Papers by Yihang Li
Efficient Inference for Large Vision-Language Models: Bottlenecks, Techniques, and Prospects (2026.findings-acl)
Copied to clipboard
Jun Zhang, Yicheng Ji, Feiyang Ren, Yihang Li, Bowen Zeng, Zonghao Chen, Ke Chen, Lidan Shou, Gang Chen, Huan Li
| Challenge: | Large Vision-Language Models are hindered by a systemic efficiency barrier known as visual token dominance. |
| Approach: | They propose a systematic taxonomy of efficiency techniques structured around the inference lifecycle . they examine visual encoding, prefilling, and decoding to understand bottlenecks . |
| Outcome: | The proposed techniques reveal how upstream decisions dictate downstream bottlenecks . the proposed techniques include hybrid compression and modality-aware decoding . |
Safety is Not Only About Refusal: Reasoning-Enhanced Fine-tuning for Interpretable LLM Safety (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are vulnerable to jailbreak attacks that exploit weaknesses in traditional safety alignment. |
| Approach: | They propose a framework that trains models to engage in explicit safe reasoning before response . they propose RATIONAL, which allows models to reject harmful prompts while providing meaningful and context-aware responses. |
| Outcome: | The proposed framework fine-tunes models to reason about query intent, ethics, and potential harm. |
ChatSOP: An SOP-Guided MCTS Planning Framework for Controllable LLM Dialogue Agents (2025.acl-long)
Copied to clipboard
Zhigen Li, Jianxiang Peng, Yanmeng Wang, Yong Cao, Tianhao Shen, Minghui Zhang, Linxi Su, Shang Wu, Yihang Wu, YuQian Wang, Ye Wang, Wei Hu, Jianfeng Li, Shaojun Wang, Jing Xiao, Deyi Xiong
| Challenge: | Existing models that use Large Language Models (LLMs) show superior performance in various tasks, but lack of controllability leads to unfocused conversations or task failure. |
| Approach: | They propose a standard operating procedure (SOP) framework to regulate dialogue flow by integrating Chain of Thought reasoning and supervised fine-tuning for SOP prediction. |
| Outcome: | The proposed method achieves a 27.95% improvement in action accuracy compared to baseline models based on GPT-3.5 and also shows notable gains for open-source models. |
From Misleading Queries to Accurate Answers: A Three-Stage Fine-Tuning Method for LLMs (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods focus on correcting the output but overlook the ability of LLMs to detect and correct misleading content in the input itself. |
| Approach: | They propose a three-stage fine-tuning method that improves LLMs' ability to detect and correct misleading information in input queries. |
| Outcome: | The proposed method improves accuracy and factuality of LLM responses while also reducing hallucinations. |
Towards IP Intelligence: Benchmarking Large Language Models on Intellectual Property Knowledge and Practice (2026.findings-acl)
Copied to clipboard
Qiyao Wang, Guhong Chen, Hongbo Wang, Huaren Liu, Minghui Zhu, Zhifei Qin, Li Linwei, Yilin Yue, Shiqiang Wang, Jiayan Li, Wu Yihang, Ziqiang Liu, Longze Chen, Run Luo, Liyang Fan, Jiaming Li, Lei Zhang, Kan Xu, Hamid Alinejad-Rokny, Chengming Li, Shiwen Ni, Yuan Lin, Min Yang
| Challenge: | Existing datasets and benchmarks focus only on patents or cover limited aspects of the IP field, lacking alignment with real-world scenarios. |
| Approach: | They propose a bilingual IP task taxonomy and a large-scale bilingual benchmark to evaluate LLMs in real-world IP practice. |
| Outcome: | The proposed model achieves only 75.8% accuracy, indicating room for improvement . open-source IP and law-oriented models lag behind closed-source general-purpose models . |
Your Language Model May Think Too Rigidly: Achieving Reasoning Consistency with Symmetry-Enhanced Training (2025.findings-acl)
Copied to clipboard
Yihang Yao, Zhepeng Cen, Miao Li, William Han, Yuyou Zhang, Emerson Liu, Zuxin Liu, Chuang Gan, Ding Zhao
| Challenge: | Large Language Models (LLMs) have demonstrated strong reasoning capabilities across various tasks. |
| Approach: | They propose a data-centric approach that enhances LLMs’ awareness of symmetry in query variations and propose syMmetry-ENhanceD (MEND) data augmentation. |
| Outcome: | Extensive experiments on logical and arithmetic reasoning tasks show that the proposed approach improves model robustness at the knowledge extraction stage through query augmentation. |
VISA: An Ambiguous Subtitles Dataset for Visual Scene-aware Machine Translation (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing multimodal machine translation datasets contain images and video captions or general subtitles which rarely contain linguistic ambiguity. |
| Approach: | They propose a dataset that consists of Japanese-English parallel sentence pairs and corresponding video clips. |
| Outcome: | The proposed dataset is challenging for the latest MMT system and can facilitate MMT research. |
CriticLean: Critic-Guided Reinforcement Learning for Mathematical Formalization (2026.acl-long)
Copied to clipboard
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
| 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. |
MELD-ST: An Emotion-aware Speech Translation Dataset (2024.findings-acl)
Copied to clipboard
Sirou Chen, Sakiko Yahata, Shuichiro Shimizu, Zhengdong Yang, Yihang Li, Chenhui Chu, Sadao Kurohashi
| Challenge: | Emotion plays a crucial role in human conversation. |
| Approach: | They present a MELD-ST dataset for the emotion-aware speech translation task . they show that fine-tuning with emotion labels can enhance translation performance . |
| Outcome: | The proposed dataset shows that fine tuning with emotion labels can improve translation performance in some settings. |
Learning to Edit Knowledge via Instruction-based Chain-of-Thought Prompting (2026.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge editing methods focus on structured fact triples, overlooking diverse unstructured forms of factual information. |
| Approach: | They propose a method that allows LLMs to edit knowledge via **Chain of Thoughts** reasoning. |
| Outcome: | The proposed method achieves strong generalization across six diverse knowledge editing scenarios with a single round of training on three open-source language models. |
Video-Helpful Multimodal Machine Translation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing multimodal machine translation datasets contain images and video captions or instructional video subtitles, which rarely contain linguistic ambiguity. |
| Approach: | They propose an MMT dataset that contains ambiguous subtitles and a video-helpful evaluation set. |
| Outcome: | The proposed model performs significantly better than existing models on ambiguous subtitles dataset . it is based on a training set and video-helpful evaluation set . |
DFAMS: Dynamic-flow guided Federated Alignment based Multi-prototype Search (2026.acl-long)
Copied to clipboard
Zhibang Yang, Xinke Jiang, Rihong Qiu, Ruiqing Li, Yihang Zhang, Yue Fang, Yongxin Xu, Hongxin Ding, Xu Chu, Junfeng Zhao, Yasha Wang
| Challenge: | Existing methods for ambiguous queries struggle to retrieve high-quality documents . DFAMS outperforms advanced FR methods by 14.37% in knowledge classification accuracy . |
| Approach: | They propose a framework that leverages dynamic information flow to identify latent query intents and construct semantically aligned knowledge partitions for accurate retrieval across heterogeneous sources. |
| Outcome: | The proposed framework outperforms existing methods in classification accuracy and retrieval recall tests. |
Self-Adjust Softmax (2025.emnlp-main)
Copied to clipboard
Chuanyang Zheng, Yihang Gao, Guoxuan Chen, Han Shi, Jing Xiong, Xiaozhe Ren, Chao Huang, Zhenguo Li, Yu Li
| Challenge: | Usually, tokens with larger attention scores are important for the final prediction. |
| Approach: | They propose to modify softmax(z) to z softmax and its normalized variant to improve the Transformer attention mechanism by making minor adjustments to the softmax function. |
| Outcome: | The proposed model provides enhanced gradient properties compared to the vanilla softmax function. |
Rethinking Meeting Effectiveness: A Benchmark and Framework for Temporal Fine-grained Automatic Meeting Effectiveness Evaluation (2026.acl-long)
Copied to clipboard
| Challenge: | Current methods for evaluating meeting effectiveness rely on post-hoc surveys that yield a single coarse-grained score for an entire meeting. |
| Approach: | They propose a new paradigm for evaluating meeting effectiveness centered on novel criteria and temporal fine-grained approach . they introduce a meta-evaluation dataset containing 2,459 human-annotated segments from 130 AMI Corpus meetings. |
| Outcome: | The proposed framework can be used to evaluate meeting effectiveness across different meeting types and from business scenarios to unstructured discussions. |
Asclepius: A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models (2025.acl-long)
Copied to clipboard
Jie Liu, Wenxuan Wang, Su Yihang, Jingyuan Huang, Yudi Zhang, Cheng-Yi Li, Wenting Chen, Xiaohan Xing, Kao-Jung Chang, Linlin Shen, Michael R. Lyu
| Challenge: | Medical Multi-Modal Large Language Models (Med-MLLMs) are a promising new form of artificial general intelligence due to their ability to tackle complex tasks. |
| Approach: | They propose a new benchmark that comprehensively assesses medical multi-modal large language models in terms of distinct medical specialties and different diagnostic capacities. |
| Outcome: | The proposed model covers 15 medical specialties and different diagnostic capacities, and excludes overlap with existing VQA dataset. |
DAPE V2: Process Attention Score as Feature Map for Length Extrapolation (2025.acl-long)
Copied to clipboard
Chuanyang Zheng, Yihang Gao, Han Shi, Jing Xiong, Jiankai Sun, Jingyao Li, Minbin Huang, Xiaozhe Ren, Michael Ng, Xin Jiang, Zhenguo Li, Yu Li
| Challenge: | Extensive experiments demonstrate that treating attention as a feature map and applying convolution as . a processing method significantly enhances Transformer performance. |
| Approach: | They propose to use the convolution operator to mimic the processing methods in computer vision to treat attention as a feature map and apply it to neighboring attention scores across different heads. |
| Outcome: | The proposed model can be adapted to various attention-related models and achieves high performance. |