Papers by Jinu 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. |
Entailment-Preserving First-order Logic Representations in Natural Language Entailment (2025.acl-long)
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| Challenge: | First-order logic (FOL) is often used to represent logical entailment, but determining natural language (NL) enanglement using FOL remains a challenge. |
| Approach: | They propose an Entailment-Preserving FOL representations task and a method which trains an NL-to-FOL translator by using the natural language entailment labels as verifiable rewards. |
| Outcome: | The proposed method achieves 1.8–2.7% improvement in EPR and 17.4–20.6% increase in E PR@16 compared to baselines in three datasets. |
Scaling Evaluation-Time Compute with Reasoning Models as Evaluators (2026.findings-acl)
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Seungone Kim, Ian Wu, Jinu Lee, Xiang Yue, Seongyun Lee, Minkyeong Moon, Carolin Lawrence, Kiril Gashteovski, Julia Hockenmaier, Graham Neubig, Sean Welleck
| Challenge: | Language model (LM) evaluators that generate chain-of-thought reasoning are widely used for the assessment of LM responses. |
| Approach: | They investigate whether increasing LMs' "thinking" time through scaling test-time compute can improve an LM's evaluation capability. |
| Outcome: | The proposed reasoning models improve evaluation performance monotonically with the number of reasoning tokens generated, mirroring trends seen in LM reasoning. |
Evaluating Step-by-step Reasoning Traces: A Survey (2025.findings-emnlp)
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| Challenge: | Existing evaluation practices are inconsistent, resulting in fragmented progress across evaluator design and benchmark development. |
| Approach: | a survey provides a comprehensive overview of step-by-step reasoning evaluation . existing evaluation practices are inconsistent, resulting in fragmented progress . |
| Outcome: | The proposed evaluation criteria are based on four top-level categories . the results are presented in a systematic review of the literature. |
Learning to Rank Generation with Pairwise Partial Rewards (2023.emnlp-main)
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| Challenge: | Existing methods for conditional text generation suffer from large action space and delayed reward, as the reward can be computed only after an entire sequence is generated. |
| Approach: | They propose a method that provides partial rewards for intermediate actions taken on partial sequences to prioritize actions that lead to the generation of more desirable sequences. |
| Outcome: | The proposed method overcomes the limitations of the prevalent supervised maximum likelihood estimation approach. |
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 . |
Evaluating Legal Reasoning Traces with Legal Issue Tree Rubrics (2026.acl-long)
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| Challenge: | Evaluating the quality of LLM-generated reasoning traces in expert domains is essential for ensuring credibility and explainability, yet remains challenging due to the inherent complexity of such reasoning tasks. |
| Approach: | They propose a large-scale legal reasoning dataset with an emphasis on reasoning trace evaluation that converts court judgments into hierarchical trees of opposing parties’ arguments and the court’s conclusions. |
| Outcome: | The proposed model improves the quality of LLM-generated reasoning traces in legal domains, whereas RL improves correctness albeit with reduced coverage. |