Papers by Dongwei Jiang
LeanReasoner: Boosting Complex Logical Reasoning with Lean (2024.naacl-long)
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| Challenge: | Large language models (LLMs) often struggle with complex logical reasoning due to logical inconsistencies and the inherent difficulty of such reasoning. |
| Approach: | They propose a method that formalizes logical reasoning problems into theorems within Lean and then proves or disproving the corresponding theorels. |
| Outcome: | The proposed method achieves state-of-the-art performance on the FOLIO dataset and near this level on ProofWriter. |
Supplement Generation Training for Enhancing Agentic Task Performance (2026.findings-acl)
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Young Min Cho, Daniele Bonadiman, Divya Bhargavi, Tamer Alkhouli, Salvatore Romeo, Dongwei Jiang, Khushbu Pahwa, Yubin Ge, Etsuko Ishii, Monica Sunkara, Yi Zhang
| Challenge: | Training large foundation models for agentic tasks is impractical due to high computational costs, long iteration cycles, and rapid obsolescence as new models are released. |
| Approach: | They propose a method that trains a small LLM to generate supplemental text that helps the larger LLM solve the task more effectively. |
| Outcome: | The proposed approach decouples task-specific optimization from large foundation models . it achieves consistent and significant performance gains across diverse tasks and models - all without gradient access to the actor model. |
Benchmarking Language Model Creativity: A Case Study on Code Generation (2025.naacl-long)
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| Challenge: | Recent studies on LLM creativity evaluation focus on open-ended generation tasks . however, the degree to which LLMs possess and utilize creativity for problem-solving remains unclear . |
| Approach: | They propose a framework for quantifying LLM creativity that incorporates design ingredients . they introduce DENIAL PROMPTING which pushes LLMs to develop more creative solutions . |
| Outcome: | The proposed framework quantifies creativity in LLMs on Codeforces problems . it also finds that even the most creative model fails to demonstrate human-like creativity . |
Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic (2024.emnlp-main)
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Nathaniel Weir, Kate Sanders, Orion Weller, Shreya Sharma, Dongwei Jiang, Zhengping Jiang, Bhavana Dalvi Mishra, Oyvind Tafjord, Peter Jansen, Peter Clark, Benjamin Van Durme
| Challenge: | Recent language models allow structured reasoning with text, but lack of a clear protocol for discerning entailment causes noisy datasets and limited performance gains. |
| Approach: | They propose a consistent approach to annotating decompositional entailment and evaluate its impact on LLM-based textual inference. |
| Outcome: | The proposed approach has higher internal consistency than prior decompositional entailment datasets and significantly improves proof quality and accuracy. |
RATIONALYST: Pre-training Process-Supervision for Improving Reasoning (2025.acl-long)
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Dongwei Jiang, Guoxuan Wang, Yining Lu, Andrew Wang, Jingyu Zhang, Chuyu Liu, Benjamin Van Durme, Daniel Khashabi
| Challenge: | RATIONALYST is a model for process-supervision of reasoning based on pretraining on rationale annotations extracted from unlabeled data. |
| Approach: | They propose a model for process-supervision of reasoning based on pre-training on rationale annotations extracted from unlabeled data. |
| Outcome: | RATIONALYST improves reasoning accuracy by 3.9% on representative reasoning benchmarks. |