Papers by Yunhui Jang
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. |
Structural Reasoning Improves Molecular Understanding of LLM (2025.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have shown significant performance, approaching human perception levels. |
| Approach: | They propose an approach that sketches molecular structures for reasoning by explicitly incorporating key structural features into the model. |
| Outcome: | The proposed framework improves molecular understanding through extensive experiments. |
Improving Chemical Understanding of LLMs via SMILES Parsing (2025.emnlp-main)
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| Challenge: | Molecular string representations such as SMILES and SELFIES are becoming a standard format for applying large language models (LLMs) however, molecular strings follow complex syntactic rules for encoding molecules, which LLMs struggle to interpret. |
| Approach: | They propose a framework that parses SMILES into clean and deterministic tasks to promote graph-level molecular comprehension. |
| Outcome: | The proposed framework improves structural comprehension and competes with the baseline on the Mol-Instructions benchmark. |
MT-Mol: Multi Agent System with Tool-based Reasoning for Molecular Optimization (2025.findings-emnlp)
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| Challenge: | Structured reasoning and tool-grounded molecular optimization are still underexplored. |
| Approach: | They propose a framework for molecular optimization that leverages tool-guided reasoning and role-specialized LLM agents. |
| Outcome: | a new framework outperforms existing LLM frameworks on 15 of 23 tasks. |