4 papers
Assessing LLM Reasoning Steps via Principal Knowledge Grounding
Hyeon Hwang, Yewon Cho, Chanwoong Yoon +5
Step-by-step reasoning has become a standard approach for large language models (LLMs) to tackle complex tasks. While this paradigm has proven effective, it raises a fundamental qu…
Ask Optimal Questions: Aligning Large Language Models with Retriever's Preference in Conversation
Chanwoong Yoon, Gangwoo Kim, Byeongguk Jeon +3
Conversational search, unlike single-turn retrieval tasks, requires understanding the current question within a dialogue context. The common approach of rewrite-then-retrieve aims…
Learning to Explore and Select for Coverage-Conditioned Retrieval-Augmented Generation
Takyoung Kim, Kyungjae Lee, Young Rok Jang +4
Interactions with large language models (LLMs) often yield long and detailed responses, leveraging both parametric knowledge and retrieval-augmented generation (RAG). While these r…
KU-DMIS at EHRSQL 2024:Generating SQL query via question templatization in EHR
Hajung Kim, Chanhwi Kim, Hoonick Lee +5
Transforming natural language questions into SQL queries is crucial for precise data retrieval from electronic health record (EHR) databases. A significant challenge in this proces…