SLPL SHROOM 在 SemEval2024 Task 06：关于模型幻觉检测能力的综合研究
SLPL SHROOM at SemEval2024 Task 06: A comprehensive study on models ability to detect hallucination
AI总结：
本文面向 SemEval-2024 Task 6 的三类生成任务，评估语义相似度与语言模型互判集成两种幻觉检测方法，发现前者效果中等、后者揭示复杂性但不及预期。
摘要：
语言模型，尤其是生成式模型，容易产生幻觉，即生成与事实知识或源文本相矛盾的输出。本研究探讨了在 SemEval-2024 Task 6 的三个任务中检测幻觉的方法：机器翻译、定义建模和释义生成。我们评估了两种方法：生成文本与事实参考之间的语义相似度，以及由语言模型组成的集成模型，让它们相互判断彼此的输出。结果表明，语义相似度在试验数据上取得了中等的准确率和相关性分数，而集成方法虽揭示了幻觉检测的复杂性，却未达到预期。本工作强调了幻觉检测的挑战，并指出有必要在这一关键领域开展进一步研究。
英文摘要：
Language models, particularly generative models, are susceptible to hallucinations, generating outputs that contradict factual knowledge or the source text. This study explores methods for detecting hallucinations in three SemEval-2024 Task 6 tasks: Machine Translation, Definition Modeling, and Paraphrase Generation. We evaluate two methods: semantic similarity between the generated text and factual references, and an ensemble of language models that judge each other's outputs. Our results show that semantic similarity achieves moderate accuracy and correlation scores in trial data, while the ensemble method offers insights into the complexities of hallucination detection but falls short of expectations. This work highlights the challenges of hallucination detection and underscores the need for further research in this critical area.
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。
