Papers by Kangda Wei
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)
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Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin
| Challenge: | a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities . |
| Approach: | They present a comparative analysis to identify and distinguish LLM activities from human activities. |
| Outcome: | The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities. |
When Do Decompositions Help for Machine Reading? (2023.emnlp-main)
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| Challenge: | Existing work on decompositions of complex questions has focused on multi-step reasoning . but, in machine reading, it is unclear when decomposing is helpful . |
| Approach: | They conduct experiments on decompositions in machine reading to unify recent work . they find that decomposing complex questions can be helpful in zero or limited-data settings . |
| Outcome: | The proposed model can learn decompositions implicitly even with limited data, the study shows . the results are consistent with previous work on decomposing complex questions . |
MMR-GRPO: Accelerating GRPO-Style Training through Diversity-Aware Reward Reweighting (2026.findings-acl)
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| Challenge: | Recent advances in large language models (LLMs) have demonstrated remarkable capabilities in mathematical reasoning tasks. |
| Approach: | They propose to use Maximal Marginal Relevance to reweigh rewards of multiple rollouts by balancing rollout quality with diversity to reduce rollout redundancy. |
| Outcome: | The proposed approach reduces training time and costs by 47.9% . evaluations across three model sizes, three GRPO variants, and five mathematical reasoning benchmarks show that it achieves comparable peak performance while requiring on average 70.2% less wall-clock time. |
Leveraging Multiple Teachers for Test-Time Adaptation of Language-Guided Classifiers (2023.findings-emnlp)
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| Challenge: | Recent approaches focus on language-guided classifiers that can generalize in zero-shot settings, but their performance varies significantly between different language explanations in unpredictable ways. |
| Approach: | They propose a framework that uses data programming to adapt a language-guided classifier for a new task when provided with multiple teachers and unlabeled test examples. |
| Outcome: | The proposed framework outperforms a baseline from previous work by 9.3%. |
Are LLMs Good Annotators for Discourse-level Event Relation Extraction? (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have demonstrated proficiency in a wide array of natural language processing tasks, but their effectiveness over discourse-level event relation extraction tasks remains unexplored. |
| Approach: | They evaluate LLMs' ability to address discourse-level event relation extraction tasks using an open-source model and a commercial model. |
| Outcome: | The proposed model performs poorly on discourse-level event relation extraction tasks. |
LegalCore: A Dataset for Event Coreference Resolution in Legal Documents (2025.findings-acl)
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Kangda Wei, Xi Shi, Jonathan Tong, null Sai Ramana Reddy, Anandhavelu Natarajan, Rajiv Jain, Aparna Garimella, Ruihong Huang
| Challenge: | Existing research on event coreference resolution is limited to news articles . existing datasets for news articles are limited to events and coreferences . |
| Approach: | They present a dataset for the legal domain LegalCore which has been annotated with event and event coreference information. |
| Outcome: | The legal contract documents annotated in this dataset are several times longer than news articles, with an average length of around 25k tokens per document. |
Mitigating Gender Bias via Fostering Exploratory Thinking in LLMs (2025.findings-emnlp)
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| Challenge: | Large Language Models often exhibit gender bias, resulting in unequal treatment of male and female subjects across contexts. |
| Approach: | They propose a framework that encourages exploratory thinking in large language models . the framework generates story pairs featuring male and female protagonists in structurally identical scenarios . |
| Outcome: | The proposed framework reduces gender bias while preserving or even enhancing general model capabilities. |