Collections
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Collections including paper arxiv:2406.00888
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Understanding the performance gap between online and offline alignment algorithms
Paper • 2405.08448 • Published • 14 -
Self-Exploring Language Models: Active Preference Elicitation for Online Alignment
Paper • 2405.19332 • Published • 15 -
Offline Regularised Reinforcement Learning for Large Language Models Alignment
Paper • 2405.19107 • Published • 13 -
Show, Don't Tell: Aligning Language Models with Demonstrated Feedback
Paper • 2406.00888 • Published • 30
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Many-Shot In-Context Learning in Multimodal Foundation Models
Paper • 2405.09798 • Published • 26 -
From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting
Paper • 2309.04269 • Published • 32 -
Show, Don't Tell: Aligning Language Models with Demonstrated Feedback
Paper • 2406.00888 • Published • 30 -
To Believe or Not to Believe Your LLM
Paper • 2406.02543 • Published • 31
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Iterative Reasoning Preference Optimization
Paper • 2404.19733 • Published • 46 -
Better & Faster Large Language Models via Multi-token Prediction
Paper • 2404.19737 • Published • 73 -
ORPO: Monolithic Preference Optimization without Reference Model
Paper • 2403.07691 • Published • 59 -
KAN: Kolmogorov-Arnold Networks
Paper • 2404.19756 • Published • 108
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OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement
Paper • 2402.14658 • Published • 82 -
KAN: Kolmogorov-Arnold Networks
Paper • 2404.19756 • Published • 108 -
Understanding the performance gap between online and offline alignment algorithms
Paper • 2405.08448 • Published • 14 -
NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models
Paper • 2405.17428 • Published • 16
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PERL: Parameter Efficient Reinforcement Learning from Human Feedback
Paper • 2403.10704 • Published • 56 -
HyperLLaVA: Dynamic Visual and Language Expert Tuning for Multimodal Large Language Models
Paper • 2403.13447 • Published • 17 -
Self-Discover: Large Language Models Self-Compose Reasoning Structures
Paper • 2402.03620 • Published • 109 -
RAFT: Adapting Language Model to Domain Specific RAG
Paper • 2403.10131 • Published • 66
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A Critical Evaluation of AI Feedback for Aligning Large Language Models
Paper • 2402.12366 • Published • 3 -
PERL: Parameter Efficient Reinforcement Learning from Human Feedback
Paper • 2403.10704 • Published • 56 -
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
Paper • 2403.03507 • Published • 182 -
Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation
Paper • 2401.08417 • Published • 30
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EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 25 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 12 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 36 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 19
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How to Train Data-Efficient LLMs
Paper • 2402.09668 • Published • 38 -
LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement
Paper • 2403.15042 • Published • 24 -
MAGID: An Automated Pipeline for Generating Synthetic Multi-modal Datasets
Paper • 2403.03194 • Published • 12 -
Orca-Math: Unlocking the potential of SLMs in Grade School Math
Paper • 2402.14830 • Published • 24
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Suppressing Pink Elephants with Direct Principle Feedback
Paper • 2402.07896 • Published • 9 -
Policy Improvement using Language Feedback Models
Paper • 2402.07876 • Published • 5 -
Direct Language Model Alignment from Online AI Feedback
Paper • 2402.04792 • Published • 27 -
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
Paper • 2401.01335 • Published • 64