activity
20232026
most citedTree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models

4 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2026

MolDeTox: Evaluating Language Model's Stepwise Fragment Editing for Molecular Detoxification

Jueon Park, Wonjune Jang, Jiwoo Lee +2

Large Language Models (LLMs) and Vision Language Models (VLMs) have recently shown promising capabilities in various scientific domain. In particular, these advances have opened ne…

cs.CL2025

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…

cs.DB2024

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…

cs.IR2024★ 3 cited

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…

cs.CL2023★ 4 cited

Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models

Gangwoo Kim, Sungdong Kim, Byeongguk Jeon +2

Questions in open-domain question answering are often ambiguous, allowing multiple interpretations. One approach to handling them is to identify all possible interpretations of the…