16 citations · 31 across the 11 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
KU-DMIS-MSRA at RadSum23: Pre-trained Vision-Language Model for Radiology Report Summarization
Gangwoo Kim, Hajung Kim, Lei Ji +7
In this paper, we introduce CheXOFA, a new pre-trained vision-language model (VLM) for the chest X-ray domain. Our model is initially pre-trained on various multimodal datasets wit…
Generating Information-Seeking Conversations from Unlabeled Documents
Gangwoo Kim, Sungdong Kim, Kang Min Yoo +1
In this paper, we introduce a novel framework, SIMSEEK, (Simulating information-Seeking conversation from unlabeled documents), and compare its two variants. In our baseline SIMSEE…
Saving Dense Retriever from Shortcut Dependency in Conversational Search
Sungdong Kim, Gangwoo Kim
Conversational search (CS) needs a holistic understanding of conversational inputs to retrieve relevant passages. In this paper, we demonstrate the existence of a retrieval shortcu…
Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering
Gangwoo Kim, Hyunjae Kim, Jungsoo Park +1
One of the main challenges in conversational question answering (CQA) is to resolve the conversational dependency, such as anaphora and ellipsis. However, existing approaches do no…
Transferability of Natural Language Inference to Biomedical Question Answering
Minbyul Jeong, Mujeen Sung, Gangwoo Kim +4
Biomedical question answering (QA) is a challenging task due to the scarcity of data and the requirement of domain expertise. Pre-trained language models have been used to address…
Look at the First Sentence: Position Bias in Question Answering
Miyoung Ko, Jinhyuk Lee, Hyunjae Kim +2
Many extractive question answering models are trained to predict start and end positions of answers. The choice of predicting answers as positions is mainly due to its simplicity a…