Papers by Yein Park
Rationale-Guided Retrieval Augmented Generation for Medical Question Answering (2025.naacl-long)
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Jiwoong Sohn, Yein Park, Chanwoong Yoon, Sihyeon Park, Hyeon Hwang, Mujeen Sung, Hyunjae Kim, Jaewoo Kang
| Challenge: | Large language models (LLMs) struggle with hallucinations and outdated knowledge. |
| Approach: | They propose a retrieval-augmented generation framework for enhancing the reliability of RAG in biomedical contexts. |
| Outcome: | The proposed framework outperforms the previous best medical RAG model by up to 5.6% across three medical question-answering benchmarks. |
ToxReason: A Benchmark for Mechanistic Chemical Toxicity Reasoning via Adverse Outcome Pathway (2026.findings-acl)
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| Challenge: | Recent advances in large language models have enabled molecular reasoning for property prediction. however, toxicity arises from complex biological mechanisms, necessitating mechanistic reasoning for reliable prediction. |
| Approach: | They propose a benchmark that evaluates organ-level toxicity reasoning across multiple organs . they find strong predictive performance does not necessarily imply reliable reasoning . |
| Outcome: | The proposed benchmark evaluates toxicity prediction performance and reasoning quality across LLMs. |
Assessing LLM Reasoning Steps via Principal Knowledge Grounding (2025.findings-emnlp)
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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. |
Does Time Have Its Place? Temporal Heads: Where Language Models Recall Time-specific Information (2025.acl-long)
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| Challenge: | Temporal Heads are attention heads that primarily handle temporal knowledge. |
| Approach: | They discover Temporal Heads, specific attention heads that primarily handle temporal knowledge, through circuit analysis. |
| Outcome: | The proposed models can handle temporal knowledge without compromising time-invariant and question-answering performances. |