Papers by Yifei Wu
From Charts to Code: A Hierarchical Benchmark for Multimodal Models (2026.acl-long)
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Jiahao Tang, Henry Hengyuan Zhao, Lijian Wu, Zijian Zhang, Yifei Tao, Dongxing Mao, Yang Wan, Jingru Tan, Min Zeng, Min Li, Alex Jinpeng Wang
| Challenge: | Chart2Code is a new benchmark for evaluating the natural language to chart code generation capabilities of large multimodal models. |
| Approach: | They introduce Chart2Code, a new benchmark for evaluating the natural language to chart code generation capabilities of large multimodal models. |
| Outcome: | The proposed benchmark is the first to scale task complexity while capturing diverse scenarios. |
From Detection to Understanding: Multi-Turn Reasoning for Video Misinformation Analysis (2026.acl-long)
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| Challenge: | Existing benchmarks focus on binary veracity judgments and do not evaluate process-level justifications for misinformation models. |
| Approach: | They propose a video misinformation analysis benchmark that assesses reasoning in video misinterpretation. |
| Outcome: | The proposed framework improves reasoning accuracy and explanation quality compared to existing models . it covers 12 fine-grained deception categories and progresses from perceptual attribution to intent and persuasion analysis. |
Enhancing Court View Generation with Knowledge Injection and Guidance (2024.lrec-main)
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| Challenge: | Existing methods of natural language generation (NLG) rely on the extensive parameters of pretrained language models (PLMs) but their effectiveness may be compromised by insufficient domain-specific knowledge. |
| Approach: | They propose a knowledge-injected prompt encoder to incorporate domain knowledge during the training stage, thereby reducing computational overhead. |
| Outcome: | The proposed approach outperforms established baselines on real-world data in responsivity of claims and in the ability to transfer domain knowledge. |
AnnaAgent: Dynamic Evolution Agent System with Multi-Session Memory for Realistic Seeker Simulation (2025.findings-acl)
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Ming Wang, Peidong Wang, Lin Wu, Xiaocui Yang, Daling Wang, Shi Feng, Yuxin Chen, Bixuan Wang, Yifei Zhang
| Challenge: | Existing models of seeker simulations are limited by the cost and ethical concerns of involving real seekers in mental health research. |
| Approach: | They propose an emotional and cognitive dynamic agent system equipped with tertiary memory to enable dynamic control of the simulator's configurations. |
| Outcome: | The proposed system achieves more realistic seeker simulation compared to baselines. |
GenPT: Beyond Self-Report for Reliable LLM Psychometrics via Generative Projective Testing (2026.acl-long)
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Ming Wang, Shuang Wu, Bixuan Wang, Lu Lin, Yuxin Chen, Xiaocui Yang, Daling Wang, Shi Feng, Yifei Zhang, Yufan Sun
| Challenge: | Large language models (LLMs) inherit contamination from training corpora, directional bias under social-desirability framing, and limited responsiveness to context beyond the item text. |
| Approach: | They propose a paradigm that reformulates TAT, Rorschach, and SCT with newly generated stimuli and organises assessment as a three-stage pipeline. |
| Outcome: | The proposed paradigm reformulates TAT, Rorschach, and SCT with newly generated stimuli and organises assessment as a three-stage pipeline. |
Towards Interactivity and Interpretability: A Rationale-based Legal Judgment Prediction Framework (2022.emnlp-main)
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| Challenge: | Existing models lack interpretability due to the neglect of rationale in the prediction process. |
| Approach: | They propose a rationale-based legal judgment prediction framework that follows the judge's real trial logic and provides good interactivity and interpretability. |
| Outcome: | The proposed framework provides good interactivity and interpretability which enables practical use. |
IPR: Intelligent Prompt Routing with User-Controlled Quality-Cost Trade-offs (2025.emnlp-industry)
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Aosong Feng, Balasubramaniam Srinivasan, Yun Zhou, Zhichao Xu, Kang Zhou, Sheng Guan, Yueyan Chen, Xian Wu, Ninad Kulkarni, Yi Zhang, Zhengyuan Shen, Dmitriy Bespalov, Soumya Smruti Mishra, Yifei Teng, Darren Yow-Bang Wang, Haibo Ding, Lin Lee Cheong
| Challenge: | Existing systems require users to manually select models or employ rigid routing rules that fail to capture the continuous spectrum of query complexity. |
| Approach: | They propose a quality-constrained intelligent prompt routing framework that automatically selects optimal models based on predicted response quality and user-specified tolerance levels. |
| Outcome: | The proposed framework achieves 43.9% cost reduction while maintaining quality parity with strongest model in the Claude family and processes requests with sub-150ms latency. |
UniLR: Unleashing the Power of LLMs on Multiple Legal Tasks with a Unified Legal Retriever (2025.acl-long)
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Ang Li, Yiquan Wu, Yifei Liu, Ming Cai, Lizhi Qing, Shihang Wang, Yangyang Kang, Chengyuan Liu, Fei Wu, Kun Kuang
| Challenge: | Existing retrieval methods are designed for general domains, struggling with legal knowledge, or tailored for specific legal tasks, unable to handle diverse legal knowledge types. |
| Approach: | They propose a novel retrieval method that integrates specialized knowledge into LLMs. |
| Outcome: | The proposed method can perform multiple legal retrieval tasks for LLMs. |
MUR: Momentum Uncertainty guided Reasoning for Large Language Models (2026.acl-long)
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Hang Yan, Fangzhi Xu, Rongman Xu, Yifei Li, Jian Zhang, Haoran Luo, Xiaobao Wu, Anh Tuan Luu, Haiteng Zhao, Qika Lin, Jun Liu
| Challenge: | Existing methods for optimizing reasoning quality are limited by overthinking. |
| Approach: | They propose a method that allocates thinking budgets to critical reasoning steps by tracking and aggregating step-wise uncertainty over time. |
| Outcome: | The proposed method reduces computation by over 45% on average while improving accuracy by 0.33–3.46%. |
Unleashing the Power of LLMs in Court View Generation by Stimulating Internal Knowledge and Incorporating External Knowledge (2024.findings-naacl)
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| Challenge: | Large Language Models (LLMs) have made remarkable strides in language generation, but they encounter difficulties in the knowledge-intensive legal domain. |
| Approach: | They propose to decompose court views into different parts, stimulate internal knowledge, and incorporate external information to unleash the power of LLMs in the task. |
| Outcome: | The proposed method generates more accurate and reliable court views on two real-world datasets LAIC2021 and CJO2022. |
Attack Named Entity Recognition by Entity Boundary Interference (2024.lrec-main)
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| Challenge: | Named Entity Recognition (NER) is a cornerstone natural language processing task . despite its robustness, studies on its robustity are lacking. |
| Approach: | They propose a one-word modification NER attack that strategically inserts a new boundary into the sentence and triggers the model to make a wrong recognition. |
| Outcome: | The proposed method is effective on English and Chinese models with 70%-90% success rate. |
Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration (2023.emnlp-main)
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Yiquan Wu, Siying Zhou, Yifei Liu, Weiming Lu, Xiaozhong Liu, Yating Zhang, Changlong Sun, Fei Wu, Kun Kuang
| Challenge: | Recent advances in deep learning have enabled a variety of techniques to be used to solve the LJP task. |
| Approach: | They propose a framework that leverages the strength of both LLMs and domain-specific models in the context of precedents. |
| Outcome: | The proposed framework leverages the strength of both LLM and domain models in the context of precedents. |
TOOLCAD: Exploring Tool-Using Large Language Models in Text-to-CAD Generation with Reinforcement Learning (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have shown remarkable advances in enabling language agents to tackle real-world tasks. |
| Approach: | They propose a tool-using agent-based CAD framework that automates text-to-CAD modeling . they propose an interactive CAD gym to roll out reasoning and tool-augmented interaction trajectories with the CAD engine . |
| Outcome: | The proposed framework can generalize across complex modeling tasks, supporting their open-source counterparts. |
Large Language Model Agents in Finance: A Survey Bridging Research, Practice, and Real-World Deployment (2025.findings-emnlp)
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Yifei Dong, Fengyi Wu, Kunlin Zhang, Yilong Dai, Sanjian Zhang, Wanghao Ye, Sihan Chen, Zhi-Qi Cheng
| Challenge: | a systematic review of large language models (LLMs) is conducted to better align their capabilities with real-world demands. |
| Approach: | They propose a functional taxonomy mapping financial domains to tasks, datasets, and institutional constraints. they catalog over 30 financial benchmarks and 20 representative models. |
| Outcome: | The proposed model frameworks are bridging financial practice and LLM research. |
CtrlNews: LLM-based Multi-Agent Controllable News Writing via Knowledge Gravitational Field (2025.findings-emnlp)
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| Challenge: | Current approaches to news writing rely on superficially retrieved information and oversimplified knowledge enumeration resulting in shallow, repetitive, and unordered outputs. |
| Approach: | They propose an LLM-based multi-agent controllable news writing framework called CtrlNews . they propose a fine-grained viewpoint control mechanism to regulate bias, emotion, and exaggeration attributes. |
| Outcome: | The proposed framework simulates expert questioning through automated role assignment and question generation followed by a three-layer hierarchical gravitational graph iteratively refined via expansion-reflection cycles. |
Chain-of-Quizzes: Pedagogy-inspired Example Selection in In-Context-Learning (2024.findings-acl)
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| Challenge: | In-context learning (ICL) is a powerful tool for enhancing large language models (LLMs) by mimicking the human learning process. |
| Approach: | They propose a Chain-of-Quizzes framework that uses LLMs to answer a quiz to sift 'good' examples, combine them iteratively with the increasing complexity, and utilize a final exam to gauge the combined example chains. |
| Outcome: | The proposed framework outperforms baseline models on diverse reasoning datasets and shows that it is scalable and can be used in future research. |
Unleashing Spatial Reasoning in Multimodal Large Language Models via Textual Representation Guided Reasoning (2026.acl-long)
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| Challenge: | Existing Multimodal Large Language Models struggle with 3D spatial reasoning as they fail to construct structured abstractions of the 3D environment depicted in video inputs. |
| Approach: | They propose a prompting method that induces MLLMs to generate 3D representations as reasoning traces for more accurate spatial question answering. |
| Outcome: | Extensive experiments on VSI-Bench and OST-Bech show that TRACE improves over prior prompting strategies across a diverse range of MLLM backbones. |
GUITester: Enabling GUI Agents for Exploratory Defect Discovery (2026.findings-acl)
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| Challenge: | Exploratory GUI testing is essential for software quality but suffers from high manual costs. |
| Approach: | They propose a framework that decouples navigation from verification via two modules . they propose 143 tasks and a GUITestBench benchmark that features 26 defects . |
| Outcome: | The proposed framework outperforms state-of-the-art benchmarks in 143 tasks and 26 defects. |
Latent Learningscape Guided In-context Learning (2024.findings-acl)
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| Challenge: | Existing methods to select demonstrations based on surface-level semantic similarities fall short of identifying the most fitting ones. |
| Approach: | They propose a method that characterizes latent learningscape features of demonstrations and uses them to create more effective prompts. |
| Outcome: | The proposed method outperforms leading models in arithmetic, commonsense, and symbolic reasoning tasks showing an average increase in scores by 7.4 percentage points. |
Adaptive Reinforcement Tuning Language Models as Hard Data Generators for Sentence Representation (2024.lrec-main)
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| Challenge: | Existing methods use contrastive learning (CL) to learn effective sentence representations, but require extensive human annotation. |
| Approach: | They propose a reinforcement learning approach for fine-tuning small-parameter LLMs to generate high-quality hard contrastive data without human feedback. |
| Outcome: | The proposed method achieves state-of-the-art on seven semantic text similarity tasks. |
Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations (2024.acl-long)
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| Challenge: | Existing methods for process-oriented math reward models rely on manual annotation. |
| Approach: | They propose a process-oriented math process reward model called Math-shepherd which assigns a reward score to each step of math problem solutions. |
| Outcome: | The proposed model breaks the bottleneck of manual supervision in two scenarios. |
From General Reward to Targeted Reward: Improving Open-ended Long-context Generation Models (2025.emnlp-main)
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| Challenge: | Current research on long-form context in Large Language Models (LLMs) focuses on understanding of long-contexts, but the open-ended Long Text Generation (Open-LTG) remains underexplored. |
| Approach: | They propose a method that uses data synthesis and a reward signal to enhance model performance. |
| Outcome: | The proposed method outperforms GPT-4-Turbo and improves performance by 20% on the Open-LTG task. |
GraphNarrator: Generating Textual Explanations for Graph Neural Networks (2025.acl-long)
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| Challenge: | Graph representation learning has garnered significant attention due to its broad applications in various domains, such as recommendation systems and social network analysis. |
| Approach: | They propose to use a generative language model to map input-output pairs to explanations reflecting the model’s decision-making process to generate a model that generates pseudo-labels that capture the model's decisions from saliency-based explanations. |
| Outcome: | Extensive experiments show that GraphNarrator produces human-preferred explanations that are faithful, concise, and human-like. |
OneRec-Think: In-Text Reasoning for Generative Recommendation (2026.acl-long)
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Zhanyu Liu, Shiyao Wang, Xingmei Wang, Rongzhou Zhang, Jiaxin Deng, Honghui Bao, Jinghao Zhang, Wuchao Li, PengFei Zheng, Xiangyu Wu, Yifei Hu, Qigen Hu, Xinchen Luo, Lejian Ren, Zhang Zixing, Qianqian Wang, Kuo Cai, Yunfan Wu, Hongtao Cheng, Zexuan Cheng, Lu Ren, Huanjie Wang, Yi Su, Ruiming Tang, Kun Gai, Guorui Zhou
| Challenge: | Existing generative models lack the capacity for explicit and controllable reasoning, a key advantage of LLMs. |
| Approach: | They propose a framework that integrates dialogue, reasoning, and personalized recommendation. |
| Outcome: | Experiments across public benchmarks show state-of-the-art performance. |
GoViG: Goal-Conditioned Visual Navigation Instruction Generation via Multimodal Reasoning (2026.findings-acl)
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Fengyi Wu, Yifei Dong, Yilong Dai, Guangyu Chen, Qifeng Wu, Huiting Huang, Hang Wang, Qi Dai, Alexander G Hauptmann, Zhi-Qi Cheng
| Challenge: | Current methods for instruction generation depend on privileged inputs such as semantic maps, landmark annotations, and panoramic views. |
| Approach: | They propose a task that generates coherent navigation instructions from egocentric visual observations. |
| Outcome: | The proposed task generates coherent navigation instructions from egocentric visual data . the proposed task improves performance over state-of-the-art methods in BLEU-4 and CIDEr scores . |
Few-shot Joint Multimodal Aspect-Sentiment Analysis Based on Generative Multimodal Prompt (2023.findings-acl)
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| Challenge: | Existing studies require massive labeled data to train models for multimodal data analysis. |
| Approach: | They propose a novel multimodal prompt model that captures specific aspect terms in a few-shot scenario. |
| Outcome: | The proposed model outperforms baselines on two MABSA-related tasks on a few-shot dataset. |