Papers by Chenghao Yang
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
| 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. |
On Isotropy, Contextualization and Learning Dynamics of Contrastive-based Sentence Representation Learning (2023.findings-acl)
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| Challenge: | Incorporating contrastive learning objectives in sentence representation learning has yielded significant improvements on many sentence-level NLP tasks. |
| Approach: | They aim to examine why contrastive learning works for learning sentence-level semantics . they interpret successes through the geometry of the representation shifts based on isotropy . |
| Outcome: | The proposed model improves on many sentence-level NLP tasks, but it is not well understood why it works. |
Knowledge-Infused Legal Wisdom: Navigating LLM Consultation through the Lens of Diagnostics and Positive-Unlabeled Reinforcement Learning (2024.findings-acl)
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| 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. |
Effective Distillation of Table-based Reasoning Ability from LLMs (2024.lrec-main)
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| Challenge: | Existing work on table-based reasoning distillation has focused on smaller models with limited performance. |
| Approach: | They propose a table-based reasoning distillation approach to distill LLMs into smaller models . their results show that a 220 million parameter model fine-tuned using distilled data improves performance . |
| Outcome: | The proposed model improves on a scientific table-to-text generation dataset and surpasses specific LLMs. |
Revealing and Enhancing Core Visual Regions: Harnessing Internal Attention Dynamics for Hallucination Mitigation in LVLMs (2026.findings-acl)
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| Challenge: | Existing training-free methods are vulnerable to the attention sink phenomenon . Existing methods include contrastive decoding and auxiliary expert models . |
| Approach: | They propose a training-free attention intervention that constructs a PAD map to identify semantically core visual regions and applies per-head Median Absolute Deviation Scaling to adaptively control the intervention strength. |
| Outcome: | The proposed intervention improves visual grounding and reduces hallucinations on multiple LVLMs and benchmarks. |
Weakly-Supervised Methods for Suicide Risk Assessment: Role of Related Domains (2021.acl-short)
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| Challenge: | Among social media platforms, Reddit has emerged as the most promising one due to its anonymity and its focus on topic-based communities (subreddits) . a challenge for previous work on suicide risk assessment has been the small amount of labeled data. |
| Approach: | They propose to use social media to collect user data from r/SuicideWatch subreddit and annotate it with user-level suicide risk: no-risk, low-risk and high-risk. |
| Outcome: | The proposed model improves by using pseudo-labeling based on related issues around mental health (e.g., anxiety, depression) |
Efficient Shapley Values Estimation by Amortization for Text Classification (2023.acl-long)
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| Challenge: | Shapley Values are often estimated with a small number of stochastic model evaluations, but this can only be mitigated by aggregating thousands of model evaluation. |
| Approach: | They propose to combine a model with thousands of model evaluations to estimate Shapley Values without additional model evaluation. |
| Outcome: | The proposed model estimates Shapley Values accurately with up to 60 times speedup compared to traditional methods and does not suffer from stability issues as inference is deterministic. |
Hello Again! LLM-powered Personalized Agent for Long-term Dialogue (2025.naacl-long)
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| 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. |
Word-level Textual Adversarial Attacking as Combinatorial Optimization (2020.acl-main)
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| Challenge: | Existing word-level attack models are far from perfect because of unsuitable search space reduction methods and inefficient optimization algorithms. |
| Approach: | They propose a novel adversarial adversarialist model that incorporates word substitution and particle swarm optimization to solve two problems separately. |
| Outcome: | The proposed model achieves much higher success rates and crafts more high-quality adversarial examples as compared to baseline methods. |
Drivel-ology: Challenging LLMs with Interpreting Nonsense with Depth (2025.emnlp-main)
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| 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. |
Can You Follow Me? Testing Situational Understanding for ChatGPT (2023.emnlp-main)
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| Challenge: | Existing studies have identified certain SU limitations in non-chatbot Large Language models, but the extent and causes of these limitations are not well understood. |
| Approach: | They propose a synthetic environment for SU testing in chat-oriented models . they test models' ability to track and enumerate environment states . |
| Outcome: | The proposed environment allows for controlled and systematic testing of SU in chat-oriented models, and to better understand underlying causes for performance patterns. |
MARS-Bench: A Multi-turn Athletic Real-world Scenario Benchmark for Dialogue Evaluation (2025.findings-emnlp)
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Chenghao Yang, Yinbo Luo, Zhoufutu Wen, Qi Chu, Tao Gong, Longxiang Liu, Kaiyuan Zhang, Jianpeng Jiao, Ge Zhang, Wenhao Huang, Nenghai Yu
| Challenge: | Large Language Models (LLMs) have been widely adopted in real-world dialogue applications, but their robustness is criticized all along. |
| Approach: | They propose to use play-by-play text commentary to build a multi-turn athletic real-world scenario dialogue benchmark to evaluate three critical aspects of multi-turned conversations: ultra multi- turn, interactive multi-twist, and cross-turn tasks. |
| Outcome: | The proposed benchmarks outperform open-source LLMs on three critical aspects of multi-turn conversations: ultra multi-turned, interactive multi- turn, and cross-turn tasks. |
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
| 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
| 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. |
Improving Stability of Fine-Tuning Pretrained Language Models via Component-Wise Gradient Norm Clipping (2022.emnlp-main)
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| Challenge: | Recent studies have attributed such instability to the catastrophic forgetting problem in the top layers of PLMs. |
| Approach: | They propose a component-wise gradient norm clipping method to adjust convergence speed for different components to improve generalization performance, convergence speed, and training stability. |
| Outcome: | The proposed method achieves consistent improvements in terms of generalization performance, convergence speed, and training stability. |
Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study (2021.tacl-1)
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| Challenge: | Recent advances in open-domain question answering (ODQA) have led to human-level performance on many datasets. |
| Approach: | They provide a comprehensive and quantitative analysis about the difficulty of book QA . they compare the results of their research with extensive ODQA experiments . |
| Outcome: | The proposed model outperforms existing models on event-oriented questions on the NarrativeQA dataset. |
TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations (2021.emnlp-main)
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| Challenge: | Existing methods to extract entities and relations from unstructured texts are difficult to handle due to the overlapping triple problem. |
| Approach: | They propose a translation decoding schema for joint extraction of entities and relations from unstructured texts to form factual triples. |
| Outcome: | The proposed model can handle the overlapping triple problem, and is 2 times faster than the state-of-the-art models. |
Elevating Legal LLM Responses: Harnessing Trainable Logical Structures and Semantic Knowledge with Legal Reasoning (2025.naacl-long)
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| 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. |
Semantically Comprehensive Token Pruning in LVLMs via Maximizing Concept Coverage (2026.acl-long)
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| Challenge: | Existing visual token pruning methods leverage simple metrics derived from human experience, such as attention or similarity, to rank and select tokens within a highly entangled feature space. |
| Approach: | They propose a novel visual token pruning method that uses a concept-driven paradigm to quantify the Marginal Semantic Gain of each token's contribution to uncovered concepts. |
| Outcome: | The proposed method outperforms state-of-the-art methods in a concept-driven model while maintaining semantic completeness. |
Identifying Self-Disclosures of Use, Misuse and Addiction in Community-based Social Media Posts (2024.findings-naacl)
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Chenghao Yang, Tuhin Chakrabarty, Karli Hochstatter, Melissa Slavin, Nabila El-Bassel, Smaranda Muresan
| Challenge: | Experimental results show that identifying the phases of opioid use disorder is highly contextual and challenging. |
| Approach: | They analyze 2500 opioid-related posts from various subreddits labeled with six different phases of opioid use . they annotate span-level extractive explanations and critically evaluate state-of-the-art models in a supervised, few-shot, or zero-shot setting. |
| Outcome: | The proposed models improve classification accuracy and quality of the extracted explanations. |
LLM Knows Body Language, Too: Translating Speech Voices into Human Gestures (2024.acl-long)
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| Challenge: | despite advances in the generation of realistic human gestures, the process often includes unintended, meaningless, or non-realistic gestures. |
| Approach: | They propose a framework that leverages large language models to generate human gestures . the primary stage employs a transformer-based auto-encoder network to encode human gesture into discrete symbols . |
| Outcome: | The proposed framework has demonstrated state-of-the-art performance on public TED and TED-Expressive datasets. |
SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval (2024.findings-acl)
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Siwei Wu, Yizhi Li, Kang Zhu, Ge Zhang, Yiming Liang, Kaijing Ma, Chenghao Xiao, Haoran Zhang, Bohao Yang, Wenhu Chen, Wenhao Huang, Noura Al Moubayed, Jie Fu, Chenghua Lin
| Challenge: | Multi-modal information retrieval (MMIR) is a rapidly evolving field . current benchmarks for image-text pairings overlook the scientific domain . |
| Approach: | They develop a scientific domain-specific MMIR benchmark to evaluate image-text pairings using open-access research paper corpora. |
| Outcome: | The proposed benchmarks are based on 530K image-text pairs extracted from scientific documents with detailed captions. |
FinDABench: Benchmarking Financial Data Analysis Ability of Large Language Models (2025.coling-main)
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Shu Liu, Shangqing Zhao, Chenghao Jia, Xinlin Zhuang, Zhaoguang Long, Jie Zhou, Aimin Zhou, Man Lan, Yang Chong
| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of tasks, but their proficiency and reliability in the specialized domain of financial data analysis remain uncertain. |
| Approach: | FinDABench is a benchmark designed to evaluate the financial data analysis capabilities of Large Language Models (LLMs) it comprises 15,200 training instances and 8,900 test instances, all meticulously crafted by human experts. |
| Outcome: | FinDABench measures the financial data analysis capabilities of large language models (LLMs) across three dimensions: 1) Core Ability; 2) Analytical Ability; 3) Technical Ability. |
Crafting Customisable Characters with LLMs: A Persona-Driven Role-Playing Agent Framework (2025.findings-emnlp)
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Bohao Yang, Dong Liu, Chenghao Xiao, Kun Zhao, Chen Tang, Chao Li, Lin Yuan, Yang Guang, Chenghua Lin
| Challenge: | Large Language Models (LLMs) are capable of generating human-like text, but the potential for freely customisable characters remains underexplored. |
| Approach: | They propose a framework which employs Large Language Models to create freely customisable characters through personalised characteristic feature injection. |
| Outcome: | The proposed framework provides valuable insights for developing more accurate and customisable human simulacra. |
Beyond One-Size-Fits-All: Inversion Learning for Highly Effective NLG Evaluation Prompts (2026.tacl-1)
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| 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. |
When Hindsight is Not 20/20: Testing Limits on Reflective Thinking in Large Language Models (2024.findings-naacl)
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| Challenge: | Recent studies suggest that self-reflective prompting can significantly enhance the reasoning capabilities of Large Language Models (LLMs). |
| Approach: | They propose guidelines for when to implement self-reflection in Large Language Models. |
| Outcome: | The proposed approach improves the reasoning capabilities of Large Language Models under a more stringent evaluation setting, and reduces tendency toward majority voting. |
InterIDEAS: Philosophical Intertextuality via LLMs (2025.emnlp-main)
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| 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 . |
When Agents Look the Same: Quantifying Distillation-Induced Similarity in Tool-Use Behaviors (2026.acl-long)
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| Challenge: | Existing metrics fail to distinguish mandatory behaviors required for task success from non-mandatory patterns that reflect a model’s autonomous preferences. |
| Approach: | They propose to use response pattern similarity and action graph similarity to isolate non-mandatory behaviors from mandatory behaviors. |
| Outcome: | Evaluating 18 models from 8 providers on -Bench and 2-Bench against Claude Sonnet 4.5, the authors find that within-family model pairs score 5.9 pp higher in response pattern similarity and action graph similarity . |
Modeling Semantic Compositionality with Sememe Knowledge (P19-1)
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| Challenge: | Semantic compositionality (SC) is defined as the phenomenon that the meaning of a complex linguistic unit can be composed of the meanings of its constituents. |
| Approach: | They propose to incorporate sememes into SC models and employ them in learning multiword expressions. |
| Outcome: | The proposed models achieve significant performance boost compared to baseline methods without sememe knowledge. |
Fisher-Driven Adaptive Locating for Knowledge Editing in Large Language Models (2026.acl-long)
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| Challenge: | Existing methods for locating and editing static knowledge are costly and risk catastrophic forgetting or error. |
| Approach: | They propose a Fisher-driven adaptation-aware locating strategy that dynamically identifies which model components should be edited for a given knowledge update. |
| Outcome: | Experiments on standard benchmarks show that FiDAL improves editing effectiveness and knowledge preservation across multiple editing methods. |