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