Papers by Yifei Gao

EcoSafeRAG: Efficient Security through Context Analysis in Retrieval-Augmented Generation (2025.findings-emnlp)

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Ruobing Yao, Yifei Zhang, Shuang Song, Neng Gao, Chenyang Tu

Challenge:	Existing defense methods rely on internal knowledge of the model, which conflicts with the design concept of Retrieval-Augmented Generation (RAG).
Approach:	EcoSafeRAG uses sentence-level processing and bait-guided context diversity detection to identify malicious content .
Outcome:	EcoSafeRAG uses sentence-level processing and bait-guided context diversity detection to identify malicious content.

Personalized Microblog Sentiment Classification via Adversarial Cross-lingual Multi-task Learning (D18-1)

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Weichao Wang, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang

Responsible & Trustworthy NLP

Challenge:	Existing personalized microblog sentiment classification methods suffer from the insufficiency of discriminative tweets for personalization learning.
Approach:	They propose to use user-attention-based Convolutional Neural Networks to capture individuality and opinion bias in microblog posts and a novel adversarial cross-lingual learning framework to enrich the user post representation.
Outcome:	The proposed method outperforms state-of-the-art baseline algorithms with large margins on English and Chinese microblog datasets.

Confront Insider Threat: Precise Anomaly Detection in Behavior Logs Based on LLM Fine-Tuning (2025.coling-main)

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Shuang Song, Yifei Zhang, Neng Gao

Language Models

Challenge:	Current methods for insider threat detection suffer from low precision and information loss . a novel approach to detect insider threats is needed to improve accuracy .
Approach:	They propose a precise anomaly detection solution based on Large Language Model (LLM) fine-tuning . they represent user behavior in natural language and implement a threat tracing mechanism .
Outcome:	The proposed solution achieves an F1 score of 0.8941 on the CERT v6.2 dataset .

Reference Attack: A New Cross-Modal Jailbreaking Attack against Multimodal Large Language Models (2026.acl-long)

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Yulong Wang, Yifei Fu, Jiayi Gao

Responsible & Trustworthy NLP

Challenge:	Large Language Models (LLMs) have raised significant safety concerns about generated content, drawing attention from both academia and industry.
Approach:	They propose a reference-guided cross-modal jailbreak method that enhances existing prompt-to-image injection attacks by exploiting MLLMs’ semantic reconstruction capabilities.
Outcome:	The proposed method achieves an attack success rate of over 93% on leading MLLMs including ChatGPT, Gemini, Claude, and the widely used open-source LLaMA model.

Compensate Quantization Errors: Make Weights Hierarchical to Compensate Each Other (2024.findings-naacl)

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Yifei Gao, Jie Ou, Lei Wang, Yuting Xiao, Xiangzhiyuan Xiangzhiyuan, Ruiting Dai, Jun Cheng

Language Models

Challenge:	Emergent Large Language Models (LLMs) use extraordinary performance and powerful deduction capacity to discern from traditional language models.
Approach:	They propose a method that uses weights to compensate quantization error and learnable singular value incremental (LSI) LSI is a technique that helps weights compensate each other conditioned on activation.
Outcome:	The proposed method achieves state-of-the-art performance in diverse quantization settings, no matter in weight-only, weight-activation or extremely low bit scenarios.

Boundary Detection with BERT for Span-level Emotion Cause Analysis (2021.findings-acl)

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Xiangju Li, Wei Gao, Shi Feng, Yifei Zhang, Daling Wang

Language Models

Challenge:	Emotion cause analysis (ECA) is an emerging topic in natural language processing, which aims to identify the reasons behind a given emotion.
Approach:	They propose to detect the precise boundaries of text spans conveying accurate emotion causes from the given context by a sequence labeling and position identification problem.
Outcome:	The proposed methods outperform existing models on two benchmark datasets on the emotion cause analysis task.

Towards Comprehensive Patent Approval Predictions:Beyond Traditional Document Classification (2022.acl-long)

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Xiaochen Gao, Zhaoyi Hou, Yifei Ning, Kewen Zhao, Beilei He, Jingbo Shang, Vish Krishnan

Information Retrieval

Challenge:	a new framework for patent approval prediction is proposed to address this problem . novelty scores are based on comparing an application with millions of prior arts .
Approach:	They propose a framework that unifies the document classifier with handcrafted features, particularly time-dependent novelty scores.
Outcome:	The proposed framework unifies the document classifier with handcrafted features, particularly time-dependent novelty scores.

DialogConv: A Lightweight Fully Convolutional Network for Multi-view Response Selection (2022.emnlp-main)

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Yongkang Liu, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang

Natural Language Interfaces

Challenge:	Existing retrieval-based dialogue systems suffer from slow inference or huge number of parameters.
Approach:	They propose a lightweight fully convolutional architecture for response selection using convolution.
Outcome:	The proposed architecture extracts matching features of context and response from 3D views.

ParetoRAG: Leveraging Sentence-Context Attention for Robust and Efficient Retrieval-Augmented Generation (2025.findings-emnlp)

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Ruobing Yao, Yifei Zhang, Shuang Song, Yuhan Liu, Neng Gao, Chenyang Tu

Responsible & Trustworthy NLP

Challenge:	Retrieval-augmented generation systems face persistent challenges in retrieval inefficiency and the inability of LLMs to filter out irrelevant information.
Approach:	They propose an unsupervised framework that optimizes RAG systems through sentence-level refinement guided by the Pareto principle.
Outcome:	The proposed framework achieves dual improvements in retrieval precision and generation quality without additional training or API resources while using only 40% of the tokens compared to traditional approaches.

Raw Pointer Rewriting with LLMs for Translating C to Safer Rust (2026.findings-acl)

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Yifei Gao, Chengpeng Wang, Pengxiang Huang, Xuwei Liu, Mingwei Zheng, Xiangyu Zhang

Text Generation

Challenge:	C2Rust is a system programming language that enforces strict memory and type safety guarantees.
Approach:	They propose a raw pointer rewriting technique that lifts raw pointers in individual functions to appropriate Rust data structures.
Outcome:	The proposed technique eliminates 18.57% of local raw pointers and improves memory safety on 28 real-world C projects.

GUITester: Enabling GUI Agents for Exploratory Defect Discovery (2026.findings-acl)

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Yifei Gao, Jiang Wu, Xiaoyi Chen, Yifan Yang, Zhe Cui, Tianyi Ma, Jiaming Zhang, Jitao Sang

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.

WebSynthesis: World Model-Guided Monte Carlo Tree Search for Efficient WebAgent Trajectory Synthesis (2026.acl-long)

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Yifei Gao, Junhong Ye, Yifan Yang, Jiaqi Wang, Yi Zhang, Zhang Ruichen, Jitao Sang

Green & Sustainable NLP

Challenge:	Recent advances in large language models have enabled increasingly capable web agents . however, training such agents at scale still relies on high-quality interaction trajectories that are difficult to obtain at scale.
Approach:	They propose a framework for scalable trajectory synthesis that simulates state transitions without network dependencies and integrates Monte Carlo Tree Search to enable reversible exploration over the simulated state space.
Outcome:	Experiments on WebArena, WebVoyager, and Mind2Web-Online show that agents trained exclusively on synthesized trajectories outperform those trained on real-world data.

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

Language Models

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.

ES4R: Speech Encoding Based on Prepositive Affective Modeling for Empathetic Response Generation (2026.acl-long)

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Zhuoyue Gao, Xiaohui Wang, Xiaocui Yang, Wen Zhang, Daling Wang, Shi Feng, Yifei Zhang

Dialogue Systems & Conversational Agents

Challenge:	Existing speech-to-speech large language models rely on ASR transcription or use encoders to extract latent representations, weakening affective information and contextual coherence in multi-turn dialogues.
Approach:	They propose a framework for speech-based empathetic response generation that captures turn-level affective states and dialogue-level emotional dynamics.
Outcome:	The proposed framework outperforms baselines in automatic and human evaluations and remains robust across different Large Language Model (LLM) backbones.