Papers by Yifei Yu
Resource-Limited Joint Multimodal Sentiment Reasoning and Classification via Chain-of-Thought Enhancement and Distillation (2026.findings-acl)
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| Challenge: | Current approaches for Multimodal Sentiment Analysis (MSA) rely on parameter-heavy LLMs for classification, overlooking multimodal sentiment reasoning generation in resource-limited environments. |
| Approach: | They propose a multimodal sentiment reasoning distillation model that employs a teacher-assistant-student paradigm to address deployment constraints in resource-limited environments. |
| Outcome: | The proposed model performs well on a resource-limited JMSRC task with only 3B parameters and shows generalization and interpretability. |
Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents (2025.findings-emnlp)
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Long Li, Weiwen Xu, Jiayan Guo, Ruochen Zhao, Xingxuan Li, Yuqian Yuan, Boqiang Zhang, Yuming Jiang, Yifei Xin, Ronghao Dang, Yu Rong, Deli Zhao, Tian Feng, Lidong Bing
| Challenge: | Existing methods for idea generation either trivially prompt LLMs or expose LLM to extensive literature without indicating useful information. |
| Approach: | They propose a chain-of-ideas agent that organizes literature in a chains structure . they propose evaluating idea-generation methods from different perspectives . |
| Outcome: | The proposed agent outperforms existing methods and matches human quality in idea generation. |
Locomo-Plus: Beyond-Factual Cognitive Memory Evaluation Framework for LLM Agents (2026.acl-long)
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| Challenge: | Existing benchmarks and evaluation protocols focus on surface-level factual recall. |
| Approach: | They propose a benchmark for assessing cognitive memory under cue–trigger semantic disconnect. |
| Outcome: | The proposed framework reveals failures not captured by existing benchmarks. |
AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving (2025.findings-emnlp)
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Kangan Qian, Sicong Jiang, Yang Zhong, Ziang Luo, Zilin Huang, Tianze Zhu, Kun Jiang, Mengmeng Yang, Zheng Fu, Jinyu Miao, Yining Shi, He Zhe Lim, Li Liu, Tianbao Zhou, Hongyi Wang, Huang Yu, Yifei Hu, Guang Li, Guang Chen, Hao Ye, Lijun Sun, Diange Yang
| Challenge: | Vision-Language Models struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning. |
| Approach: | AgentThink integrates Chain-of-Thought reasoning with dynamic, agent-style tool invocation for autonomous driving tasks. |
| Outcome: | Experiments on the DriveLMM-o1 benchmark show AgentThink significantly boosts overall reasoning scores by 53.91% and enhances answer accuracy by 33.54% . |
Towards Provably Secure Generative AI: Reliable Consensus Sampling (2026.findings-acl)
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Yu Cui, Hang Fu, Sicheng Pan, Zhuoyu Sun, Yifei Liu, Yuhong Nie, Bo Ran, Baohan Huang, Xufeng Zhang, Haibin Zhang, Cong Zuo, Licheng Wang
| Challenge: | Existing research on generative AI security is driven by mutually reinforcing attack and defense methodologies grounded in empirical experience. |
| Approach: | They propose a new algorithm that uses a random sampling algorithm to control risk. |
| Outcome: | The proposed algorithm improves robustness and utility while maintaining latency comparable to existing algorithms. |
Neural Parameter Search for Slimmer Fine-Tuned Models and Better Transfer (2025.acl-long)
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Guodong Du, Zitao Fang, Jing Li, Junlin Li, Runhua Jiang, Shuyang Yu, Yifei Guo, Yangneng Chen, Sim Kuan Goh, Ho-Kin Tang, Daojing He, Honghai Liu, Min Zhang
| Challenge: | Foundational models and their checkpoints have advanced deep learning, boosting performance across applications. |
| Approach: | They propose a method for pruning fine-tuned models by calculating differences between them and original model. |
| Outcome: | The proposed method can improve performance across vision, NLP, and multi-modal benchmarks. |
Experience Retrieval-Augmentation with Electronic Health Records Enables Accurate Discharge QA (2026.acl-long)
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| Challenge: | Existing methods to improve the reliability of Large Language Models (LLMs) in clinical applications require factual knowledge from open-ended datasets and clinical case-based knowledge to provide context grounded in real-world patient experiences. |
| Approach: | They propose a retrieval-augmented generation framework based on the electronic health record to offer contextual information from other patients’ discharge reports. |
| Outcome: | The proposed framework outperforms a text-based ranker in a clinical QA dataset with 1,280 discharge-related questions . |
Unveiling Factual Recall Behaviors of Large Language Models through Knowledge Neurons (2024.emnlp-main)
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| Challenge: | Recent advances in Large Language Models have underscored their exceptional reasoning prowess with natural language understanding across a broad spectrum of tasks. |
| Approach: | They examine whether Large Language Models actively recall or retrieve their internal repositories of factual knowledge when faced with reasoning tasks. |
| Outcome: | The proposed model improves reasoning performance while suppressing it leads to notable degradation. |
VortexPIA: Indirect Prompt Injection Attack against LLMs for Efficient Extraction of User Privacy (2026.findings-eacl)
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| Challenge: | Large language models (LLMs) have been widely deployed in Conversational AIs . however, the methods proposed in the study rely on a white-box setting . |
| Approach: | They propose an indirect prompt injection attack that induces privacy extraction in LLMs . they use token-efficient data containing false memories to inject LLM data . |
| Outcome: | The proposed method outperforms baselines and achieves state-of-the-art performance. |
RolePlot: A Systematic Framework for Evaluating and Enhancing the Plot-Progression Capabilities of Role-Playing Agents (2025.acl-long)
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| Challenge: | Existing research has focused on role-playing agents’ ability to portray specified characters, but their ability to advance the plot requires substantial improvements to deliver more engaging interaction. |
| Approach: | They propose a role-playing framework to evaluate and enhance the plot-progression capabilities of role-players. |
| Outcome: | The proposed framework improves RPAs’ ability to time plot developments and yields a significant increase in conversation turns and sustained higher arousal levels. |
Sequential-NIAH: A Needle-In-A-Haystack Benchmark for Extracting Sequential Needles from Long Contexts (2025.emnlp-main)
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Yifei Yu, Qian-Wen Zhang, Lingfeng Qiao, Di Yin, Fang Li, Jie Wang, Chen Zeng Xi, Suncong Zheng, Xiaolong Liang, Xing Sun
| Challenge: | Recent models have extended Corresponding Author. context lengths to millions of tokens while maintaining reasoning and comprehension capabilities. |
| Approach: | They propose a benchmark to evaluate the ability of large language models to extract sequential information items from long contexts. |
| Outcome: | The proposed model achieves maximum accuracy of 63.50% on six well-known LLMs. |
UCFE: A User-Centric Financial Expertise Benchmark for Large Language Models (2025.findings-naacl)
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Yuzhe Yang, Yifei Zhang, Yan Hu, Yilin Guo, Ruoli Gan, Yueru He, Mingcong Lei, Xiao Zhang, Haining Wang, Qianqian Xie, Jimin Huang, Honghai Yu, Benyou Wang
| Challenge: | Recent advances in large language models (LLMs) have expanded their potential applications in finance. |
| Approach: | They propose a framework to evaluate the ability of large language models to handle financial tasks using human expert evaluations and task-specific interactions. |
| Outcome: | The proposed framework evaluates the ability of large language models to handle complex financial tasks and combines human expert evaluations with dynamic, task-specific interactions to simulate the complexities of evolving financial scenarios. |
To See a World in a Spark of Neuron: Disentangling Multi-Task Interference for Training-Free Model Merging (2025.emnlp-main)
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Zitao Fang, Guodong Du, Shuyang Yu, Yifei Guo, Yiwei Zhang, Yiyao Cao, Jing Li, Ho-Kin Tang, Sim Kuan Goh
| Challenge: | Existing approaches to model merging ignore the fundamental roles of neurons, connectivity and activation. |
| Approach: | They propose a framework that relies on neuronal mechanisms to mitigate task interference . they decomposed task-specific representations into two complementary subspaces . their results offer new insights into mitigating task interference and improving knowledge fusion . |
| Outcome: | The proposed framework reduces task interference within neurons and improves knowledge fusion. |
Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation (2025.findings-naacl)
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| Challenge: | Existing methods to enhance credibility and verifiability of large language models (LLMs) mainly focus on passage-level or paragraph-level references or citations, which fall short in verifikatability. |
| Approach: | They propose a method that provides sentence-level citations in LLM-generated responses. |
| Outcome: | The proposed method achieves 90% accuracy in long-form question-answering 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. |
Pretraining Language Models with Text-Attributed Heterogeneous Graphs (2023.findings-emnlp)
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| Challenge: | Existing pretraining tasks for Language Models (LMs) focus on learning the textual information of each entity and overlook the crucial aspect of capturing topological connections among entities in TAHGs. |
| Approach: | They propose a topology-aware pretraining task that explicitly considers the topological and heterogeneous information in TAHGs by optimizing an LM and an auxiliary heterogenous graph neural network. |
| Outcome: | The proposed framework explicitly considers the topological and heterogeneous information in TAHGs. |
AutoSDT: Scaling Data-Driven Discovery Tasks Toward Open Co-Scientists (2025.emnlp-main)
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Yifei Li, Hanane Nour Moussa, Ziru Chen, Shijie Chen, Botao Yu, Mingyi Xue, Benjamin Burns, Tzu-Yao Chiu, Vishal Dey, Zitong Lu, Chen Wei, Qianheng Zhang, Tianyu Zhang, Song Gao, Xuhui Huang, Xia Ning, Nesreen K. Ahmed, Ali Payani, Huan Sun
| Challenge: | AutoSDT-5K is the only automatically collected and the largest open dataset for data-driven scientific discovery. |
| Approach: | They propose an automatic pipeline that collects high-quality coding tasks in real-world data-driven discovery workflows. |
| Outcome: | The proposed pipeline synthesizes accurate tasks and tasks from a dataset of 5,404 tasks covering four scientific disciplines and 756 Python packages. |
Uncertainty Unveiled: Can Exposure to More In-context Examples Mitigate Uncertainty for Large Language Models? (2025.findings-acl)
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| Challenge: | Recent advances in handling long sequences have unlocked new possibilities for long-context in-contact learning (ICL). |
| Approach: | They investigate how increased examples influence predictive uncertainty . they quantify uncertainty across different “shot” configurations and focus on EU . |
| Outcome: | The proposed model reduces uncertainty in simple and complex tasks by injecting task-specific knowledge. |
Knowledge Fusion By Evolving Weights of Language Models (2024.findings-acl)
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| Challenge: | Experimental results on mainstream language models show that Evolver outperforms previous state-of-the-art models by large margins due to the high training costs of large language models. |
| Approach: | They propose a method to integrate multiple models from diverse training scenarios into a unified model. |
| Outcome: | The proposed method outperforms state-of-the-art models on mainstream language models by large margins. |
Unsolvable Problem Detection: Robust Understanding Evaluation for Large Multimodal Models (2025.acl-long)
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Atsuyuki Miyai, Jingkang Yang, Jingyang Zhang, Yifei Ming, Qing Yu, Go Irie, Yixuan Li, Hai Helen Li, Ziwei Liu, Kiyoharu Aizawa
| Challenge: | Multiple-choice question answering (MCQA) is widely used to assess the understanding capability of Large Multimodal Models (LMMs). |
| Approach: | They propose a task to evaluate the robust understanding capability of Large Multimodal Models (LMMs) they introduce a benchmark to assess performance across various ability dimensions . |
| Outcome: | The proposed model can withhold answers when encountering unsolvable problems of MCQA, proving it understands the answer. |
BERT-BC: A Unified Alignment and Interaction Model over Hierarchical BERT for Response Selection (2024.lrec-main)
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| Challenge: | Recent performance boosting for dialogue response selection task achieved by Cross-Encoder based models is limited and the learned models have poor generalization capability in realistic scenarios. |
| Approach: | They propose a model that combines the representation-based Bi-Encoder and interaction-based Cross-Encoding to achieve better semantic representation. |
| Outcome: | The proposed model can achieve state-of-the-art performance on three benchmark datasets for multi-turn response selection. |