism on all three datasets. 5. Compare against the baseline on: validation perplexity, inference speed, and perplexity on held-out samples of varying lengths (10, 50, 100 characters). 6. Visualize attention patterns at different layers to identify any learned sentence-like structures. 7. Evaluate the model's ability to capture long-range dependencies by measuring perplexity on the task of predicting the last character of 50-character sequences. 8. Analyze how the cyclic bias affects the learning of punctuation and capitalization patterns.", | |
| "Interestingness": 8, | |
| "Feasibility": 9, | |
| "Novelty": 8, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "global_context_vector", | |
| "Title": "Global Context Vector: Enhancing Long-Range Understanding in Character-Level Language Models", | |
| "Experiment": "1. Implement a function to compute a Global Context Vector (GCV) using average pooling over the input sequence. 2. Modify the GPT class to store the GCV and inject it at specific layers (e.g., every 2 layers). 3. Update the forward method to concatenate or add the GCV to the hidden states at injection points. 4. Train models with and without GCV on all three datasets, comparing validation perplexity, inference speed, training time, and generated sample quality. 5. Analyze the impact of GCV on attention patterns and hidden state representations. 6. Evaluate the model's performance on tasks requiring long-range understanding, such as predicting the last character of long sequences. 7. Experiment with different GCV injection methods (concatenation vs. addition) and injection frequencies.", | |
| "Interestingness": 8, | |
| "Feasibility": 9, | |
| "Novelty": 8, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "local_context_attention", | |
| "Title": "Local Context-Sensitive Attention: Enhancing Character-Level Language Models with Adaptive Attention Patterns", | |
| "Experiment": "1. Modify the CausalSelfAttention class to compute a 'local context vector' for each position using a small window of surrounding characters (e.g., 5 characters before and after). 2. Use this local context vector to modulate the attention weights through a simple transformation (e.g., element-wise multiplication). 3. Train models with this new attention mechanism on all three datasets. 4. Compare against the baseline on validation perplexity, inference speed, and generated sample quality. 5. Analyze how attention patterns change based on local context across different datasets and text styles. 6. Evaluate the model's adaptability by measuring perplexity on held-out samples from specific genres or writing styles. 7. Experiment with different window sizes for the local context to find the optimal balance between performance and computational overhead.", | |
| "Interestingness": 8, | |
| "Feasibility": 9, | |
| "Novelty": 7, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "selective_retention_attention", | |
| "Title": "Selective Retention Attention: Enhancing Long-Range Coherence in Character-Level Language Models", | |
| "Experiment": "1. Modify the CausalSelfAttention class to include a learnable 'retention score' vector. 2. Update the attention computation to incorporate the retention scores, modulating the attention weights. 3. Implement a simple mechanism to update retention scores based on the current context. 4. Train models with and without selective retention attention on all three datasets. 5. Compare validation perplexity, inference speed, and generated sample quality, focusing on long-range coherence. 6. Analyze the learned retention scores to understand what types of information the model prioritizes. 7. Evaluate performance on tasks requiring long-range understanding, such as predicting characters at varying distances. 8. Experiment with different retention score update mechanisms to optimize performance.", | |
| "Interestingness": 8, | |
| "Feasibility": 9, | |
| "Novelty": 8, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "adaptive_memory_bank", | |
| "Title": "Adaptive Memory Bank: Enhancing Long-Range Coherence in Character-Level Language Models", | |
| "Experiment": "1. Modify the GPT class to include an Adaptive Memory Bank (AMB) with 5 memory slots. 2. Implement an update function for the AMB using a gating mechanism (similar to LSTM) in the forward pass. 3. Adjust the forward method to incorporate AMB information before the self-attention layer. 4. Train models with and without AMB on all three datasets. 5. Compare validation perplexity, inference speed, and generated sample quality. 6. Measure perplexity on specific long-range tasks (e.g., predicting characters 100, 200, and 300 steps ahead). 7. Analyze AMB contents during inference to understand stored information. 8. Evaluate performance on maintaining consistent style/topic over long generated sequences (500+ characters).", | |
| "Interestingness": 9, | |
| "Feasibility": 9, | |
| "Novelty": 8, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "char_style_statistics", | |
| "Title": "Character-Level Style Statistics: Enhancing Local Coherence in Language Models", | |
| "Experiment": "1. Implement a CharStyleStats class to compute and update rolling statistics on capitalization, punctuation, and character repetition using a 50-character window. 2. Modify the GPT class to include CharStyleStats, updating it during the forward pass. 3. Adjust the forward method to incorporate style statistics before the first transformer layer. 4. Train models with and without style statistics on all three datasets. 5. Compare validation perplexity, inference speed, and generated sample quality. 6. Evaluate local coherence by measuring consistency of style features over 100-character windows. 7. Test the model's adaptability to style changes using artificially created sequences with abrupt style shifts. 8. Analyze how different style features influence the model's predictions.", | |
| "Interestingness": 8, | |
| "Feasibility": 9, | |
| "Novelty": 7, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "adaptive_style_attention", | |
| "Title": "Adaptive Style Attention: Enhancing Style Consistency in Character-Level Language Models", | |
| "Experiment": "1. Implement an AdaptiveStyleEmbedding class that computes a rolling average of character embeddings over a fixed window. 2. Modify the CausalSelfAttention class to accept the style embedding as an additional input. 3. Update the attention computation to incorporate the style embedding, using it to bias the attention weights. 4. Train models with and without adaptive style attention on all three datasets. 5. Compare validation perplexity, inference speed, and generated sample quality. 6. Evaluate style consistency using a pre-trained style classifier on generated sequences of varying lengths (100, 500, 1000 characters). 7. Analyze how attention patterns change based on different style embeddings. 8. Experiment with different window sizes for the rolling average to find the optimal balance between adaptability and stability.", | |
| "Interestingness": 9, | |
| "Feasibility": 9, | |
| "Novelty": 8, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "multi_style_adapter", | |
| "Title": "Multi-Style Adapter: Enhancing Style Awareness and Consistency in Character-Level Language Models", | |
| "Experiment": "1. Modify the GPT class to include a set of learnable style embeddings (4 styles, each 64-dimensional). 2. Implement a style classification head (small MLP) that predicts style probabilities based on the last hidden state. 3. Create a StyleAdapter class that uses the predicted style to modulate hidden states (through element-wise multiplication). 4. Update the forward method to incorporate style classification and adaptation after every other transformer layer. 5. Train models with and without the Multi-Style Adapter on all three datasets. 6. Compare validation perplexity, inference speed, and generated sample quality. 7. Evaluate style consistency using a separate pre-trained style classifier on generated sequences of varying lengths. 8. Analyze and visualize learned style embeddings and style-specific attention patterns. 9. Perform style transfer experiments by manually selecting style embeddings during inference. 10. Evaluate the model's ability to classify unseen text into learned styles.", | |
| "Interestingness": 9, | |
| "Feasibility": 9, | |
| "Novelty": 9, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "rare_char_boosting", | |
| "Title": "Rare Character Boosting: Improving Representation and Prediction of Uncommon Characters in Language Models", | |
| "Experiment": "1. Implement a function to compute character frequencies from the training data, storing results in a dictionary. 2. Create a rarity_score function that converts frequencies to rarity scores (e.g., inverse frequency). 3. Modify the training loop to compute rarity scores once per epoch. 4. Update the loss computation in the forward method to apply a boosting factor based on the rarity score of each character. 5. Train models with and without Rare Character Boosting on all three datasets. 6. Compare validation perplexity, with particular attention to perplexity on rare character sequences. 7. Evaluate the model's ability to generate and complete sequences containing rare characters. 8. Analyze how the boosting mechanism affects attention patterns and embedding spaces for rare vs. common characters.", | |
| "Interestingness": 9, | |
| "Feasibility": 9, | |
| "Novelty": 8, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "soft_word_boundary_attention", | |
| "Title": "Soft Word Boundary Attention: Enhancing Semantic Coherence in Character-Level Language Models", | |
| "Experiment": "1. Modify the GPT class to include a small neural network that predicts boundary scores for each character position. 2. Update the CausalSelfAttention class to use these boundary scores as an additional attention bias. 3. Implement a gating mechanism in the forward method that uses boundary scores to modulate hidden state updates. 4. Train models with and without the soft word boundary mechanism on all three datasets. 5. Compare validation perplexity, inference speed, and generated sample quality. 6. Evaluate semantic coherence using a sliding window perplexity measure over varying sequence lengths (50, 100, 200 characters). 7. Test the model's ability to predict the next word given a character sequence, using a simplified word completion task. 8. Analyze learned boundary patterns and visualize attention maps to understand the mechanism's impact.", | |
| "Interestingness": 9, | |
| "Feasibility": 9, | |
| "Novelty": 9, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "adaptive_importance_attention", | |
| "Title": "Adaptive Importance-Weighted Attention: Enhancing Long-Range Coherence in Character-Level Models", | |
| "Experiment": "1. Modify the CausalSelfAttention class to include a small MLP that predicts 'importance scores' for each position. 2. Implement a mechanism to use these scores to directly modulate attention weights. 3. Add a gating mechanism to learn when to apply the importance-based modulation. 4. Update the forward method to incorporate this adaptive attention mechanism. 5. Train models with and without adaptive importance-weighted attention on all three datasets. 6. Compare validation perplexity, inference speed, and generated sample quality, focusing on long-range coherence (e.g., 500+ character sequences). 7. Evaluate performance on tasks requiring long-range understanding, such as predicting characters at varying distances (50, 100, 200 steps ahead). 8. Analyze learned importance patterns and gating behavior across different datasets and text styles. 9. Visualize attention maps to understand how the adaptive mechanism affects the model's focus.", | |
| "Interestingness": 9, | |
| "Feasibility": 9, | |
| "Novelty": 8, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "dual_scale_transformer", | |
| "Title": "Dual-Scale Transformer: Enhancing Hierarchical Understanding in Character-Level Language Models", | |
| "Experiment": "1. Modify the GPT class to implement a dual-scale transformer architecture with character-level and n-gram level processing (n as a hyperparameter). 2. Update the CausalSelfAttention class to compute attention separately for each scale. 3. Implement a combination mechanism using concatenation followed by linear projection. 4. Train models with different n-gram sizes (3, 5, 7) and the baseline on all three datasets. 5. Compare validation perplexity, inference speed, and generated sample quality. 6. Evaluate long-range coherence by measuring perplexity on sequences of varying lengths (100, 500, 1000 characters). 7. Assess hierarchical understanding using a word boundary prediction task. 8. Analyze attention patterns at different scales to understand how the model utilizes dual-scale information.", | |
| "Interestingness": 9, | |
| "Feasibility": 9, | |
| "Novelty": 9, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "phoneme_aware_char_encoding", | |
| "Title": "Phoneme-Aware Character Encoding: Enhancing Linguistic Structure Learning in Character-Level Models", | |
| "Experiment": "1. Implement a simplified rule-based character-to-phoneme mapping for common English phonetic patterns. 2. Modify the GPT class to include a shared embedding layer for characters and phonemes. 3. Create a function to generate phoneme representations for input sequences, using the simplified mapping. 4. Update the forward method to combine character and phoneme information through element-wise addition of their embeddings. 5. Train models with and without phoneme-aware encoding on all three datasets. 6. Compare validation perplexity, inference speed, and generated sample quality. 7. Evaluate performance on a custom task of predicting whether two words rhyme based on their character sequences, using held-out data. Compare performance against the baseline model. 8. Analyze attention patterns to understand how the model utilizes phonetic information across different layers.", | |
| "Interestingness": 9, | |
| "Feasibility": 9, | |
| "Novelty": 9, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "hierarchical_chunk_attention", | |
| "Title": "Hierarchical Chunk Attention: Enhancing Long-Range Coherence in Character-Level Language Models", | |
| "Experiment": "1. Modify the GPT class to implement a two-level attention mechanism: character-level and chunk-level. 2. Implement a simple, rule-based chunk segmentation method (e.g., fixed-length chunks of 5 characters). 3. Update the CausalSelfAttention class to compute attention scores at both character and chunk levels. 4. Implement a weighted sum mechanism with learnable weights to combine character-level and chunk-level attention outputs. 5. Train models with and without Hierarchical Chunk Attention on all three datasets. 6. Compare validation perplexity, inference speed, and generated sample quality, with a focus on long-range coherence (1000+ character sequences). 7. Analyze attention patterns at both levels to understand how the model utilizes the hierarchical information. 8. Experiment with different fixed chunk sizes (3, 5, 7 characters) to optimize performance.", | |
| "Interestingness": 9, | |
| "Feasibility": 7, | |
| "Novelty": 9, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "adaptive_style_memory", | |
| "Title": "Adaptive Style Memory: Enhancing Stylistic Consistency in Character-Level Language Models", | |
| "Experiment": "1. Implement an AdaptiveStyleMemory class that maintains decaying statistics on capitalization, punctuation, and character n-gram frequencies. 2. Modify the CausalSelfAttention class to accept style statistics as input. 3. Update the attention computation to use style statistics as an additional bias term. 4. Modify the GPT class to include the AdaptiveStyleMemory module, updating it every 10 forward passes. 5. Train models with and without Adaptive Style Memory on all three datasets. 6. Compare validation perplexity, inference speed, and generated sample quality, focusing on stylistic consistency over long sequences (500+ characters). 7. Evaluate stylistic consistency using a pre-trained style classifier on generated sequences of varying lengths. 8. Analyze how the Adaptive Style Memory affects attention patterns and predictions for different stylistic elements.", | |
| "Interestingness": 9, | |
| "Feasibility": 8, | |
| "Novelty": 9, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "sliding_window_pseudo_words", | |
| "Title": "Sliding Window Pseudo-Word Formation: Enhancing Semantic Coherence in Character-Level Language Models", | |
| "Experiment": "1. Implement a sliding_window_pseudo_word function that creates pseudo-word embeddings from a fixed-size window (e.g., 5 characters) of the input sequence. 2. Modify the GPT class to compute pseudo-word embeddings for each position in the input sequence. 3. Update the CausalSelfAttention class to use both character and pseudo-word embeddings in computing attention scores (e.g., by concatenation). 4. Train models with and without sliding window pseudo-words on all three datasets. 5. Compare validation perplexity, inference speed, and generated sample quality, focusing on semantic coherence over long sequences (500+ characters). 6. Evaluate the model's ability to capture word-like structures by comparing the generated pseudo-words to actual words in the dataset using a simple string matching metric. 7. Analyze attention patterns to understand how the model utilizes pseudo-word information across different layers.", | |
| "Interestingness": 9, | |
| "Feasibility": 9, | |
| "Novelty": 8, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "adaptive_neuron_gating", | |
| "Title": "Adaptive Neuron Gating: Dynamic Capacity Control in Character-Level Language Models", | |
| "Experiment": "1. Implement a small GatingNetwork class that predicts activation masks for each layer based on input n-grams. 2. Modify the GPT class to include the GatingNetwork and apply masks to layer outputs. 3. Update the forward method in Block class to apply neuron gating after self-attention and feedforward operations. 4. Train models with and without adaptive neuron gating on all three datasets. 5. Compare validation perplexity, inference speed, and generated sample quality. 6. Analyze activation patterns for different types of input sequences. 7. Evaluate computational efficiency by measuring effective FLOPs for processing sequences of varying complexity. 8. Experiment with different gating granularities (e.g., individual neurons vs. groups of neurons). 9. Investigate correlations between gating patterns and linguistic features (e.g., word boundaries, punctuation, rare characters).", | |
| "Interestingness": 9, | |
| "Feasibility": 8, | |
| "Novelty": 8, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "discrete_context_activations", | |
| "Title": "Discrete Context-Dependent Activation Functions: Enhancing Interpretable Adaptive Computation in Character-Level Language Models", | |
| "Experiment": "1. Define a set of 4 activation functions (e.g., ReLU, GELU, Swish, Mish). 2. Implement a lightweight ContextClassifier that predicts the best activation function every 10 time steps. 3. Modify the MLP class to switch between activation functions based on the ContextClassifier output. 4. Update the forward method in the Block class to incorporate the ContextClassifier and pass its prediction to the MLP. 5. Train models with and without discrete context-dependent activations on all three datasets. 6. Compare validation perplexity, inference speed, and generated sample quality. 7. Analyze the distribution of selected activation functions for different types of input sequences and across different layers. 8. Evaluate the model's adaptability by measuring perplexity on held-out samples from specific genres or writing styles. 9. Assess computational efficiency by comparing FLOPs and memory usage against the baseline model.", | |
| "Interestingness": 9, | |
| "Feasibility": 9, | |
| "Novelty": 9, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "adaptive_layer_fusion", | |
| "Title": "Adaptive Layer Fusion: Enhancing Multi-scale Feature Learning in Character-Level Language Models", | |
| "Experiment": "1. Modify the Block class to include an AdaptiveFusion module. 2. Implement AdaptiveFusion as a small neural network that computes fusion weights based on the current layer input and output. 3. Update the forward method in Block to apply a weighted sum of the current layer output and the previous layer output (or input embedding for the first layer). 4. Train models with and without Adaptive Layer Fusion on all three datasets. 5. Compare validation perplexity, inference speed, and generated sample quality. 6. Implement a multi-scale character prediction task (predicting characters at 1, 5, and 10 steps ahead) and evaluate performance. 7. Analyze fusion weights across different layers and input patterns to understand the model's adaptive behavior. 8. Experiment with different architectures for the fusion weight computation (e.g., single layer vs. two-layer neural network). 9. Measure and compare the computational overhead introduced by the fusion mechanism.", | |
| "Interestingness": 9, | |
| "Feasibility": 9, | |
| "Novelty": 8, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "dynamic_context_scaling", | |
| "Title": "Dynamic Context Scaling: Adaptive Balance of Local and Global Information in Character-Level Language Models", | |
| "Experiment": "1. Modify the CausalSelfAttention class to include a position-based scaling factor that increases with sequence length. 2. Update the attention computation to use this scaling factor, adjusting the balance between local and global attention. 3. Train models with and without Dynamic Context Scaling on all three datasets. 4. Compare validation perplexity, inference speed, and generated sample quality, with a focus on long-range coherence (500+ characters). 5. Evaluate performance on a custom task of predicting characters at varying distances (1, 50, 100 steps ahead). 6. Analyze how the scaling factor affects attention patterns across different sequence lengths. 7. Experiment with different scaling factor functions (e.g., linear, logarithmic) to optimize performance.", | |
| "Interestingness": 9, | |
| "Feasibility": 9, | |
| "Novelty": 8, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "implicit_morpheme_attention", | |
| "Title": "Implicit Morpheme-Aware Attention: Enhancing Linguistic Structure Learning in Character-Level Language Models", | |
| "Experiment": "1. Modify the GPT class to expand the character embedding dimension, allowing for richer representations. 2. Update the CausalSelfAttention class to include a 'morpheme-like' attention mechanism using character n-grams (n=3,4,5) as pseudo-morphemes. 3. Implement a function to generate n-gram representations for input sequences. 4. Modify the forward method to apply the morpheme-like attention before the standard self-attention. 5. Train models with and without Implicit Morpheme-Aware Attention on all three datasets. 6. Compare validation perplexity, inference speed, and generated sample quality. 7. Evaluate performance on a word completion task, predicting the last 3 characters given the first n characters of words. 8. Analyze attention patterns to understand how the model utilizes morpheme-like information across different layers.", | |
| "Interestingness": 9, | |
| "Feasibility": 9, | |
| "Novelty": 8, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "dynamic_memory_compression", | |
| "Title": "Dynamic Memory Compression: Enhancing Long-Range Coherence in Character-Level Language Models", | |
| "Experiment": "1. Implement a DynamicMemory class with a fixed number of memory slots (e.g., 5) and a single-layer update network. 2. Modify the GPT class to include the DynamicMemory module. 3. Update the forward method to compress and store information in the memory every 10 characters. 4. Modify the CausalSelfAttention class to concatenate the compressed memory to the input sequence before computing attention scores. 5. Train models with and without Dynamic Memory Compression on all three datasets. 6. Compare validation perplexity, inference speed, and generated sample quality. 7. Evaluate long-range coherence using a sliding window perplexity measure over varying sequence lengths (100, 500, 1000 characters). 8. Analyze the contents of the memory slots to understand what information the model learns to retain. 9. Measure the computational overhead introduced by the memory mechanism.", | |
| "Interestingness": 9, | |
| "Feasibility": 9, | |
| "Novelty": 9, | |
| "novel": false | |
| }, | |
| { | |
| "Name": "dual_tier_adaptive_dimensionality", | |
| "Title": "Dual-Tier Adaptive Dimensionality: Efficient Dynamic Representation in Character-Level Language Models", | |
| "Experiment": "1. Modify the GPT class to include a lightweight DimensionSelector that predicts whether to use base or enhanced dimension for each position. 2. Implement dual embedding layers: one for base dimension and one for enhanced dimension. 3. Update the Block class to handle both dimension sizes, using a simple switch mechanism rather than continuous projection. 4. Modify CausalSelfAttention and MLP classes to work with both dimension sizes. 5. Train models with and without Dual-Tier Adaptive Dimensionality on all three datasets. 6. Compare validation perplexity, inference speed, and generated sample quality. 7. Analyze patterns of base vs. enhanced dimension usage across different input types and sequence positions. 8. Evaluate computational efficiency by measuring effective FLOPs and memory usage. 9. Experiment with different ratios between base and enhanced dimensions to optimize the performance-efficiency trade-off.", | |
| "Interestingness": 9, | |
| "Feasibility": 8, | |
| "Novelty": 8, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "online_micro_adaptation", | |
| "Title": "Online Micro-Adaptation: Efficient Parameter Updates for Dynamic Context Adaptation in Character-Level Language Models", | |
| "Experiment": "1. Modify the GPT class to include a small set of adaptable parameters in the final layer. 2. Implement an efficient online update mechanism that computes gradients and updates these parameters every 50 characters during inference. 3. Add a regularization term to keep adapted parameters close to their original values. 4. Update the forward method to incorporate the online parameter updates. 5. Train baseline models on all three datasets. 6. Implement inference-time evaluation with micro-adaptation. 7. Compare perplexity and adaptation speed on sequences with deliberate style shifts. 8. Analyze parameter changes during processing of style-shifting sequences. 9. Evaluate performance on a custom task of predicting characters immediately following style transitions.", | |
| "Interestingness": 9, | |
| "Feasibility": 9, | |
| "Novelty": 9, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "context_adaptive_layer_norm", | |
| "Title": "Context-Adaptive Layer Normalization: Enhancing Style Consistency in Character-Level Language Models", | |
| "Experiment": "1. Modify the LayerNorm class to include a single linear layer for predicting scaling and shifting parameters. 2. Implement an efficient running average mechanism for hidden states as context. 3. Add a gating mechanism to balance context-adaptive and standard normalization. 4. Update the forward method of LayerNorm to use the running average context for parameter prediction. 5. Train models with Context-Adaptive Layer Normalization and compare with standard Layer Normalization on all three datasets. 6. Compare validation perplexity, inference speed, and generated sample quality, focusing on style consistency over long sequences (500+ characters). 7. Evaluate style consistency using a pre-trained style classifier on generated sequences of varying lengths. 8. Conduct an ablation study to analyze the impact of CALN at different layers of the model. 9. Measure the computational overhead introduced by CALN and compare it to the performance gains.", | |
| "Interestingness": 9, | |
| "Feasibility": 9, | |
| "Novelty": 8, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "contrastive_char_embedding", | |
| "Title": "Contrastive Character Embedding: Enhancing Representation Learning in Character-Level Language Models", | |
| "Experiment": "1. Implement a ContrastiveEmbedding class that wraps the existing embedding layer. 2. Create a function to generate positive and negative character pairs using a fixed-size context window (e.g., 5 characters). 3. Implement a contrastive loss function with a temperature parameter to control the strength of the contrastive signal. 4. Modify the training loop to compute and combine the contrastive loss with the standard language modeling loss, using a hyperparameter alpha to control their relative weights. 5. Train models with different alpha values on all three datasets. 6. Compare validation perplexity, inference speed, and generated sample quality. 7. Evaluate embeddings using a character analogy task (e.g., 'a' is to 'A' as 'b' is to 'B'). 8. Analyze learned embeddings through visualization techniques like t-SNE. 9. Conduct an ablation study on the impact of the context window size and temperature parameter.", | |
| "Interestingness": 9, | |
| "Feasibility": 9, | |
| "Novelty": 9, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "visual_character_embedding", | |
| "Title": "Visual Character Embedding: Enhancing Character-Level Language Models with Shape-Based Information", | |
| "Experiment": "1. Create a simple 3x3 binary matrix visual representation for the most common 100 characters. 2. Implement a VisualCharacterEmbedding class that combines the standard character embedding with a learned transformation of the visual representation. 3. Modify the GPT class to use the VisualCharacterEmbedding instead of the standard Embedding. 4. Implement a visual similarity loss term based on cosine similarity of visual embeddings. 5. Train models with and without Visual Character Embedding on all three datasets, incorporating the visual similarity loss. 6. Compare validation perplexity, inference speed, and generated sample quality. 7. Evaluate performance on custom tasks: typo correction, l33t speak translation, and emoji prediction. 8. Analyze how the model leverages visual similarity for characters not in the top 100. 9. Conduct an ablation study on the impact of visual similarity loss and the number of characters with visual embeddings.", | |
| "Interestingness": 9, | |
| "Feasibility": 9, | |
| "Novelty": 9, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "multi_scale_char_attention", | |
| "Title": "Multi-Scale Character Attention: Enhancing Contextual Understanding in Character-Level Language Models", | |
| "Experiment": "1. Modify the CausalSelfAttention class to implement three attention heads operating at different scales (1-gram, 3-gram, 5-gram). 2. Update the forward method to concatenate the outputs from different scales before feeding into the feed-forward layer. 3. Train models with and without Multi-Scale Character Attention on all three datasets. 4. Compare validation perplexity, inference speed, and generated sample quality, focusing on coherence over varying sequence lengths (50, 200, 500 characters). 5. Analyze the contribution of each scale to the model's predictions using attention visualization techniques. 6. Evaluate performance on custom tasks designed to test both character-level (e.g., next character prediction) and higher-level semantic understanding (e.g., word boundary detection). 7. Conduct ablation studies by training models with different combinations of scales to understand their individual impacts.", | |
| "Interestingness": 9, | |
| "Feasibility": 7, | |
| "Novelty": 8, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "frequency_adaptive_computation", | |
| "Title": "Frequency-Adaptive Computation: Dynamic Attention Allocation in Character-Level Language Models", | |
| "Experiment": "1. Implement a FrequencyAnalyzer class to maintain running counts of characters and bigrams. 2. Modify the GPT class to include the FrequencyAnalyzer and update it during forward passes. 3. Update the CausalSelfAttention class to dynamically adjust the number of attention heads based on character/bigram frequencies. 4. Train models with and without Frequency-Adaptive Computation on all three datasets. 5. Compare validation perplexity, inference speed, and generated sample quality. 6. Measure computational efficiency by tracking the average number of attention heads used per forward pass and comparing total FLOPs with the baseline model. 7. Evaluate performance on rare character sequences by creating a test set of low-frequency bigrams. 8. Test model adaptability using out-of-distribution samples (e.g., text with deliberately skewed character frequencies). 9. Conduct an ablation study on frequency thresholds for attention head adjustment.", | |
| "Interestingness": 9, | |
| "Feasibility": 9, | |
| "Novelty": 9, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "dual_aspect_char_embedding", | |
| "Title": "Dual-Aspect Character Embedding: Integrating Visual and Semantic Representations in Language Models", | |
| "Experiment": "1. Implement a DualAspectCharacterEmbedding class that combines separate embedding layers for visual and semantic character representations. 2. Create a simple visual similarity mapping using 5x5 binary matrices for character shapes. 3. Define a semantic mapping based on character categories (lowercase, uppercase, punctuation, digit, special). 4. Modify the GPT class to use the DualAspectCharacterEmbedding instead of the standard Embedding. 5. Implement a loss function that encourages consistency between visual and semantic embeddings. 6. Train baseline models and models with Dual-Aspect Character Embedding on all three datasets. 7. Compare validation perplexity, inference speed, and generated sample quality between baseline and dual-aspect models. 8. Evaluate performance on custom tasks: cross-case character prediction and simple typo correction. 9. Visualize learned embeddings to analyze how visual and semantic aspects are captured. 10. Conduct an ablation study on the impact of visual and semantic components on model performance.", | |
| "Interestingness": 8, | |
| "Feasibility": 9, | |
| "Novelty": 8, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "dynamic_depth_adaptation", | |
| "Title": "Dynamic Depth Adaptation: Efficient Processing with Discrete Layer Configuration Selection in Character-Level Language Models", | |
| "Experiment": "1. Modify the GPT class to support three predefined depth configurations (e.g., 4, 6, and 8 layers). 2. Implement a lightweight ConfigSelector module that predicts the depth configuration every 50 characters. 3. Update the forward method to use the selected configuration for each segment. 4. Implement a loss function that balances performance and computational efficiency. 5. Train models using curriculum learning, gradually introducing adaptive depth selection. 6. Compare validation perplexity, inference speed, and generated sample quality with baseline models. 7. Analyze the distribution of selected configurations for different input types and sequence positions. 8. Evaluate computational efficiency by measuring average FLOPs per inference pass. 9. Introduce an 'adaptation effectiveness' metric comparing performance to a model always using the highest depth configuration. 10. Conduct ablation studies on segment length and configuration options.", | |
| "Interestingness": 9, | |
| "Feasibility": 9, | |
| "Novelty": 9, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "progressive_character_unfolding", | |
| "Title": "Progressive Character Unfolding: Two-Tier Processing for Efficient Character-Level Language Models", | |
| "Experiment": "1. Modify the GPT class to implement a two-tier processing system (basic and detailed). 2. Create a LevelClassifier module (a small 2-layer MLP) that predicts the required processing level for each 5-character window. 3. Update the CausalSelfAttention class to use sparse attention patterns for basic-level processing. 4. Implement a custom loss function that balances performance and computational efficiency. 5. Train models with and without PCU on all three datasets. 6. Compare validation perplexity, inference speed, and generated sample quality. 7. Analyze the distribution of processing levels for different types of sequences and character positions. 8. Evaluate computational efficiency by measuring average FLOPs per inference pass. 9. Conduct a specific evaluation task: predicting the next character in both simple (common words) and complex (rare words, equations) sequences.", | |
| "Interestingness": 9, | |
| "Feasibility": 9, | |
| "Novelty": 9, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "fixed_interval_semantic_summaries", | |
| "Title": "Fixed-Interval Semantic Summaries: Enhancing Long-Range Coherence in Character-Level Language Models", | |
| "Experiment": "1. Implement a SemanticSummaryModule class that creates compressed representations of text at fixed intervals. 2. Modify the GPT class to include the SemanticSummaryModule and update it during forward passes. 3. Update the CausalSelfAttention class to incorporate semantic summaries into its attention computation. 4. Train models with and without Fixed-Interval Semantic Summaries on all three datasets. 5. Compare validation perplexity, inference speed, and generated sample quality against baseline models, focusing on long-range coherence (1000+ characters). 6. Evaluate performance on custom tasks: theme continuation and long-text summarization. 7. Analyze the impact of different summary intervals and compression methods on model performance. 8. Conduct an ablation study to quantify the contribution of semantic summaries to the model's performance.", | |
| "Interestingness": 9, | |
| "Feasibility": 9, | |
| "Novelty": 8, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "pattern_sensitive_attention", | |
| "Title": "Pattern-Sensitive Attention: Enhancing Linguistic Generalization in Character-Level Language Models", | |
| "Experiment": "1. Modify the CausalSelfAttention class to compute a 'pattern similarity score' between the current position and previous positions. 2. Implement the pattern similarity computation using a simple n-gram overlap metric (e.g., for n=3). 3. Update the attention computation to incorporate the pattern similarity scores as an additional bias term. 4. Train models with and without Pattern-Sensitive Attention on all three datasets. 5. Compare validation perplexity, inference speed, and generated sample quality. 6. Evaluate performance on custom tasks: handling misspellings, adapting to simple made-up languages, and cross-lingual character prediction. 7. Analyze how the pattern-sensitive attention affects the model's behavior on different types of sequences. 8. Conduct ablation studies on the impact of different n-gram sizes for pattern matching.", | |
| "Interestingness": 8, | |
| "Feasibility": 9, | |
| "Novelty": 7, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "multi_modal_char_representation", | |
| "Title": "Multi-Modal Character Representation: Enhancing Character-Level Language Models with Visual and Phonetic Features", | |
| "Experiment": "1. Implement a VisualEncoder class that creates a 5x5 binary matrix representation for the 100 most common characters. 2. Implement a PhoneticEncoder class that assigns characters to one of 10 basic phonetic categories. 3. Modify the GPT class to include a MultiModalCharacterEmbedding layer that combines visual, phonetic, and contextual embeddings. 4. Update the forward method to use the multi-modal embeddings. 5. Implement a loss function that includes a consistency term between modalities. 6. Train models with and without Multi-Modal Character Representation on all three datasets. 7. Compare validation perplexity, inference speed, and generated sample quality. 8. Evaluate performance on a character similarity task and an out-of-vocabulary character prediction task. 9. Visualize learned embeddings using t-SNE to analyze the contribution of different modalities. 10. Conduct an ablation study by training models with different combinations of modalities.", | |
| "Interestingness": 9, | |
| "Feasibility": 9, | |
| "Novelty": 9, | |
| "novel": true | |
| }, | |
| { | |
| "Name": "emergent_syntax_attention", | |
| "Title": "Emergent Syntax Attention: Unsupervised Discovery of Linguistic Structure in Character-Level Language Models", | |
| "Experiment": "1. Modify the CausalSelfAttention class to include a 'syntax score' computation based on local character patterns (e.g., using a small MLP). 2. Update the attention computation to incorporate these syntax scores as additional bias terms. 3. Implement a custom loss term that encourages diversity and consistency in syntax score predictions, using a combination of entropy and temporal consistency losses. 4. Train models with and without Emergent Syntax Attention on all three datasets. 5. Compare validation perplexity and generated sample quality, focusing on grammatical correctness. 6. Evaluate performance on two specific tasks: (a) predicting closing brackets/quotation marks, and (b) maintaining subject-verb agreement over long distances. 7. Visualize learned syntax patterns using attention heatmaps and t-SNE plots of syntax scores. 8. Analyze how syntax scores change for different types of character sequences, including across sentence boundaries and for various punctuation marks. 9. Conduct an ablation study on the impact of the syntax score computation and loss term components.", | |
| "Interestingness": 9, | |
| "Feasibility": 9, | |
| "Novelty": 9, | |
| "novel": true | |
| } | |
| ] |