---
library_name: transformers
tags: []
license: mit
---
## 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
Walrus are the largest mammal
answer the question
What is the best PC?',
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