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license: gpl-3.0 |
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# TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space |
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> [Shaolei Zhang](https://zhangshaolei1998.github.io/), Tian Yu, [Yang Feng](https://people.ucas.edu.cn/~yangfeng?language=en)* |
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**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. |
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<div align="center"> |
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<img src="./truthx_results.png" alt="img" width="100%" /> |
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</div> |
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<p align="center"> |
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TruthfulQA MC1 accuracy of TruthX across 13 advanced LLMs |
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</p> |
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This repo provides **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. |
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## Quick Starts |
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Inference with Llama-2-7B-Chat-TruthX: |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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llama2chat_with_truthx = "ICTNLP/Llama-2-7b-chat-TruthX" |
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tokenizer = AutoTokenizer.from_pretrained(llama2chat_with_truthx, trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained(llama2chat_with_truthx, trust_remote_code=True,torch_dtype=torch.float16).cuda() |
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question = "What are the benefits of eating an apple a day?" |
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encoded_inputs = tokenizer(question, return_tensors="pt")["input_ids"] |
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outputs = model.generate(encoded_inputs.cuda())[0, encoded_inputs.shape[-1] :] |
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outputs_text = tokenizer.decode(outputs, skip_special_tokens=True).strip() |
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print(outputs_text) |
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``` |
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Please refer to [GitHub repo](https://github.com/ictnlp/TruthX) and our paper for more details. |
