Papers by Chenghao Li
ReCode: Robustness Evaluation of Code Generation Models (2023.acl-long)
Copied to clipboard
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 the Rigour of Scientific Writing: Criteria, Analysis, and Insights (2024.findings-emnlp)
Copied to clipboard
| Challenge: | despite its importance, little work exists on modelling rigour in scientific writing . despite widespread use of term, scientific literature lacks definition of rigor . |
| Approach: | They propose a framework to automatically identify and define rigour criteria and assess their relevance in scientific writing. |
| Outcome: | The proposed framework can be tailored to the evaluation of scientific rigour for different areas. |
Revealing and Enhancing Core Visual Regions: Harnessing Internal Attention Dynamics for Hallucination Mitigation in LVLMs (2026.findings-acl)
Copied to clipboard
| 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. |
ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning (2025.emnlp-main)
Copied to clipboard
Yu Sun, Xingyu Qian, Weiwen Xu, Hao Zhang, Chenghao Xiao, Long Li, Deli Zhao, Wenbing Huang, Tingyang Xu, Qifeng Bai, Yu Rong
| Challenge: | Existing medical reasoning datasets are limited in scale and typically rely on incomplete data. |
| Approach: | They propose to use ReasonMed to train medical reasoning models using a multi-agent generation, verification, and refinement pipeline. |
| Outcome: | The largest medical reasoning dataset to date surpasses the prior best sub-10B models by 4.17% and even exceeds LLaMA3.1-70B on PubMedQA by 4.60%. |
AutoScraper: A Progressive Understanding Web Agent for Web Scraper Generation (2024.emnlp-main)
Copied to clipboard
Wenhao Huang, Zhouhong Gu, Chenghao Peng, Jiaqing Liang, Zhixu Li, Yanghua Xiao, Liqian Wen, Zulong Chen
| Challenge: | Existing methods for web scraping suffer from limited adaptability and scalability when faced with a new website. |
| Approach: | They propose a framework that generates web scrapers with large language models and a new executability metric to measure the performance of web scraper generation tasks. |
| Outcome: | The proposed framework can handle diverse web environments more efficiently. |
Hello Again! LLM-powered Personalized Agent for Long-term Dialogue (2025.naacl-long)
Copied to clipboard
| 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. |
RIGOURATE: Quantifying Scientific Exaggeration with Evidence-Aligned Claim Evaluation (2026.findings-acl)
Copied to clipboard
| Challenge: | Scientific rigour tends to be sidelined in favour of bold statements, leading authors to overstate claims beyond what their results support. |
| Approach: | They propose a multimodal framework that retrieves supporting evidence from a paper and assigns each claim an overstatement score. |
| Outcome: | The proposed framework retrieves supporting evidence from ICLR and NeurIPS papers and assigns each claim an overstatement score. |
LSDC: An Efficient and Effective Large-Scale Data Compression Method for Supervised Fine-tuning of Large Language Models (2025.findings-naacl)
Copied to clipboard
Zhaoguang Long, Yuhao Zhou, Shangqing Zhao, Yupei Ren, Li Cai, Chenghao Jia, Zhe Chen, Zhe Fang, Yuxiang Song, Man Lan
| Challenge: | Large Language Models (LLMs) are expanding in scale and size, increasing computational costs . large-scale data compression techniques can reduce the size of training datasets while maintaining data integrity. |
| Approach: | They propose a large-scale data compression method to reduce the size of training data . they use a bifurcated quantization strategy to maximize the diversity of samples . |
| Outcome: | The proposed method significantly reduces the size of training data while maximizing the submodular gain. |
Beyond Quantity: Trajectory Diversity Scaling for Code Agents (2026.findings-acl)
Copied to clipboard
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)
Copied to clipboard
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. |
TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations (2021.emnlp-main)
Copied to clipboard
| 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. |
Length is a Curse and a Blessing for Document-level Semantics (2023.emnlp-main)
Copied to clipboard
| Challenge: | In recent years, contrastive learning (CL) has been extensively utilized to recover sentence and document-level encoding capability from pre-trained language models. |
| Approach: | They propose a document-based contrastive learning framework that is length-agnostic self-reference based on document length. |
| Outcome: | The proposed framework achieves state-of-the-art on the standard information retrieval benchmark. |
Semantically Comprehensive Token Pruning in LVLMs via Maximizing Concept Coverage (2026.acl-long)
Copied to clipboard
| 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. |
Interpret and Improve In-Context Learning via the Lens of Input-Label Mappings (2025.acl-long)
Copied to clipboard
| Challenge: | Large language models excel at downstream NLP tasks through in-context learning . however, the internal mechanisms behind ICL remain under-explored . |
| Approach: | They propose a PC patching approach to identify modules where input-label mappings function . they observe and verify that key heads utilize input-labeled mappings to generate target labels for new queries. |
| Outcome: | The proposed approach detects modules where input-label mappings function . it also detects that key heads use the mappings to generate labels for new queries . |
SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval (2024.findings-acl)
Copied to clipboard
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. |
Crafting Customisable Characters with LLMs: A Persona-Driven Role-Playing Agent Framework (2025.findings-emnlp)
Copied to clipboard
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. |
PEMT: Multi-Task Correlation Guided Mixture-of-Experts Enables Parameter-Efficient Transfer Learning (2024.findings-acl)
Copied to clipboard
| Challenge: | Parameter-efficient fine-tuning (PEFT) is an effective method for adapting pre-trained language models to various tasks efficiently. |
| Approach: | They propose a parameter-efficient fine-tuning framework that captures transferable knowledge as a weighted combination of adapters trained on source tasks. |
| Outcome: | The proposed method yields stable improvements over full fine-tuning and knowledge transferring methods on a broad range of tasks over 17 datasets. |
When Hindsight is Not 20/20: Testing Limits on Reflective Thinking in Large Language Models (2024.findings-naacl)
Copied to clipboard
| 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. |
HyperRouter: Towards Efficient Training and Inference of Sparse Mixture of Experts (2023.emnlp-main)
Copied to clipboard
Truong Do, Le Khiem, Quang Pham, TrungTin Nguyen, Thanh-Nam Doan, Binh Nguyen, Chenghao Liu, Savitha Ramasamy, Xiaoli Li, Steven Hoi
| Challenge: | Recent studies suggest that fixing the routers can achieve competitive performance by alleviating the collapsing problem, where all experts eventually learn similar representations. |
| Approach: | They propose a method that dynamically generates router parameters through a fixed hypernetwork and trainable embeddings to achieve a balance between training the routers and freezing them to learn an improved routing policy. |
| Outcome: | Experiments on a wide range of tasks show that the proposed method performs better than existing methods. |