Papers by Wonseok Lee
SymBa: Symbolic Backward Chaining for Structured Natural Language Reasoning (2025.naacl-long)
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| Challenge: | Among different methods for structured reasoning, we focus on backward chaining, where the goal is recursively decomposed into subgoals by searching and applying rules. |
| Approach: | They propose a backward chaining system that integrates a symbolic solver and an LLM to improve the performance of LLM-based reasoning. |
| Outcome: | The proposed system improves deductive, relational, and arithmetic reasoning benchmarks compared to baselines. |
Taxation Perspectives from Large Language Models: A Case Study on Additional Tax Penalties (2026.eacl-long)
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| Challenge: | Large language models (LLMs) have demonstrated promising results across various domains, including the legal domain. |
| Approach: | They propose a benchmark to assess the ability of large language models to predict the legitimacy of additional tax penalties. |
| Outcome: | The proposed model is based on 100 Korean court precedents and 100 binary-choice questions. |
Cost-effective End-to-end Information Extraction for Semi-structured Document Images (2021.emnlp-main)
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| Challenge: | a real-world information extraction system for semi-structured document images often involves a long pipeline of multiple modules, which can lead to unstable performance if not designed carefully. |
| Approach: | They propose to use a sequence generation task to build an end-to-end IE system . they propose to combine three manually engineered modules with one data-driven module . |
| Outcome: | The proposed system can be easily replaced and deployed in large-scale production. |
LegalSearchLM: Rethinking Legal Case Retrieval as Legal Elements Generation (2025.emnlp-main)
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| Challenge: | Existing studies on legal case retrieval have limited results . limited representations and legally irrelevant matches are often used . |
| Approach: | They propose a large-scale Korean LCR benchmark and a retrieval model that performs legal element reasoning over the query case. |
| Outcome: | a new model outperforms baseline models on a Korean LCR benchmark . it performs state-of-the-art on 411 diverse crime types in queries over 1.2M candidate cases . previous studies have shown that the model can generalize to out-of domain cases if it is trained on in-domain data . |
Progressive Multimodal Search and Reasoning for Knowledge-Intensive Visual Question Answering (2026.acl-long)
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| Challenge: | Existing approaches to knowledge-intensive visual question answering lack mechanisms to revise misdirected reasoning. |
| Approach: | They propose a framework that progressively constructs a structured reasoning trajectory . they use dual-scope queries to retrieve diverse knowledge from heterogeneous knowledge bases . |
| Outcome: | The proposed framework improves retrieval recall and end-to-end answer accuracy. |
SAAS: Solving Ability Amplification Strategy for Enhanced Mathematical Reasoning in Large Language Models (2024.emnlp-industry)
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| Challenge: | Existing approaches to enhance mathematical reasoning and problem-solving abilities of Large Language Models (LLMs) despite their remarkable performance across domains, a notable challenge persists in the realm of mathematical reasoning. |
| Approach: | They propose a sequential learning approach that integrates the Chain-of-Thought and the Program-ofThough. |
| Outcome: | The proposed approach achieves state-of-the-art (SOTA) performance by integrating CoT and PoT learning. |