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--- |
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base_model: microsoft/Phi-3.5-mini-instruct |
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library_name: peft |
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license: mit |
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tags: |
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- trl |
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- sft |
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- generated_from_trainer |
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model-index: |
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- name: outputs |
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results: [] |
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--- |
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/josh-longenecker1-groundedai/phi3.5-hallucination/runs/re0kg3gs) |
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## Merged Model Performance |
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This repository contains our hallucination evaluation PEFT adapter model. |
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### Hallucination Detection Metrics |
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Our merged model achieves the following performance on a binary classification task for detecting hallucinations in language model outputs: |
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``` |
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precision recall f1-score support |
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0 0.77 0.91 0.83 100 |
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1 0.89 0.73 0.80 100 |
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accuracy 0.82 200 |
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macro avg 0.83 0.82 0.82 200 |
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weighted avg 0.83 0.82 0.82 200 |
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``` |
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### Model Usage |
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For best results, we recommend starting with the following prompting strategy (and encourage tweaks as you see fit): |
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```python |
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def format_input(reference, query, response): |
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prompt = f"""Your job is to evaluate whether a machine learning model has hallucinated or not. |
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A hallucination occurs when the response is coherent but factually incorrect or nonsensical |
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outputs that are not grounded in the provided context. |
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You are given the following information: |
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####INFO#### |
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[Knowledge]: {reference} |
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[User Input]: {query} |
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[Model Response]: {response} |
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####END INFO#### |
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Based on the information provided is the model output a hallucination? Respond with only "yes" or "no" |
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""" |
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return input |
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text = format_input(reference="The apple mac has the best hardware", |
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query="What computer has the best software?", |
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response="Apple mac") |
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messages = [ |
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{"role": "user", "content": text} |
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] |
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pipe = pipeline( |
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"text-generation", |
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model=base_model, |
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model_kwargs={"attn_implementation": attn_implementation, "torch_dtype": torch.float16}, |
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tokenizer=tokenizer, |
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) |
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generation_args = { |
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"max_new_tokens": 2, |
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"return_full_text": False, |
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"temperature": 0.01, |
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"do_sample": True, |
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} |
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output = pipe(messages, **generation_args) |
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print(f'Hallucination: {output['generated_text'].strip().lower()}') |
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# Hallucination: yes |
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``` |
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### Comparison with Other Models |
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We compared our merged model's performance on the hallucination detection benchmark against several other state-of-the-art language models: |
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| Model | Precision | Recall | F1 | |
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|---------------------- |----------:|-------:|-------:| |
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| Our Merged Model | 0.77 | 0.91 | 0.83 | |
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| GPT-4 | 0.93 | 0.72 | 0.82 | |
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| GPT-4 Turbo | 0.97 | 0.70 | 0.81 | |
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| Gemini Pro | 0.89 | 0.53 | 0.67 | |
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| GPT-3.5 | 0.89 | 0.65 | 0.75 | |
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| GPT-3.5-turbo-instruct| 0.89 | 0.80 | 0.84 | |
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| Palm 2 (Text Bison) | 1.00 | 0.44 | 0.61 | |
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| Claude V2 | 0.80 | 0.95 | 0.87 | |
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Scores from arize/phoenix |
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As shown in the table, our merged model achieves competitive performance, with an F1 score of 0.83, matching or outperforming several state-of-the-art language models on this hallucination detection task. |
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## Model description |
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This model is a fine-tuned version of the Phi-3.5-mini-instruct model, specifically adapted for hallucination detection. It has been trained on the HaluEval dataset to identify when language model outputs contain hallucinations - responses that are coherent but factually incorrect or not grounded in the provided context. |
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## Intended uses & limitations |
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This model is intended for use in evaluating the outputs of language models to detect potential hallucinations. It can be integrated into pipelines for content validation, fact-checking, or as a component in larger systems aimed at improving the reliability of AI-generated content. |
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Limitations: |
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- The model's performance may vary depending on the domain and complexity of the input. |
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- It may not catch all types of hallucinations, especially those that are subtle or require extensive domain knowledge. |
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- The model should be used as part of a broader strategy for ensuring AI output quality, not as a sole arbiter of truth. |
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## Training and evaluation data |
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This model was trained using the HaluEval dataset: |
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@misc{HaluEval, |
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author = {Junyi Li and Xiaoxue Cheng and Wayne Xin Zhao and Jian-Yun Nie and Ji-Rong Wen }, |
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title = {HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models}, |
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year = {2023}, |
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journal={arXiv preprint arXiv:2305.11747}, |
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url={https://arxiv.org/abs/2305.11747} |
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} |
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The HaluEval dataset is specifically designed for evaluating hallucinations in large language models, making it an ideal choice for training our hallucination detection model. |
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In order to test the model's performance for yourself, feel free to use the test set at grounded-ai/hallucination_test. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 4 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 20 |
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- training_steps: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 2.2594 | 0.5263 | 5 | 2.2572 | |
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| 1.6785 | 1.0526 | 10 | 1.8170 | |
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| 1.6015 | 1.5789 | 15 | 1.4296 | |
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| 1.0556 | 2.1053 | 20 | 1.1199 | |
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| 0.9412 | 2.6316 | 25 | 1.0660 | |
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| 0.8872 | 3.1579 | 30 | 1.0523 | |
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| 0.9157 | 3.6842 | 35 | 1.0713 | |
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| 0.7735 | 4.2105 | 40 | 1.0983 | |
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| 0.6182 | 4.7368 | 45 | 1.0816 | |
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| 0.734 | 5.2632 | 50 | 1.1017 | |
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| 0.4736 | 5.7895 | 55 | 1.2109 | |
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| 0.3138 | 6.3158 | 60 | 1.2195 | |
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| 0.5315 | 6.8421 | 65 | 1.3147 | |
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### Framework versions |
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- PEFT 0.12.0 |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
