1 citations · 3 across the 16 of their papers we have counts for
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IterCOMP: Reasoning-aware Adaptive Prompt Compression for Multi-hop Question Answering
JungMin Yun, YoungBin Kim
Multi-hop question answering requires complex reasoning across multiple evidence segments, which often overwhelms retrieval-augmented generation systems with lengthy and noisy cont…
CRiT-QA: Evaluating Multi-hop Reasoning with Counterfactual Chains and Distractor Traps
JungMin Yun, JuneHyoung Kwon, YoungBin Kim
Evaluating the multi-hop reasoning capabilities of large language models remains a significant challenge. Although current models achieve strong results on existing multi-hop quest…
Before Forgetting, Learn to Remember: Revisiting Foundational Learning Failures in LVLM Unlearning Benchmarks
JuneHyoung Kwon, MiHyeon Kim, Eunju Lee +3
While Large Vision-Language Models (LVLMs) offer powerful capabilities, they pose privacy risks by unintentionally memorizing sensitive personal information. Current unlearning ben…
Aligning with Your Own Voice: Self-Corrected Preference Learning for Hallucination Mitigation in LVLMs
Byeonggeuk Lim, JungMin Yun, Junehyoung Kwon +2
Large Vision-Language Models (LVLMs) frequently suffer from hallucinations. Existing preference learning-based approaches largely rely on proprietary models to construct preference…
Query, Decompose, Compress: Structured Query Expansion for Efficient Multi-Hop Retrieval
JungMin Yun, YoungBin Kim
Large Language Models (LLMs) have been increasingly employed for query expansion. However, their generative nature often undermines performance on complex multi-hop retrieval tasks…
Position on LLM-Assisted Peer Review: Addressing Reviewer Gap through Mentoring and Feedback
JungMin Yun, JuneHyoung Kwon, MiHyeon Kim +1
The rapid expansion of AI research has intensified the Reviewer Gap, threatening the peer-review sustainability and perpetuating a cycle of low-quality evaluations. This position p…
SUMMPILOT: Bridging Efficiency and Customization for Interactive Summarization System
JungMin Yun, Juhwan Choi, Kyohoon Jin +3
This paper incorporates the efficiency of automatic summarization and addresses the challenge of generating personalized summaries tailored to individual users' interests and requi…