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--- |
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library_name: transformers |
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license: mit |
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tags: [] |
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--- |
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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 0.85 0.71 0.77 100 |
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1 0.75 0.87 0.81 100 |
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accuracy 0.79 200 |
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macro avg 0.80 0.79 0.79 200 |
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weighted avg 0.80 0.79 0.79 200 |
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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(query='Based on the follwoing |
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<context>Walrus are the largest mammal</context> |
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answer the question |
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<query> What is the best PC?</query>', |
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response='The best PC is the 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[0]['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 | 0.75 | 0.87 | 0.81 | |
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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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As shown in the table, our merged model achieves one of the highest F1 scores of 0.81, outperforming several other state-of-the-art language models on this hallucination detection task. |
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We will continue to improve and fine-tune our merged model to achieve even better performance across various benchmarks and tasks. |
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Citations: |
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Scores from arize/phoenix |
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### Training Data |
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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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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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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: 4 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 10 |
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- training_steps: 150 |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
