Papers by Yeonjun In
SIMPLOT: Enhancing Chart Question Answering by Distilling Essentials (2025.findings-naacl)
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| Challenge: | Recent advances in vision-language models have accelerated research into models capable of advanced reasoning based on images. |
| Approach: | They propose a method that leverages vision-language models to convert charts into table format . they use Large Language Model (LLM) for reasoning to extract only the essential information . |
| Outcome: | The proposed method extracts only the elements necessary for chart reasoning without the need for additional annotations or datasets. |
Is Safety Standard Same for Everyone? User-Specific Safety Evaluation of Large Language Models (2025.findings-emnlp)
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Yeonjun In, Wonjoong Kim, Kanghoon Yoon, Sungchul Kim, Mehrab Tanjim, Sangwu Park, Kibum Kim, Chanyoung Park
| Challenge: | Extensive benchmarks evaluate LLM safety relying heavily on general standards . no benchmark datasets exist to evaluate the user-specific safety of LLMs . |
| Approach: | a new benchmark is designed to assess user-specific aspect of LLM safety . authors propose a simple remedy based on chain-of-thought to improve user-specified safety. |
| Outcome: | a new benchmark assesses the user-specific aspect of LLM safety . the proposed solution improves user-specified safety by chain-of-thought . |
Reasoning Structure Matters for Safety Alignment of Reasoning Models (2026.acl-long)
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| Challenge: | Large reasoning models (LRMs) achieve strong performance on complex reasoning tasks but often generate harmful responses to malicious user queries. |
| Approach: | They propose a method that alters the reasoning structure of large reasoning models to achieve effective safety alignment by supervised fine-tuning. |
| Outcome: | The proposed method is practical and generalizable, requiring no complex training or reward design. |
Disambiguation in Conversational Question Answering in the Era of LLMs and Agents: A Survey (2025.emnlp-main)
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Mehrab Tanjim, Yeonjun In, Xiang Chen, Victor Bursztyn, Ryan A. Rossi, Sungchul Kim, Guang-Jie Ren, Vaishnavi Muppala, Shun Jiang, Yongsung Kim, Chanyoung Park
| Challenge: | Existing literature on ambiguity and disambiguation with Large Language Models (LLMs) ambiguities are a fundamental challenge in human-AI interactions due to complexity and flexibility of human language. |
| Approach: | They propose to define key terms and concepts and categorize various disambiguation approaches enabled by LLMs and provide a comparative analysis of their advantages and disadvantages. |
| Outcome: | The proposed frameworks are compared against different disambiguation approaches and highlight their relevance for future research. |
Diversify-verify-adapt: Efficient and Robust Retrieval-Augmented Ambiguous Question Answering (2025.naacl-long)
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| Challenge: | Existing approaches to address ambiguous questions are limited in their efficiency and performance. |
| Approach: | They propose a retrieval augmented generation framework that diversifies and verifies the retrieved passages to encompass diverse interpretations and adapts the most suitable approach tailored to their quality. |
| Outcome: | The proposed approach improves accuracy and robustness by handling low quality retrieval issue in ambiguous questions while enhancing efficiency. |