Papers by Jihoon Lee
Think Clearly: Improving Reasoning via Redundant Token Pruning (2025.findings-emnlp)
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Daewon Choi, Jimin Lee, Jihoon Tack, Woomin Song, Saket Dingliwal, Sai Muralidhar Jayanthi, Bhavana Ganesh, Jinwoo Shin, Aram Galstyan, Sravan Babu Bodapati
| Challenge: | Recent large language models show promising capabilities in long-form reasoning . however, they tend to include substantial redundancy in reasoning paths . |
| Approach: | They propose a structure-aware pruning method that prioritizes removing redundant tokens . they remove redundant token and then resume the reasoning generation . |
| Outcome: | The proposed method shows strong performance on reasoning-intensive benchmarks without training. |
Semiparametric Token-Sequence Co-Supervision (2024.acl-long)
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| Challenge: | Using semiparametric token-sequence co-supervision, language models are trained using a finite parametric vocabulary space. |
| Approach: | They propose a semiparametric token-sequence co-supervision training method that leverages supervision from two different supervisions. |
| Outcome: | The proposed method outperforms models trained via each supervision independently and shows that it encourages a broader generalization capability across the model. |
Summary Level Training of Sentence Rewriting for Abstractive Summarization (D19-54)
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| Challenge: | Existing models rely on sentence-level rewards or suboptimal labels to achieve summary-level ROUGE scores. |
| Approach: | They propose a model that extracts salient sentences from a document and paraphrases them to generate a summary. |
| Outcome: | The proposed model improves on CNN/Daily Mail and New York Times datasets. |
Retrieval Visual Contrastive Decoding to Mitigate Object Hallucinations in Large Vision-Language Models (2025.findings-acl)
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| Challenge: | Large Vision Language Models are not free from the issue of Object Hallucination (OH) OH is a phenomenon where LVLMs generate hallucinated objects and descriptions in their outputs. |
| Approach: | They propose a method to suppress OH by referencing images from AI-generated images at the logit level. |
| Outcome: | The proposed method significantly improves existing methods on visual contrast decoding. |
LLM as a Risk Manager: LLM Semantic Filtering for Lead–Lag Trading in Prediction Markets (2026.acl-industry)
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Sumin Kim, Minjae Kim, Jihoon Kwon, Yoon Kim, Oscar Levy, Alejandro Lopez-Lira, Yongjae Lee, Chanyeol Choi
| Challenge: | Prediction markets provide a unique setting where event-level time series are directly tied to natural-language descriptions, yet discovering robust lead–lag relationships remains challenging due to spurious statistical correlations. |
| Approach: | They propose a statistical stage that uses Granger causality to identify candidate leader–follower pairs from market-implied probability time series and an LLM-based semantic stage that re-ranks these candidates by assessing whether the proposed direction admits a plausible economic transmission mechanism. |
| Outcome: | The proposed approach consistently outperforms the statistical baseline on Kalshi Economics markets. |