Papers by Gangwoo Kim
Generating Information-Seeking Conversations from Unlabeled Documents (2022.emnlp-main)
Copied to clipboard
| Challenge: | a novel framework for conversational question answering from unlabeled documents has been proposed . a large-scale dataset of synthetic conversations is available for use in real-world applications . |
| Approach: | They propose a framework for conversational question answering from unlabeled documents . they propose 'SimSeek' framework that simulates conversation from unlabelled documents based on two scenarios . |
| Outcome: | The proposed framework achieves state-of-the-art performance on a recent CQA benchmark, QuAC. |
Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering (2021.acl-long)
Copied to clipboard
| Challenge: | Existing approaches do not explicitly train QA models on how to resolve conversational dependency, and thus these models are limited in understanding human dialogues. |
| Approach: | They propose a framework that generates self-contained questions that can be understood without the conversation history and then trains a QA model with the pairs of original and self-constructed questions using a consistency-based regularizer. |
| Outcome: | The proposed framework improves the models’ performance by up to 1.2 F1 on QuAC, and 5.2 F1 for CANARD, while addressing the limitations of the existing approaches. |
Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | In open-domain question answering, users often ask ambiguous questions (AQs) . one approach is to identify all possible interpretations of the AQ and generate a long-form answer addressing them all. |
| Approach: | They propose a framework that generates a long-form answer addressing all possible interpretations of an ambiguous question. |
| Outcome: | The proposed framework outperforms baselines on ASQA in a few-shot setup across metrics while surpassing fully-supervised baselines trained on the whole training set in terms of Disambig-F1 and Disambigo-ROUGE. |
Saving Dense Retriever from Shortcut Dependency in Conversational Search (2022.emnlp-main)
Copied to clipboard
| Challenge: | a retrieval shortcut in conversational search (CS) relies on partial history to retrieve relevant passages . naively trained dense retrievers heavily exploit the shortcut and perform poorly when asked to answer history-independent questions. |
| Approach: | They propose to exploit a retrieval shortcut in conversational search (CS) that allows models to only use partial history to retrieve relevant passages while disregarding the latest question. |
| Outcome: | The proposed model outperforms the previous state-of-the-art model by 11.0 on QReCC. |
Assessing LLM Reasoning Steps via Principal Knowledge Grounding (2025.findings-emnlp)
Copied to clipboard
Hyeon Hwang, Yewon Cho, Chanwoong Yoon, Yein Park, Minju Song, Kyungjae Lee, Gangwoo Kim, Jaewoo Kang
| Challenge: | Step-by-step reasoning has become a standard approach for large language models to tackle complex tasks. |
| Approach: | They propose a framework that assesses the knowledge grounding of intermediate reasoning by using a large-scale repository of atomic knowledge essential for reasoning. |
| Outcome: | The evaluation suite identifies missing or misapplied knowledge elements and provides crucial insights for uncovering fundamental reasoning deficiencies in LLMs. |
Learning to Explore and Select for Coverage-Conditioned Retrieval-Augmented Generation (2025.findings-naacl)
Copied to clipboard
| Challenge: | Recent advances of large language models (LLMs) have enabled them to provide long and detailed responses by leveraging their parametric knowledge. |
| Approach: | They introduce a dataset of 10K hierarchical sets of information-seeking subqueries that define structured boundaries for outline creation and evaluation in C2 scenarios. |
| Outcome: | The proposed model generates higher-quality outlines that better fulfill diverse user needs when trained with alignment techniques like DPO. |
Ask Optimal Questions: Aligning Large Language Models with Retriever’s Preference in Conversation (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods to perform conversational search are sub-optimal due to the limited ability to incorporate signals from the retrieval results. |
| Approach: | They propose to optimize a language model for reformulating search queries in line with retrievers’ preferences by combining a large-scale dataset with Retrievers’ Feedback. |
| Outcome: | The proposed framework outperforms existing methods on two benchmarks and surpasses the state-of-the-art methods. |
Look at the First Sentence: Position Bias in Question Answering (2020.emnlp-main)
Copied to clipboard
| Challenge: | Extractive question answering models are trained to predict start and end positions of answers . recent QA models outperform humans in some datasets due to their simplicity and effectiveness. |
| Approach: | They propose to use prior distribution of answer positions as a bias model to reduce position bias. |
| Outcome: | The proposed model outperforms BERT from 37.48% to 81.64% when trained on a biased SQUAD dataset. |