IRLab@iKAT24：面向对话式搜索的基于多维度LLM查询生成的学习型稀疏检索
IRLab@iKAT24: Learned Sparse Retrieval with Multi-aspect LLM Query Generation for Conversational Search
AI总结：
本文研究对话式搜索中的查询重写，提出基于MQ4CS框架的多维度LLM查询生成方法，并结合SPLADE学习型稀疏检索与交叉编码器重排序，在iKAT 2024任务中验证了该方法能有效提升检索性能并超越人工重写效果。
摘要：
交互式知识助手赛道（iKAT）2024致力于推进对话式助手的发展，使其能够根据个性化用户知识调整交互和响应。该赛道将个人文本知识库（PTKB）与对话式AI任务（如段落排序和响应生成）相结合。查询重写是解决对话上下文问题的有效方法，我们探索使用大语言模型（LLMs）作为查询重写器。具体而言，我们提交的运行系统探索了使用MQ4CS框架进行多维度查询生成，并通过SPLADE架构进一步增强了学习型稀疏检索，同时结合了鲁棒的交叉编码器模型。我们还提出了一种替代先前交错策略的方法，在重排序阶段聚合多个维度。我们的研究结果表明，当与先进的检索和重排序模型集成时，多维度查询生成能够有效提升性能。我们的结果还为对话式搜索中更好的个性化指明了方向，依靠LLMs将个性化整合到查询重写中，并超越了人工重写的性能。
英文摘要：
The Interactive Knowledge Assistant Track (iKAT) 2024 focuses on advancing conversational assistants, able to adapt their interaction and responses from personalized user knowledge. The track incorporates a Personal Textual Knowledge Base (PTKB) alongside Conversational AI tasks, such as passage ranking and response generation. Query Rewrite being an effective approach for resolving conversational context, we explore Large Language Models (LLMs), as query rewriters. Specifically, our submitted runs explore multi-aspect query generation using the MQ4CS framework, which we further enhance with Learned Sparse Retrieval via the SPLADE architecture, coupled with robust cross-encoder models. We also propose an alternative to the previous interleaving strategy, aggregating multiple aspects during the reranking phase. Our findings indicate that multi-aspect query generation is effective in enhancing performance when integrated with advanced retrieval and reranking models. Our results also lead the way for better personalization in Conversational Search, relying on LLMs to integrate personalization within query rewrite, and outperforming human rewrite performance.
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。
