Papers by Jihoon Kim
Hierarchical Retrieval with Evidence Curation for Open-Domain Financial Question Answering on Standardized Documents (2025.findings-acl)
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| Challenge: | standardized documents share similar formats and table structures . this similarity forces traditional RAG methods to misidentify near-duplicate text . |
| Approach: | They propose a hierarchical retrieval framework that performs hierarchically to reduce confusion among similar texts. |
| Outcome: | The proposed framework reduces confusion among similar documents by removing irrelevant passages . it generates complementary queries to collect missing information . |
A Practical Approach for Building Production-Grade Conversational Agents with Workflow Graphs (2025.acl-industry)
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Chiwan Park, Wonjun Jang, Daeryong Kim, Aelim Ahn, Kichang Yang, Woosung Hwang, Jihyeon Roh, Hyerin Park, Hyosun Wang, Min Seok Kim, Jihoon Kang
| Challenge: | Large Language Models (LLMs) have led to significant improvements in various service domains, including search, recommendation, and chatbot applications. |
| Approach: | They propose a framework for developing scalable, controllable, and reliable AI-driven agents that can be applied to real-world applications. |
| Outcome: | The proposed framework bridges the gap between academic research and real-world application, and enables scalable, controllable, and reliable AI-driven agents. |
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. |
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. |
X-SNS: Cross-Lingual Transfer Prediction through Sub-Network Similarity (2023.findings-emnlp)
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| Challenge: | Cross-lingual transfer (XLT) is an emergent ability of multilingual language models that preserves their performance when evaluated in non-English languages. |
| Approach: | They propose to use sub-network similarity between two languages as a proxy for XLT prediction. |
| Outcome: | The proposed method shows proficiency in ranking candidates for zero-shot XLT, achieving an improvement of 4.6% on average in terms of NDCG@3. |
Mamba Drafters for Speculative Decoding (2025.findings-emnlp)
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Daewon Choi, Seunghyuk Oh, Saket Dingliwal, Jihoon Tack, Kyuyoung Kim, Woomin Song, Seojin Kim, Insu Han, Jinwoo Shin, Aram Galstyan, Shubham Katiyar, Sravan Babu Bodapati
| Challenge: | Existing drafters that use external drafters suffer from slower drafting while self-speculation methods use drafters tailored to the target model but require re-training. |
| Approach: | They propose a drafter based on a state space model, Mamba, as a solution that combines the best aspects of both approaches. |
| Outcome: | The proposed drafters outperform existing drafters while using less memory and maintaining their cross-model adaptability. |