Papers by Jeongjae Park
EpiCaR: Knowing What You Don’t Know Matters for Better Reasoning in LLMs (2026.acl-long)
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| Challenge: | Existing approaches to improving reasoning abilities of large language models incur a significant calibration cost. |
| Approach: | They propose an epistemic learning problem that integrates reasoning and calibration into an iterative supervised training framework. |
| Outcome: | The proposed method achieves Pareto-superiority over standard baselines in accuracy and calibration. |
Evaluating Structure-Aware Retrieval and Safety in Statute-Centric Legal QA (2026.acl-long)
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Kyubyung Chae, Jewon Yeom, Jeongjae Park, Seunghyun Bae, Ijun Jang, Hyunbin Jin, Jinkwan Jang, Taesup Kim
| Challenge: | Legal QA benchmarks focus on case law, overlooking statute-centric regulatory reasoning . relevant evidence is distributed across hierarchically linked documents, creating statutory retrieval gap . |
| Approach: | They propose a structure- and safety-aware benchmark for statute-centric legal QA . the benchmark assesses whether models can retrieve hierarchically fragmented evidence . |
| Outcome: | The proposed benchmark evaluates whether models can retrieve hierarchically fragmented evidence and safely abstain when statutory context is insufficient. |