Papers by Chenghao Wang

ReCode: Robustness Evaluation of Code Generation Models (2023.acl-long)

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Shiqi Wang, Zheng Li, Haifeng Qian, Chenghao Yang, Zijian Wang, Mingyue Shang, Varun Kumar, Samson Tan, Baishakhi Ray, Parminder Bhatia, Ramesh Nallapati, Murali Krishna Ramanathan, Dan Roth, Bing Xiang

Responsible & Trustworthy NLP

Challenge:	Existing work on robustness in text or code tasks has focused on classification, while robustness for code generation tasks is an uncharted area.
Approach:	They propose a robustness evaluation benchmark for code generation models that customizes over 30 transformations specifically for code on docstrings, function and variable names, code syntax, and code format.
Outcome:	The proposed model performs better on human annotators and on SOTA models with human annnotators.

Knowledge-Infused Legal Wisdom: Navigating LLM Consultation through the Lens of Diagnostics and Positive-Unlabeled Reinforcement Learning (2024.findings-acl)

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Yang Wu, Chenghao Wang, Ece Gumusel, Xiaozhong Liu

Language Models

Challenge:	Recent years have witnessed a substantial increase in the demand for legal services, especially for individuals with modest means.
Approach:	They propose a diagnostic legal large language model which uses adaptive lawyer-like diagnostic questions to collect additional case information and then provides high-quality feedback.
Outcome:	The proposed model surpasses classical LLMs by providing outstanding performance and a remarkable user experience in the legal domain.

Hello Again! LLM-powered Personalized Agent for Long-term Dialogue (2025.naacl-long)

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Hao Li, Chenghao Yang, An Zhang, Yang Deng, Xiang Wang, Tat-Seng Chua

Natural Language Interfaces

Challenge:	Existing dialogue systems focus on brief single-session interactions, neglecting real-world needs for long-term companionship and personalized interactions.
Approach:	They propose a model-agnostic framework for long-term dialogue agents . they use event summary and persona management to enable reasoning .
Outcome:	The proposed framework incorporates three independently tunable modules dedicated to event perception, persona extraction, and response generation.

Drivel-ology: Challenging LLMs with Interpreting Nonsense with Depth (2025.emnlp-main)

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Yang Wang, Chenghao Xiao, Chia-Yi Hsiao, Zi Yan Chang, Chi-Li Chen, Tyler Loakman, Chenghua Lin

Responsible & Trustworthy NLP

Challenge:	Despite excelling at many natural language processing tasks, large language models fail to grasp the layered semantics of Drivelological text.
Approach:	They construct a benchmark dataset of over 1,200+ carefully curated and diverse examples across English, Mandarin, Spanish, French, Japanese, and Korean to examine their Drivelological characteristics.
Outcome:	The proposed models lack conceptual understanding and lack conceptual and semantic accuracy.

Beyond Quantity: Trajectory Diversity Scaling for Code Agents (2026.findings-acl)

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Guhong Chen, Chenghao Sun, Cheng Fu, Qiyao Wang, Zhihong Huang, ChaoPeng Wei, Guangxu Chen, Feiteng Fang, Ahmadreza Argha, Bing Zhao, Xander Xu, Qi Han, Hamid Alinejad-Rokny, Qiang Qu, Binhua Li, Shiwen Ni, Min Yang, HU Wei, Yongbin Li

Green & Sustainable NLP

Challenge:	Code large language models (LLMs) are becoming tool-interactive agents . quantity-centric scaling exhibits an early bottleneck that underutilizes trajectory data . et al.: a new approach to scale trajectory diversity improves tool-use generalization .
Approach:	They propose a Trajectory Diversity Scaling-based data synthesis framework for code agents that scales performance through diversity rather than raw volume.
Outcome:	Experiments on general tool-use benchmarks and code agent tasks show that TDScaling improves tool-user generalization and inherent coding proficiency.

Agent-GWO: Collaborative Agents for Dynamic Prompt Optimization in Large Language Models (2026.findings-acl)

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Xudong Wang, Chaoning Zhang, Chenghao Li, Shuxu Chen, Qigan Sun, Jiaquan Zhang, Fachrina Dewi Puspitasari, Tae-Ho Kim, Jiwei Wei, Malu Zhang, Guoqing Wang, Yang Yang, Heng Tao Shen

Language Models

Challenge:	Existing automatic prompt optimization methods fail to optimize prompts and decoding hyperparameters within a unified framework to achieve stable global improvements.
Approach:	They propose a dynamic prompt optimization framework for complex reasoning that unifies prompt templates and decodes hyperparameters as inheritable agent configurations.
Outcome:	Experiments on multiple mathematical and hybrid reasoning benchmarks show that Agent-GWO improves accuracy and stability over existing prompt optimization methods.

Elevating Legal LLM Responses: Harnessing Trainable Logical Structures and Semantic Knowledge with Legal Reasoning (2025.naacl-long)

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Rujing Yao, Yang Wu, Chenghao Wang, Jingwei Xiong, Fang Wang, Xiaozhong Liu

Information Retrieval

Challenge:	Existing approaches to large language models focus on semantic similarity, neglecting the intricate logical structures and reasoning essential for addressing complex legal issues.
Approach:	They propose a Logical-Semantic Integration Model (LSIM) that bridges semantic and logical coherence and a supervised framework that integrates semantic features with in-context learning.
Outcome:	The proposed framework significantly improves accuracy and reliability on a real-world legal QA dataset.

CITE: Benchmarking Heterogeneous Text-Attributed Graph Models (2026.acl-long)

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Chenghao Zhang, Qingqing Long, Ludi Wang, Wenjuan Cui, Jianjun Yu, Yi Du

Structured Data in NLP

Challenge:	Recent advances in large language models and text-aware graph learning have increased interest in reasoning over text-attributed graphs.
Approach:	They propose a large-scale heterogeneous text-attributed graph benchmark for catalytic materials that contains over 438K nodes and 1.2M edges . they establish standardized evaluation protocols for node classification and link prediction and conduct ablation studies to assess the impact of graph heterogenity and textual attributes.
Outcome:	The proposed benchmarks are compared to existing methods and provide a baseline for the evaluation of four classes of learning paradigms.

Beyond One-Size-Fits-All: Inversion Learning for Highly Effective NLG Evaluation Prompts (2026.tacl-1)

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Hanhua Hong, Chenghao Xiao, Yang Wang, Yiqi Liu, Wenge Rong, Chenghua Lin

Responsible & Trustworthy NLP

Challenge:	Evaluating natural language generation systems is challenging due to the diversity of valid outputs.
Approach:	They propose an inversion learning method that learns effective reverse mappings from model outputs back to their input instructions.
Outcome:	The proposed method requires only a single evaluation sample and eliminates manual prompt engineering.

InterIDEAS: Philosophical Intertextuality via LLMs (2025.emnlp-main)

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Yue Yang, Yinzhi Xu, Chenghao Huang, JohnMichael Jurgensen, Han Hu, Hao Wang

Challenge:	a new dataset aims to bridge philosophy, literary studies, and natural language processing (NLP) by integrating theories of intertextuality with bibliometric techniques.
Approach:	They propose a dataset that bridges philosophy, literary studies, and natural language processing (NLP) it combines theories of intertextuality from literary studies with bibliometric techniques and recent LLMs .
Outcome:	a new dataset bridges philosophy, literary studies, and natural language processing (NLP) to analyze intertextuality . the proposed method helps scholars understand the intellectual, social, and historical relations embedded in texts . it also contributes to the development of language models, authors say .