Papers by Jung-jae Kim
From Speculation Detection to Trustworthy Relational Tuples in Information Extraction (2023.findings-emnlp)
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| Challenge: | Existing studies on speculation detection are defined at sentence level, but not all factual tuples extracted from a sentence are speculative. |
| Approach: | They propose to study speculations in OIE tuples and determine whether a tample is speculative. |
| Outcome: | The proposed model is based on the LSOIE dataset and provides labels for speculative tuples. |
TransferCVLM: Transferring Cross-Modal Knowledge for Vision-Language Modeling (2024.findings-emnlp)
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| Challenge: | Recent large vision-language multimodal models pre-trained with huge amount of image-text pairs show remarkable performances in downstream tasks. |
| Approach: | They propose a method of efficient knowledge transfer that integrates pre-trained uni-modal models into a combined vision-language model without pre-training . they propose to fine-tune the model and transfer multimodal knowledge from a teacher vision-linguistic model to the CVLM for each task application. |
| Outcome: | The proposed method outperforms existing vision-language models in downstream tasks. |
CoinMath: Harnessing the Power of Coding Instruction for Math LLM (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have shown strong performance in solving mathematical problems, with code-based solutions proving particularly effective. |
| Approach: | They propose a learning strategy to enhance mathematical reasoning by diversifying the coding styles of code-based rationales. |
| Outcome: | The proposed learning strategy outperforms its baseline model, MAmmoTH, which uses code-based solutions. |
HiRAS: A Hierarchical Multi-Agent Framework for Paper-to-Code Generation and Execution (2026.findings-acl)
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| Challenge: | Existing approaches to automate computational research use fixed sequential agent pipelines with weak global coordination, which limits their robustness and overall performance. |
| Approach: | They propose a hierarchical multi-agent framework for end-to-end paper reproduction that employs supervisory manager agents to coordinate specialised agents across fine-grained stages. |
| Outcome: | The proposed framework improves the paper2code benchmark and significantly reduces hallucination in the evaluation. |
Revisiting Self-Play Preference Optimization: On the Role of Prompt Difficulty (2026.findings-acl)
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| Challenge: | incorporating difficult prompts into training fails to enhance overall performance, e.g., as prompt difficulty decreases. |
| Approach: | They investigate how prompts of varying difficulty influence self-play preference optimization . they use the reward of sampled responses of a prompt as a proxy for its difficulty . |
| Outcome: | The proposed model improves on difficult prompts and easy prompts, but fails to train on difficult ones and learns from failures. |
Open Information Extraction via Chunks (2023.emnlp-main)
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| Challenge: | Existing OIE systems split a sentence into tokens and recognize token spans as tuple relations and arguments. |
| Approach: | They propose to split a sentence into tokens and recognize token spans as tuple relations and arguments. |
| Outcome: | The proposed model achieves state-of-the-art on multiple OIE datasets showing that SaC has better properties than sentence as token sequence. |