README.md

---
license: gpl-3.0
---
# TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space

> [Shaolei Zhang](https://zhangshaolei1998.github.io/), Tian Yu, [Yang Feng](https://people.ucas.edu.cn/~yangfeng?language=en)*

**TruthX** is an inference-time method to elicit the truthfulness of LLMs by editing their internal representations in truthful space, thereby mitigating the hallucinations of LLMs. On the [TruthfulQA benchmark](https://paperswithcode.com/sota/question-answering-on-truthfulqa), TruthX yields an average **enhancement of 20% in truthfulness** across 13 advanced LLMs.

<div  align="center">   
  <img src="./truthx_results.png" alt="img" width="100%" />
</div>
<p align="center">
  TruthfulQA MC1 accuracy of TruthX across 13 advanced LLMs
</p>

This repo provide **Llama-2-7B-Chat-TruthX**, a Llama-2-7B-Chat model with baked-in TruthX model. You can directly download this baked-in model and use it like standard Llama, no additional operations are required.

## Quick Starts
Inference with Llama-2-7B-Chat-TruthX:

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

llama2chat_with_truthx = "ICTNLP/Llama-2-7b-chat-TruthX"
tokenizer = AutoTokenizer.from_pretrained(llama2chat_with_truthx, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(llama2chat_with_truthx, trust_remote_code=True,torch_dtype=torch.float16).cuda()

question = "What are the benefits of eating an apple a day?"
encoded_inputs = tokenizer(question, return_tensors="pt")["input_ids"]
outputs = model.generate(encoded_inputs.cuda())[0, encoded_inputs.shape[-1] :]
outputs_text = tokenizer.decode(outputs, skip_special_tokens=True).strip()
print(outputs_text)
```

Please refer to [GitHub repo](https://github.com/ictnlp/TruthX) and our paper for more details.