|
--- |
|
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 |
