Iter-AHMCL：通过迭代模型级对比学习缓解大语言模型的幻觉问题
Iter-AHMCL: Alleviate Hallucination for Large Language Model via Iterative Model-level Contrastive Learning
AI总结：
本文提出Iter-AHMCL方法，通过在有无幻觉数据上训练的正负模型对比修改预训练LLM表示层，在TruthfulQA上平均提升10.1分，有效缓解幻觉并保持模型通用能力。
摘要：
大语言模型（LLMs）的发展显著推进了商业和科学研究领域的各种人工智能应用，例如科学文献摘要、写作辅助和知识图谱构建。然而，一个重大挑战是LLM推理过程中存在极高的幻觉风险，这可能导致事实不准确、信息不一致和捏造内容等安全担忧。为了解决这个问题，必须开发有效的方法来减少幻觉，同时保持LLM的原始能力。本文引入了一种名为迭代模型级对比学习（Iter-AHMCL）的新方法来解决幻觉。该方法通过使用在有无幻觉数据上训练的对比“正”和“负”模型，修改预训练LLM的表示层。通过利用这两个模型之间的差异，我们创建了一条更直接的路径来消除幻觉，并且对比学习的迭代性质进一步增强了性能。在四个预训练基础LLM（LLaMA2、Alpaca、LLaMA3和Qwen）上使用专门设计的数据集进行微调的实验验证表明，我们的方法在TruthfulQA基准上平均提高了10.1分。综合实验证明了Iter-AHMCL在减少幻觉的同时保持LLM通用能力的有效性。
英文摘要：
The development of Large Language Models (LLMs) has significantly advanced various AI applications in commercial and scientific research fields, such as scientific literature summarization, writing assistance, and knowledge graph construction. However, a significant challenge is the high risk of hallucination during LLM inference, which can lead to security concerns like factual inaccuracies, inconsistent information, and fabricated content. To tackle this issue, it is essential to develop effective methods for reducing hallucination while maintaining the original capabilities of the LLM. This paper introduces a novel approach called Iterative Model-level Contrastive Learning (Iter-AHMCL) to address hallucination. This method modifies the representation layers of pre-trained LLMs by using contrastive `positive' and `negative' models, trained on data with and without hallucinations. By leveraging the differences between these two models, we create a more straightforward pathway to eliminate hallucinations, and the iterative nature of contrastive learning further enhances performance. Experimental validation on four pre-trained foundation LLMs (LLaMA2, Alpaca, LLaMA3, and Qwen) finetuning with a specially designed dataset shows that our approach achieves an average improvement of 10.1 points on the TruthfulQA benchmark. Comprehensive experiments demonstrate the effectiveness of Iter-AHMCL in reducing hallucination while maintaining the general capabilities of LLMs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
