Papers by Yaqiang Wu
OptiVerse: A Comprehensive Benchmark towards Optimization Problem Solving (2026.findings-acl)
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| Challenge: | Existing benchmarks focus on Mathematical Programming and Combinatorial Optimization, hindering comprehensive evaluation. |
| Approach: | They propose a benchmarking tool that compares 1,000 curated optimization problems across three difficulty levels. |
| Outcome: | The proposed model improves performance on hard problems while maintaining 27% accuracy. |
PhysPRM: A Generative Process Reward Model with Fine-grained Diagnosis for Physics Problem Solving (2026.findings-acl)
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| Challenge: | Existing Large Language Models (LLMs) struggle with physics problem solving due to difficulties in decoding implicit constraints and maintaining physical consistency. |
| Approach: | They propose a Generative PRM that treats evaluation as a generative task . it produces fine-grained diagnoses comprising critiques, final judgments, and specific error types . |
| Outcome: | The proposed model improves performance across seven benchmarks in Best-of-N and critique refinement strategies. |
Generalized Category Discovery with Large Language Models in the Loop (2024.findings-acl)
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Wenbin An, Wenkai Shi, Feng Tian, Haonan Lin, QianYing Wang, Yaqiang Wu, Mingxiang Cai, Luyan Wang, Yan Chen, Haiping Zhu, Ping Chen
| Challenge: | Generalized Category Discovery (GCD) is a crucial task that aims to recognize both known and novel categories from a set of unlabeled data. |
| Approach: | They propose a framework that introduces Large Language Models into the training loop to generate category names without human effort. |
| Outcome: | The proposed framework outperforms SOTA models on three benchmark datasets and generates accurate category names for the discovered clusters. |
MatchPrompt: Prompt-based Open Relation Extraction with Semantic Consistency Guided Clustering (2022.emnlp-main)
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| Challenge: | Existing methods for open relation extraction (OpenRE) focus on labeled and pre-defined instances, which are costly to acquire in reality. |
| Approach: | They propose a framework that can extract relations without pre-defined types from open-domain corpus with efficient knowledge transfer from a few pre-determined relational instances. |
| Outcome: | The proposed framework achieves the new SOTA results for OpenRE on different datasets. |