Rowen：用于缓解大语言模型幻觉的自适应检索增强生成
Rowen: Adaptive Retrieval-Augmented Generation for Hallucination Mitigation in LLMs
AI总结：
本研究针对大语言模型的内部与外部幻觉问题，提出Rowen框架，通过基于一致性的幻觉检测模块自适应触发外部检索，在幻觉检测与缓解效果上均超越现有最优水平。
摘要：
幻觉是大语言模型（LLM）面临的一项重大挑战。LLM在生成事实性内容时对参数化知识的利用受限于其自身有限的知识储备，可能引发内部幻觉。引入外部信息虽有助于填补知识空白，但也会带来无关信息的风险，进而增加外部幻觉的发生概率。为平衡LLM内部参数化知识与外部信息的使用，本研究提出了Rowen——一种新型框架，通过定制化的自适应检索增强流程优化LLM，以解决幻觉输出问题。Rowen引入了基于一致性的幻觉检测模块，通过评估不同语言或模型生成的各类回复中的语义不一致性，判断模型对输入查询的不确定性。当检测到回复存在高不确定性时，Rowen会启动外部信息检索以修正模型输出。通过全面的实证实验，我们证明Rowen在检测和缓解LLM输出中的幻觉内容方面，均超越了当前最优水平。
英文摘要：
Hallucinations present a significant challenge for large language models (LLMs). The utilization of parametric knowledge in generating factual content is constrained by the limited knowledge of LLMs, potentially resulting in internal hallucinations. While incorporating external information can help fill knowledge gaps, it also introduces the risk of irrelevant information, thereby increasing the likelihood of external hallucinations. To balance the use of parametric knowledge within LLMs and external information, in this study, we present Rowen, a novel framework that enhances LLMs with an adaptive retrieval augmentation process tailored to address hallucinated outputs. Rowen introduces a consistency-based hallucination detection module, which assesses the model's uncertainty regarding the input query by evaluating the semantic inconsistencies in various responses generated across different languages or models. When high uncertainties in the responses are detected, Rowen activates the retrieval of external information to rectify the model outputs. Through comprehensive empirical experiments, we demonstrate that Rowen surpasses the current state-of-the-art in both detecting and mitigating hallucinated content within the outputs of LLMs.
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