Papers by Jeonghyun Park
MARCH: Evaluating the Intersection of Ambiguity Interpretation and Multi-hop Inference (2026.findings-acl)
Copied to clipboard
Jeonghyun Park, Ingeol Baek, Seunghyun Yoon, Haeun Jang, Aparna Garimella, Akriti Jain, Nedim Lipka, Hwanhee Lee
| Challenge: | Existing benchmarks on multi-hop QA focus on single-hop and layered ambiguity, but they focus on ambiguous questions . ambiguities can arise at any stage, complicating the reasoning process . |
| Approach: | They propose a benchmark to evaluate ambiguity in multi-hop question answering . they propose MARCH, which uses 2,209 carefully annotated questions . |
| Outcome: | The proposed framework outperforms existing approaches and significantly outperfies existing frameworks. |
Investigating Language Preference of Multilingual RAG Systems (2025.findings-acl)
Copied to clipboard
| Challenge: | Empirical results show that DKM-RAG mitigates language preference in generation and enhances performance across diverse linguistic settings. |
| Approach: | They propose a framework that integrates translated passages with internal knowledge to overcome these issues. |
| Outcome: | The proposed framework mitigates language preference in generation and enhances performance across diverse linguistic settings. |
Enhancing Multilingual RAG Systems with Debiased Language Preference-Guided Query Fusion (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing studies show that mRAGs exhibit a perceived preference for high-resource languages, particularly English. |
| Approach: | They propose a debiased language preference metric to explicitly factor out structural priors . they propose mRAG framework that leverages monolingual alignment to optimize cross-lingual retrieval and generation. |
| Outcome: | The proposed framework outperforms baselines for English pivoting and mRAG in multiple languages. |
FeRG-LLM : Feature Engineering by Reason Generation Large Language Models (2025.findings-naacl)
Copied to clipboard
| Challenge: | FeRG-LLM is a large language model that performs feature engineering at an 8billion-parameter scale. |
| Approach: | They propose a framework to perform feature engineering at an 8billion-parameter scale using conversational dialogues. |
| Outcome: | The proposed framework outperforms Llama 3.1 70B and Llma 3.2 on most datasets while using fewer resources and achieving reduced inference time. |