README.md

---
base_model: microsoft/Phi-3.5-mini-instruct
library_name: peft
license: mit
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: outputs
  results: []
---

[<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)

## 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