DecoPrompt：当大语言模型遇到错误前提时，解码提示词可减少幻觉
DecoPrompt : Decoding Prompts Reduces Hallucinations when Large Language Models Meet False Premises
AI总结：
针对大语言模型在错误前提下的幻觉问题，提出DecoPrompt提示算法，通过解码错误前提提示词而非直接生成来减少幻觉，并在两个数据集上验证了其有效性和跨模型可迁移性。
摘要：
虽然大语言模型（LLMs）展现出日益强大的能力，但它们也引发了对其产生偏离事实正确陈述的幻觉输出的研究。本文聚焦于错误前提这一重要场景，即尽管模型具备准确回答原始问题所需的事实知识，却会被不匹配的陈述所干扰。受观察启发——错误前提提示词的熵与其引发幻觉生成的可能性密切相关——我们提出了一种新的提示算法，名为DecoPrompt，以缓解幻觉。DecoPrompt利用LLMs“解码”错误前提提示词，而不真正引发LLMs的幻觉输出。我们在两个数据集上进行实验，证明DecoPrompt能有效减少不同LLMs输出的幻觉。此外，DecoPrompt展现出跨模型的可迁移性，这有助于其应用于如大规模LLMs或无法获取模型logits等场景。
英文摘要：
While large language models (LLMs) have demonstrated increasing power, they have also called upon studies on their hallucinated outputs that deviate from factually correct statements. In this paper, we focus on one important scenario of false premises, where LLMs are distracted by misaligned claims although the model possesses the required factual knowledge to answer original questions accurately. Inspired by the observation that entropy of the false-premise prompt is closely related to its likelihood to elicit hallucination generation, we propose a new prompting algorithm, named DecoPrompt, to mitigate hallucination. DecoPrompt leverages LLMs to "decode" the false-premise prompts without really eliciting hallucination output from LLMs. We perform experiments on two datasets, demonstrating that DecoPrompt can reduce hallucinations effectively on outputs from different LLMs. Moreover, DecoPrompt exhibits cross-model transferability, which facilitates its applications to scenarios such as LLMs of large sizes or unavailable model logits.
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。
