Papers by Hyunwoo Jung
Socratic-MCTS: Test-Time Visual Reasoning by Asking the Right Questions (2025.emnlp-main)
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| Challenge: | Recent research in vision-language models has centered around the possibility of equipping them with implicit long-form chain-of-thought reasoning via distillation and reinforcement learning. |
| Approach: | They propose a Monte Carlo Tree Search-inspired algorithm that injects subquestion–subanswer pairs into the model’s output stream to elicit hidden knowledge and induce long reasoning traces. |
| Outcome: | The proposed method yields a 2% improvement on MMMU-PRO, including a significant 9% gain in Liberal Arts. |
Episodic Memory Reader: Learning What to Remember for Question Answering from Streaming Data (P19-1)
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| Challenge: | Existing QA methods lack scalability and performance is difficult to solve with document-level contexts. |
| Approach: | They propose an end-to-end deep network model that sequentially reads the input contexts into an external memory while replacing memories that are less important for answering unseen questions. |
| Outcome: | The proposed model improves on a synthetic dataset and real-world large-scale textual and video QA datasets. |
FINKRX: Establishing Best Practices for Korean Financial NLP (2025.acl-industry)
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| Challenge: | Existing tools to evaluate large language models in the financial domain are limited by the inherently closed nature of the financial industry. |
| Approach: | They present the first open leaderboard for evaluating Korean large language models focused on finance. |
| Outcome: | The proposed model is FINKRX, a fully open and transparent LLM built using these best practices. |
Perceptions to Beliefs: Exploring Precursory Inferences for Theory of Mind in Large Language Models (2024.emnlp-main)
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| Challenge: | While theory of mind (ToM) is naturally developed for humans in childhood, large language models (LLMs) exhibit inconsistency in ToM tasks, despite early reports of successful cases. |
| Approach: | They propose to evaluate human ToM precursors-perception inference and perception-to-belief inference-in large language models (LLMs) by annotating characters’ perceptions on ToMi and FANToM. |
| Outcome: | The proposed method significantly improves LLMs’ performance in false belief scenarios. |