README.md

---
license: mit
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: microsoft/Phi-3-mini-4k-instruct
model-index:
- name: outputs
  results: []
---

## 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       0.85      0.71      0.77       100
           1       0.75      0.87      0.81       100

    accuracy                           0.79       200
   macro avg       0.80      0.79      0.79       200
weighted avg       0.80      0.79      0.79       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(query='Based on the follwoing
          <context>Walrus are the largest mammal</context>
          answer the question
          <query> What is the best PC?</query>',
          response='The best PC is the 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[0]['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      | 0.75      | 0.87   | 0.81   |
| 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   |

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.

We will continue to improve and fine-tune our merged model to achieve even better performance across various benchmarks and tasks.

Citations:
 Scores from arize/phoenix

### Training Data

@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}
}
 
### Framework versions

- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 150

### Framework versions

- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1