Papers by Sungjae Lee
Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in Table Question Answering (2026.acl-long)
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| Challenge: | Existing approaches to QA tables rely on independent row or column selection, fail to capture cross-row and cross-column dependencies, or attempt global reasoning. |
| Approach: | They propose a divide-and-conquer subtable selection framework that aggregates local evidence without requiring explicit global reasoning. |
| Outcome: | The proposed framework outperforms previous approaches to table QA in the noisy context. |
Semantic Exploration with Adaptive Gating for Efficient Problem Solving with Language Models (2025.acl-long)
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| Challenge: | Existing methods for multi-step reasoning suffer from inefficiency and redundancy . existing methods neglect the diversity of task difficulties leading to extensive searches even for easy tasks . |
| Approach: | They propose a method that explores reasoning paths with a gating mechanism that decides whether to conduct a tree search based on the confidence level of answers from a previous simple reasoning method. |
| Outcome: | The proposed method significantly improves accuracy by 4.3% on average while requiring only 31% of computational costs. |
PaT: Planning-after-Trial for Efficient Test-Time Code Generation (2026.acl-long)
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| Challenge: | Existing methods for scaling test-time computation are rigid and inefficient . a heterogeneous configuration achieves performance comparable to a large homogeneously model . |
| Approach: | They propose an adaptive planning policy that invokes a planner only upon verification failure. |
| Outcome: | The proposed model achieves comparable performance to a large homogeneous model while reducing inference cost by approximately 69% across multiple benchmarks and model families. |
Selective Vision is the Challenge for Visual Reasoning: A Benchmark for Visual Argument Understanding (2024.emnlp-main)
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| Challenge: | Visual arguments rely on images to persuade viewers to do or believe something . |
| Approach: | They propose three tasks for evaluating visual argument understanding . they use visual premises, commonsense premises and reasoning trees to analyze visual arguments . |
| Outcome: | The proposed tasks evaluate visual argument understanding using a dataset of 1,611 images annotated with 5,112 visual premises (with regions), 5,574 commonsense premises, and reasoning trees connecting them into structured arguments. |
What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers (2021.emnlp-main)
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Boseop Kim, HyoungSeok Kim, Sang-Woo Lee, Gichang Lee, Donghyun Kwak, Jeon Dong Hyeon, Sunghyun Park, Sungju Kim, Seonhoon Kim, Dongpil Seo, Heungsub Lee, Minyoung Jeong, Sungjae Lee, Minsub Kim, Suk Hyun Ko, Seokhun Kim, Taeyong Park, Jinuk Kim, Soyoung Kang, Na-Hyeon Ryu, Kang Min Yoo, Minsuk Chang, Soobin Suh, Sookyo In, Jinseong Park, Kyungduk Kim, Hiun Kim, Jisu Jeong, Yong Goo Yeo, Donghoon Ham, Dongju Park, Min Young Lee, Jaewook Kang, Inho Kang, Jung-Woo Ha, Woomyoung Park, Nako Sung
| Challenge: | GPT-3 has been used to train large-scale language models on hundreds of billion scale data. |
| Approach: | They propose a Korean variant of GPT-3 that uses Korean tokens to train in-context models. |
| Outcome: | The proposed method shows state-of-the-art zero-shot and few-shot learning on downstream tasks in Korean. |
Efficient Latent Semantic Clustering for Scaling Test-Time Computation of LLMs (2025.findings-emnlp)
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| Challenge: | Existing methods for scaling test-time computation rely on external models that introduce substantial computational overhead and fail to capture context-aware semantics. |
| Approach: | They propose a method that leverages the generator LLM’s internal hidden states for clustering, eliminating the need for external models. |
| Outcome: | The proposed method improves the computational efficiency of test-time scaling while maintaining or exceeding the performance of existing methods. |
Self-Training Large Language Models with Confident Reasoning (2025.findings-emnlp)
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| Challenge: | Large language models generate reasoning paths before final answers, but learning such a path requires costly human supervision. |
| Approach: | They propose a method that fine-tunes LLMs to prefer reasoning paths with high confidence . they propose 'cORE-PO' that fine tunes Lms to choose high-quality reasoning paths . |
| Outcome: | The proposed method improves the accuracy of outputs on four in-distribution and two out-of-difference benchmarks. |
VisEscape: A Benchmark for Evaluating Exploration-driven Decision-making in Virtual Escape Rooms (2025.emnlp-main)
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| Challenge: | Existing studies on embodied agents have addressed the importance of exploration in environments where tasks and solutions are not predefined. |
| Approach: | They propose a virtual escape room that evaluates AI models in a dynamic environment . they propose to integrate memory management and reasoning into the simulation . |
| Outcome: | The proposed model improves in dynamic and exploration-driven environments by integrating memory management and reasoning. |