Papers by Jiawei Peng

Enhancing Language Representation with Constructional Information for Natural Language Understanding (2023.acl-long)

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Lvxiaowei Xu, Jianwang Wu, Jiawei Peng, Zhilin Gong, Ming Cai, Tianxiang Wang

Challenge:	Recent advances in natural language processing focus on acquiring lexico-semantic information.
Approach:	They propose a construction grammar which highlights the pairings of form and meaning to enrich language representation.
Outcome:	The proposed model is superior to existing models on a variety of NLU tasks.

Few-Shot Named Entity Recognition: An Empirical Baseline Study (2021.emnlp-main)

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Jiaxin Huang, Chunyuan Li, Krishan Subudhi, Damien Jose, Shobana Balakrishnan, Weizhu Chen, Baolin Peng, Jianfeng Gao, Jiawei Han

Language Models

Challenge:	Existing methods to build named entity recognition systems with limited labeled data are lacking.
Approach:	They propose three orthogonal schemes to build named entity recognition systems when labeled data is limited.
Outcome:	The proposed NER systems outperform existing methods on few-shot and training-free settings.

Efficient Contextualized Representation: Language Model Pruning for Sequence Labeling (D18-1)

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Liyuan Liu, Xiang Ren, Jingbo Shang, Xiaotao Gu, Jian Peng, Jiawei Han

Semantic Text Processing

Challenge:	Existing efforts to train pre-trained language models have brought significant improvements to various NLP applications.
Approach:	They propose to compress bulky LMs while preserving useful information for a specific task.
Outcome:	The proposed method can detach any layer without affecting others, and stretch shallow and wide LMs to be deep and narrow.

FCGEC: Fine-Grained Corpus for Chinese Grammatical Error Correction (2022.findings-emnlp)

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Lvxiaowei Xu, Jianwang Wu, Jiawei Peng, Jiayu Fu, Ming Cai

Syntactic Text Processing

Challenge:	grammatical error correction (GEC) is a complex task that requires high-quality data from native speakers.
Approach:	They propose a human-annotated corpus to detect, identify and correct grammatical errors in Chinese examinations.
Outcome:	The proposed model outperforms other models in low-resource settings, but there is a significant gap between the models and humans that encourages future models to bridge it.

ActionIE: Action Extraction from Scientific Literature with Programming Languages (2024.acl-long)

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Xianrui Zhong, Yufeng Du, Siru Ouyang, Ming Zhong, Tingfeng Luo, Qirong Ho, Hao Peng, Heng Ji, Jiawei Han

Multimodality

Challenge:	a method that extracts experimental procedures from human language into actionable sequences in robotics language is challenging given the complexity of the instructions and context-dependent nature of the instruction.
Approach:	They propose a method that converts actions written in natural language into Python code that can be easily translated into robotics language.
Outcome:	The proposed method can extract experimental procedures from human language into actionable sequences in robotics language.

CriticLean: Critic-Guided Reinforcement Learning for Mathematical Formalization (2026.acl-long)

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Zhongyuan Peng, Yifan Yao, Kaijing Ma, Shuyue Guo, Yizhe Li, Yichi Zhang, Chenchen Zhang, Yifan Zhang, Zhouliang Yu, Luming Li, Minghao Liu, Yihang Xia, Jiawei Shen, Yuchen Wu, Yixin Cao, Zhaoxiang Zhang, Wenhao Huang, Jiaheng Liu, Ge Zhang

Reasoning

Challenge:	Existing approaches to formalizing mathematical statements face limitations in accuracy, especially in the context of complex, highlevel problems that involve sophisticated mathematical reasoning.
Approach:	They propose a CriticLean framework that elevates the role of the critic from a passive validator to an active learning component and introduce a benchmark to measure models’ ability to distinguish semantically correct from incorrect formalizations.
Outcome:	The proposed framework outperforms open- and closed-source benchmarks and shows that it significantly outperformed existing models.

VReST: Enhancing Reasoning in Large Vision-Language Models through Tree Search and Self-Reward Mechanism (2025.acl-long)

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Congzhi Zhang, Jiawei Peng, Zhenglin Wang, Yilong Lai, Haowen Sun, Heng Chang, Fei Ma, Weijiang Yu

Multimodality

Challenge:	Large Vision-Language Models (LVLMs) have shown exceptional performance in multimodal tasks, but their effectiveness in complex visual reasoning is constrained.
Approach:	They propose a training-free approach that enhances Reasoning in Large Vision-Language Models . they propose integrating Monte Carlo Tree Search and Self-Reward mechanisms into the reasoning tree .
Outcome:	The proposed approach surpasses current prompting methods and secures state-of-the-art performance across three multimodal reasoning benchmarks.

Transforming Visual Scene Graphs to Image Captions (2023.acl-long)

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Xu Yang, Jiawei Peng, Zihua Wang, Haiyang Xu, Qinghao Ye, Chenliang Li, Songfang Huang, Fei Huang, Zhangzikang Li, Yu Zhang

Multimodality

Challenge:	Existing approaches to generate captions using image captioning are based on multi-head attention (MHA)
Approach:	They propose to transform scene graphs into more descriptive captions by using multi-head attention to build a Graph Neural Network (GNN) . they construct a Mixture-of-Expert (MOE)-based decoder where each expert is built on MHA for discriminating the graph embeddings to generate different kinds of words.
Outcome:	The proposed framework can generate captions from multiple visual features and objects . it is based on a mixture-of-expert (MOE)-based decoder based upon MHA .

CoELM: Construction-Enhanced Language Modeling (2024.acl-long)

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Lvxiaowei Xu, Zhilin Gong, Jianhua Dai, Tianxiang Wang, Ming Cai, Jiawei Peng

Language Models

Challenge:	Recent studies show that integrating constructional information can improve the performance of pre-trained language models.
Approach:	They propose a construction-Enhanced language model that embeds constructional semantics into language models for natural language generation.
Outcome:	The proposed model outperforms existing models on various benchmarks.

Frame First, Then Extract: A Frame-Semantic Reasoning Pipeline for Zero-Shot Relation Triplet Extraction (2025.emnlp-main)

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Zehan Li, Fu Zhang, Wenqing Zhang, Jiawei Li, Zhou Li, Jingwei Cheng, Tianyue Peng

Responsible & Trustworthy NLP

Challenge:	Existing methods to extract triplets for unseen relations rely on costly fine-tuning and lack structured semantic guidance.
Approach:	They propose a framework that adopts a "frame first, then extract" paradigm to extract triplets from unstructured text.
Outcome:	The proposed framework achieves competitive zero-shot performance on multiple benchmarks and can be used to enhance existing extraction methods.