Papers by Yun Zhang

Better Process Supervision with Bi-directional Rewarding Signals (2025.findings-acl)

Challenge:	Spreadsheets are characterized by their extensive two-dimensional grids, flexible layouts, and varied formatting options, which pose significant challenges for large language models (LLMs).
Approach:	They propose a structural-anchor-based compression, inverse index translation, and data-format-aware aggregation module to compress spreadsheets effectively.
Outcome:	The proposed method outperforms the existing model in GPT4 and achieves a state-of-the-art 78.9% F1 score.

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Wenxiang Chen, Wei He, Zhiheng Xi, Honglin Guo, Boyang Hong, Jiazheng Zhang, Nijun Li, Tao Gui, Yun Li, Qi Zhang, Xuanjing Huang

Reasoning

Challenge:	Existing processes that reward for each step are one-directional and lack a mechanism to model the distance to the final target.
Approach:	They propose a process supervision model that evaluates the correctness of previous steps and the probability of future success.
Outcome:	The proposed model outperforms existing supervision models like ORM and PRM on reasoning tasks and improves solution re-design.

Personality-Guided Code Generation Using Large Language Models (2025.acl-long)

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Yaoqi Guo, Zhenpeng Chen, Jie M. Zhang, Yang Liu, Yun Ma

Do LLMs Catch Their Own Mistakes? A Comprehensive Benchmark for Reflective Tool Use LLMs (2026.findings-acl)

Challenge:	Existing studies have shown that personality-guided code generation improves software development outcomes when individuals are assigned tasks that match their personality types.
Approach:	They evaluate how emulating personality traits appropriate to the coding tasks affects LLM performance by using seven widely adopted LLMs.
Outcome:	The proposed approach improves pass rates in 23 out of 28 LLM-dataset combinations, while emulating personality traits can be easily integrated with other prompting strategies to further boost performance.

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Zheyuan Liu, Liqiang Xiao, Yang Li, Hyokun Yun, Lihong Li, Chao Zhang, Meng Jiang

Dialogue Systems & Conversational Agents

Challenge:	Existing benchmarks primarily evaluate planning and execution success, overlooking the self-reflective dimension of tool use.
Approach:	They propose a benchmark to assess LLMs’ self-reflective reasoning in tool-augmented multi-turn dialogues.
Outcome:	The proposed benchmark covers 10 domains with 88 distinct APIs and 968 annotated dialogues, systematically injecting diverse error types arising from both user and assistant behavior.

Zero-Shot Cross-Lingual Transfer of Neural Machine Translation with Multilingual Pretrained Encoders (2021.emnlp-main)

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Guanhua Chen, Shuming Ma, Yun Chen, Li Dong, Dongdong Zhang, Jia Pan, Wenping Wang, Furu Wei

Multilinguality

Challenge:	Existing work on improving cross-lingual transferability of NMT model is under-explored.
Approach:	They propose a model that leverages a multilingual pretrained encoder to improve cross-lingual transferability.
Outcome:	The proposed model outperforms mBART and m2m-100 on a zero-shot cross-lingual transfer task.

Tag-Instruct: Controlled Instruction Complexity Enhancement through Structure-based Augmentation (2025.findings-acl)

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He Zhu, Zhiwen Ruan, Junyou Su, Xingwei He, Yun Chen, Wenjia Zhang, Guanhua Chen

Challenge:	High-quality instruction data is crucial for developing large language models (LLMs), yet existing approaches struggle to effectively control instruction complexity.
Approach:	They propose a framework that compresses instructions into a compact tag space and enhances complexity through RL-guided tag expansion.
Outcome:	The proposed framework outperforms existing methods in the evaluation of instruction complexity augmentation and semantic compression of text into a compact tag space.

ACBQ: Adaptive Cross-Block Quantization of Large Language Models (2026.acl-long)

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Hailing Wang, Jianglin Lu, Yitian Zhang, Huimin Zeng, Yun Fu

LLM4Vis: Explainable Visualization Recommendation using ChatGPT (2023.emnlp-industry)

Challenge:	Existing methods for post-training quantization struggle to support weight–activation joint quantization and extreme low-bit weight quantization.
Approach:	They propose a framework that addresses weight–activation joint quantization and extreme weight quantization.
Outcome:	The proposed framework achieves superior performance under both W4A4 and highly aggressive W2 settings while incurring negligible additional computational overhead.

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Lei Wang, Songheng Zhang, Yun Wang, Ee-Peng Lim, Yong Wang

ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch (2026.acl-long)

Challenge:	Existing methods to perform visualization recommendation require a large corpus of dataset-visualization pairs for training and lack natural explanations for their results.
Approach:	They propose a new method that uses a ChatGPT-based prompting approach to perform visualization recommendation and return human-like explanations using very few demonstration examples.
Outcome:	The proposed method outperforms or performs similarly to supervised learning models like Random Forest, Decision Tree, and MLP, in both few-shot and zero-shot settings.

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Zheng Liu, Honglin Lin, Xiaoyang Wang, Xin Gao, Yu Li, Mengzhang Cai, Yun Zhu, Zhanping Zhong, Qizhi Pei, Zhuoshi Pan, Xiaoran Shang, Conghui He, Bin Cui, Wentao Zhang, Lijun Wu

Breaking Agents: Compromising Autonomous LLM Agents Through Malfunction Amplification (2025.emnlp-main)

Challenge:	Existing open-source vision language models lack high-quality training data for chart reasoning . current models are simplistic and repetitive, while associated QA pairs are prone to hallucinations .
Approach:	They propose a framework to synthesize complex charts and reliable reasoning data from scratch.
Outcome:	Experimental results show that ChartVerse-8B surpasses existing models in QA and difficulty . lack of high-quality training data hampers development of open-source models .

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Boyang Zhang, Yicong Tan, Yun Shen, Ahmed Salem, Michael Backes, Savvas Zannettou, Yang Zhang

Bridging Local Details and Global Context in Text-Attributed Graphs (2024.emnlp-main)

Challenge:	Recent advances in large language models (LLMs) have increased the vulnerability of LLMs, but they can cause more severe damage than standalone systems if compromised.
Approach:	They propose a new type of attack that induces malfunctions by misleading the agent into executing repetitive or irrelevant actions.
Outcome:	The proposed attacks induce failure rates exceeding 80% in multiple scenarios, highlighting the substantial risks associated with this vulnerability.

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Yaoke Wang, Yun Zhu, Wenqiao Zhang, Yueting Zhuang, Liyunfei Liyunfei, Siliang Tang

Green & Sustainable NLP

Challenge:	Existing studies focus on combining different information levels but overlook interconnections, i.e., contextual textual information among nodes.
Approach:	They propose a framework that bridges local and global perspectives by leveraging contextual textual information.
Outcome:	The proposed framework achieves state-of-the-art performance while reducing tokens significantly.

Aligning Large Language Models with Implicit Preferences from User-Generated Content (2025.acl-long)

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Zhaoxuan Tan, Zheng Li, Tianyi Liu, Haodong Wang, Hyokun Yun, Ming Zeng, Pei Chen, Zhihan Zhang, Yifan Gao, Ruijie Wang, Priyanka Nigam, Bing Yin, Meng Jiang

AutoMixAlign: Adaptive Data Mixing for Multi-Task Preference Optimization in LLMs (2025.acl-long)

Challenge:	Existing preference learning methods rely heavily on curated data from humans or advanced LLMs, which is costly and difficult to scale.
Approach:	They propose a framework that leverages implicit preferences in unlabeled user-generated content to generate preference data.
Outcome:	The proposed framework transforms user-generated content into user queries and generates responses from the policy model.

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Nicholas E. Corrado, Julian Katz-Samuels, Adithya M Devraj, Hyokun Yun, Chao Zhang, Yi Xu, Yi Pan, Bing Yin, Trishul Chilimbi

Meta-Reflection: A Feedback-Free Reflection Learning Framework (2025.acl-long)

Challenge:	Existing approaches to align large language models rely on large ablation studies, heuristics, or human intuition to produce models with strong performance across tasks.
Approach:	They propose an algorithm that mixes datasets during LLM training to balance performance across multiple tasks.
Outcome:	The proposed algorithm outperforms existing methods on multitask alignment setups and achieves convergence rate of O(1/T) in the convex case.

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Yaoke Wang, Yun Zhu, XintongBao XintongBao, Wenqiao Zhang, Suyang Dai, Kehan Chen, Wenqiang Li, Gang Huang, Siliang Tang, Yueting Zhuang

Information Retrieval

Challenge:	Existing approaches to improve large language models' ability to understand and reason are limited by external feedback.
Approach:	They propose a feedback-free reflection mechanism that requires only a single inference pass without external feedback.
Outcome:	The proposed method is based on an industrial e-commerce benchmark and public datasets.

RefuteBench: Evaluating Refuting Instruction-Following for Large Language Models (2024.findings-acl)

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Jianhao Yan, Yun Luo, Yue Zhang

Towards Unified Multimodal Large Language Models: A survey (2026.findings-acl)

Challenge:	The application scope of large language models (LLMs) is expanding . however, evaluating whether models can respond to user feedback has not been thoroughly analyzed.
Approach:	They propose a benchmark to assess whether large language models can respond to refuting feedback and adhere to user demands throughout the conversation.
Outcome:	The proposed benchmark covers tasks such as question answering, machine translation, and email writing.

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Xu Ma, Yitian Zhang, Yun Fu

Revealing the Seen, Imagining the Beyond: A Survey of Image-Grounded Chain-of-Thought Reasoning in Multimodal LLMs (2026.acl-long)

Challenge:	unified multimodal large language models (MLLMs) are emerging but lack a systematic framework to connect them and situate current trends within a broader landscape.
Approach:	They present a systematic review of unified Multimodal Large Language Models . they outline the foundational concepts and prerequisites for understanding them .
Outcome:	The present review provides a systematic and systematic overview of unified MLLMs . it discusses persistent challenges and identify promising directions for future research .

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Qihua Dong, Yitian Zhang, Huimin Zeng, Yizhou Wang, Jianglin Lu, Kuo Yang, Yun Fu

FAITH: Factuality Alignment through Integrating Trustworthiness and Honestness (2026.findings-acl)

Challenge:	Recent advances in Multimodal Large Language Models (MLLMs) have shifted visual reasoning from tool-calling to end-to-end perceptionreasoning.
Approach:	They synthesize the emerging paradigm of Image-Grounded Chain-of-Thought (IG-CoT) they propose a method-centric taxonomy covering prompting, supervised fine-tuning, and reinforcement learning .
Outcome:	The proposed model is based on a method-centric taxonomy and benchmarks.

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Xiaoning Dong, Chengyan Wu, Yajie Wen, Yu Chen, Yun Xue, Zhang Jing, Wei Xu, Bolei Ma

Information Retrieval

Challenge:	Existing approaches to correct factually inaccurate outputs are lacking the semantic richness needed to properly understand its internal states of trustworthiness and honesty.
Approach:	They propose a framework for factuality alignment that integrates natural-language uncertainty signals with external knowledge and computes confidence scores and semantic entropy from LLM outputs.
Outcome:	Extensive experiments on four knowledge-intensive benchmarks show that FAITH improves the factual accuracy and truthfulness of Large Language Models (LLMs).

Matching Distributions between Model and Data: Cross-domain Knowledge Distillation for Unsupervised Domain Adaptation (2021.acl-long)

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Bo Zhang, Xiaoming Zhang, Yun Liu, Lei Cheng, Zhoujun Li

Composite Backdoor Attacks Against Large Language Models (2024.findings-naacl)

Challenge:	Existing methods require to learn to adapt the target model by exploiting the source data and sharing the network architecture across domains.
Approach:	They propose a framework that allows to transfer the knowledge of source domain to the unlabeled target domain without using source data.
Outcome:	The proposed framework matches distributions between a trained source model and a set of target data and achieves superior performance on cross-domain text classification.

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Hai Huang, Zhengyu Zhao, Michael Backes, Yun Shen, Yang Zhang

ToM: Leveraging Tree-oriented MapReduce for Long-Context Reasoning in Large Language Models (2025.emnlp-main)

Challenge:	Large language models (LLMs) have demonstrated superior performance on various tasks, but untrustworthy third-party LLMs may covertly introduce vulnerabilities for downstream tasks.
Approach:	They propose a composite backdoor attack that scatters multiple trigger keys in different prompt components.
Outcome:	The proposed attack achieves 100% Attack Success Rate (ASR) with a False Triggered Rate (FTR) below 2.06% and negligible model accuracy degradation.

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Jiani Guo, Zuchao Li, Jie Wu, Qianren Wang, Yun Li, Lefei Zhang, Hai Zhao, Yujiu Yang

Multi-Granularity Semantic Revision for Large Language Model Distillation (2026.acl-long)

Challenge:	Experimental results show ToM outperforms existing divide-and-conquer frameworks . RAG relies on similarity-based rankings to retrieve and reason over chunks based on logical coherence .
Approach:	They propose a Tree-oriented MapReduce framework for long-context reasoning . it leverages the hierarchical structure of long documents by constructing a DocTree .
Outcome:	Experimental results show that ToM outperforms existing divide-and-conquer frameworks and RAGs . the proposed framework improves logical coherence and long-context reasoning on 70B+ LLMs compared to existing approaches .

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Xiaoyu Liu, Yun Zhang, Wei Li, Simiao Li, Xudong Huang, Hanting Chen, Yehui Tang, Jie Hu, Zhiwei Xiong, Yunhe Wang

Neural Search Space in Gboard Decoder (2024.emnlp-industry)

Challenge:	Existing methods for generating large language models rely on student-generated outputs, which introduce generation errors and misguide the distillation process.
Approach:	They propose a multi-granularity semantic revision method for LLM distillation that corrects errors using teacher-generated tokens and re-generates the sequence to minimize errors.
Outcome:	The proposed method reduces errors and misguides distillation on student models and improves consistency between teacher and student outputs.

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Yanxiang Zhang, Yuanbo Zhang, Haicheng Sun, Yun Wang, Gary Sivek, Shumin Zhai

M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis (2025.emnlp-main)

Challenge:	Gboard decoder uses context, a lexicon and language models to provide a user-friendly keyboard.
Approach:	They propose a Neural Search Space which replaces an N-gram LM with a neural network LM and dynamically constructs the search space during decoding.
Outcome:	The proposed system improves the quality of the decoded keyboards on various locales with acceptable latency increases.

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ChengYan Wu, Bolei Ma, Yihong Liu, Zheyu Zhang, Ningyuan Deng, Yanshu Li, Baolan Chen, Yi Zhang, Yun Xue, Barbara Plank

Multilinguality

Challenge:	Existing studies focus on English-centric aspects of sentiment analysis, limiting scope for multilingual evaluation and research.
Approach:	They propose to use a multilingual dataset to analyze aspects with associated sentiment elements in text.
Outcome:	The proposed dataset is the most extensive multilingual parallel dataset for ABSA to date.

Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition (2020.emnlp-main)

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Yun He, Ziwei Zhu, Yin Zhang, Qin Chen, James Caverlee

ESPnet-ST-v2: Multipurpose Spoken Language Translation Toolkit (2023.acl-demo)

Challenge:	Existing methods to augment pre-trained language models with disease knowledge are lacking.
Approach:	They propose a method to augment BERT-like pre-trained language models with disease knowledge.
Outcome:	The proposed method improves on a suite of BERT models over three tasks.

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Brian Yan, Jiatong Shi, Yun Tang, Hirofumi Inaguma, Yifan Peng, Siddharth Dalmia, Peter Polák, Patrick Fernandes, Dan Berrebbi, Tomoki Hayashi, Xiaohui Zhang, Zhaoheng Ni, Moto Hira, Soumi Maiti, Juan Pino, Shinji Watanabe

GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models (2025.findings-emnlp)

Challenge:	ESPnet-ST-v2 is a revamp of the open-source spoken language translation toolkit . it supports offline speech-to-text translation (ST), simultaneous speech- to-text (SST), and offline speech to-speech (S2ST)
Approach:	They propose to revamp the open-source ESPnet-ST toolkit to support offline speech-to-text translation, simultaneous speech- to-text and offline speech to-speech translation.
Outcome:	The updated version of ESPnet-ST supports offline speech-to-text translation (ST), simultaneous speech- to-text (SST), and offline speech to-speech translation (S2ST).

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Jialin Chen, Houyu Zhang, Seongjun Yun, Alejandro Mottini, Rex Ying, Xiang Song, Vassilis N. Ioannidis, Zheng Li, Qingjun Cui

IPR: Intelligent Prompt Routing with User-Controlled Quality-Cost Trade-offs (2025.emnlp-industry)

Challenge:	Existing graph RAGs decouple retrieval and reasoning processes, preventing adaptability . existing graph Raggings depend heavily on ground-truth entities, which are often unavailable in open-domain settings.
Approach:	They propose a graph retriever that is trained end-to-end with large-scale graphs . structure and semantic features are encoded via soft tokens and the verbalized graph .
Outcome:	The proposed approach improves the performance of large-scale graph retrieval models by grounding it with external knowledge.

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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

Low-Resource NLP

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.

M³GQA: A Multi-Entity Multi-Hop Multi-Setting Graph Question Answering Benchmark (2025.acl-long)

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Boci Peng, Yongchao Liu, Xiaohe Bo, Jiaxin Guo, Yun Zhu, Xuanbo Fan, Chuntao Hong, Yan Zhang

Exploiting Sentiment and Common Sense for Zero-shot Stance Detection (2022.coling-1)

Challenge:	GraphRAG systems have achieved remarkable progress in enhancing performance and reliability of large language models.
Approach:	They propose a GraphRAG benchmark focusing on multi-entity queries with six settings for comprehensive evaluation.
Outcome:	The proposed method can construct diverse data with semantically correct ground-truth reasoning paths.

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Yun Luo, Zihan Liu, Yuefeng Shi, Stan Z. Li, Yue Zhang

Sentiment Analysis

Challenge:	Existing stance detection models use sentiment and commonsense knowledge to classify stance toward documents and topics . obtaining rich annotated data in stance detector is time-consuming and laborintensive .
Approach:	They propose to use sentiment and commonsense knowledge to boost transferability of stance detection model by using sentiment and similar knowledge.
Outcome:	The proposed model outperforms the state-of-the-art methods on the zero-shot and few-shot benchmark datasets.

PILOT: Planning via Internalized Latent Optimization Trajectories for Large Language Models (2026.acl-long)

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Haoyu Zheng, Yun Zhu, Yuqian Yuan, Bo Yuan, Wenqiao Zhang, Siliang Tang, Jun Xiao

Keys to Robust Edits: From Theoretical Insights to Practical Advances (2025.acl-long)

Challenge:	Large Language Models lack the capacity to formulate global strategies due to latency and availability constraints.
Approach:	They propose a framework to internalize the strategic oversight of large models into intrinsic Latent Guidance by synthesizing a query-conditioned Latent Guide.
Outcome:	The proposed framework outperforms strong baselines on mathematical and coding benchmarks with negligible inference latency.

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Jianhao Yan, Futing Wang, Yun Luo, Yafu Li, Yue Zhang

Robustness in NLP

Challenge:	Existing methods for modifying parametric memory are prone to inaccuracies due to conflicting or outdated information.
Approach:	They propose a plug-and-play module that disentangles editing keys from native model representations and dynamically adjusts keys via contrastive learning to achieve robustness-specificity balance.
Outcome:	The proposed method improves over robustness tests by up to 66.4% while maintaining the success rate unaffected.

KeFVP: Knowledge-enhanced Financial Volatility Prediction (2023.findings-emnlp)

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Hao Niu, Yun Xiong, Xiaosu Wang, Wenjing Yu, Yao Zhang, Weizu Yang

VFA: Empowering Multilingual MLLMs via Vision-Free Adaptation (2026.acl-long)

Challenge:	Current studies ignore the role of financial metrics knowledge in earnings calls and little consideration is given to integrating text and price information.
Approach:	They propose to integrate financial metrics knowledge into text comprehension by knowledge-enhanced adaptive pre-training and effectively incorporating text and price information by introducing a conditional time series prediction module.
Outcome:	The proposed method outperforms state-of-the-art methods on three real-world datasets and is effective and reliable.

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Yixia Li, Yaqing Shi, Zhiwen Ruan, Dongdong Zhang, Lingjie Jiang, Shaohan Huang, Yun Chen, Guanhua Chen, Furu Wei

Enhancing Argument Structure Extraction with Efficient Leverage of Contextual Information (2023.findings-emnlp)

Challenge:	Multimodal large language models have advanced rapidly, yet most remain English-centric . scaling multilingual multimodal instruction tuning is limited by the scarcity and high cost of non-English image–text supervision.
Approach:	They propose a framework that decouples multilingual language enhancement from visual alignment by composing complementary task vectors over a shared LLM backbone.
Outcome:	The proposed framework achieves competitive performance with a fully multimodally trained model using less than 2% of the text data.

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Yun Luo, Zhen Yang, Fandong Meng, Yingjie Li, Jie Zhou, Yue Zhang

From Words to Pixels: A Comprehensive Survey on Large Language Models in Visual Segmentation (2026.acl-long)

Challenge:	Argument structure extraction (ASE) aims to identify the discourse structure of arguments within documents.
Approach:	They propose an Efficient Context-aware ASE model that fully exploits contextual information by augmenting modeling capacity and augmenting training data.
Outcome:	The proposed model can extract argumentative discourse structure from documents and reduce reliance on specific words or less informative sentences.

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Yizhou Wang, Mang Tik Chiu, Lingzhi Zhang, Xuan Shen, Sohrab Amirghodsi, Yun Fu

CoRE: A Fine-Grained Code Reasoning Benchmark Beyond Output Prediction (2026.findings-acl)

Challenge:	Visual segmentation with instruction has been a challenging task for many years . large language models and large multimodal models have spurred a new wave of research .
Approach:	They review recent works in LLM-based visual segmentation and analyze their architectural innovations, training strategies, and benchmark performance.
Outcome:	The present study reviews the most recent works in LLM-driven visual segmentation . it identifies key challenges and promising future directions .

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Jun Gao, Yun Peng, Qian Qiao, Changhai Zhou, Yuhua Zhou, Shiyang Zhang, Shichao Weng, Zhenchang Xing, Xiaoxue Ren

Composition-based Heterogeneous Graph Multi-channel Attention Network for Multi-aspect Multi-sentiment Classification (2022.coling-1)

Challenge:	Existing code reasoning benchmarks evaluate final output correctness under a single implementation.
Approach:	They propose a Code Reasoning benchmark that evaluates code reasoning through implementation invariance and process transparency.
Outcome:	The proposed benchmarks lack implementation invariance and process transparency . they observe superficial execution where models arrive at correct outputs without reasoning .

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Hao Niu, Yun Xiong, Jian Gao, Zhongchen Miao, Xiaosu Wang, Hongrun Ren, Yao Zhang, Yangyong Zhu

Sentiment Analysis

Challenge:	Existing methods for Aspect-based sentiment analysis (ABSA) focus on aspect terms with the same sentiment polarity . current methods focus on sentences with only one aspect term or multiple aspect terms .
Approach:	They propose a novel method to model inter-aspect relationships and aspect-context relationships simultaneously using a heterogeneous graph.
Outcome:	The proposed method can predict sentiments towards the given aspect term in a sentence . it can provide more detailed predictions compared with sentence-level sentiment analysis.

Adaptive Structure Induction for Aspect-based Sentiment Analysis with Spectral Perspective (2023.findings-emnlp)

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Hao Niu, Yun Xiong, Xiaosu Wang, Wenjing Yu, Yao Zhang, Zhonglei Guo

Sentiment Analysis

Challenge:	incorporating structure information can enhance the performance of aspect-based sentiment analysis.
Approach:	They propose to use pre-trained language models to induct latent structures from a spectrum perspective.
Outcome:	The proposed model shortens Aspects-sentiment Distance and improves structure induction ability.

FinSafetyBench: Evaluating LLM Safety in Real-World Financial Scenarios (2026.findings-acl)

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Yutao Hou, Yihan Jiang, Yuhan Xie, Jian Yang, Liwen Zhang, Hailiang Huang, Guanhua Chen, Yun Chen

LLMs for Now, Fine-Tuning for Later: An Ensemble Approach to Data Drift in Domain-Specific Tasks (2026.acl-srw)

Challenge:	Existing large language models (LLMs) are prone to misuse and misinformation, posing serious compliance risks.
Approach:	They propose a bilingual red-teaming benchmark to test an LLM’s refusal of requests that violate financial compliance.
Outcome:	The proposed benchmark is based on real-world financial crime cases and ethical violations and includes 14 subcategories covering financial crimes and ethical breaches.

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Yuxuan Lu, Bingsheng Yao, Shao Zhang, Yisi Sang, Yun Wang, Hansu Gu, Peng Zhang, Tun Lu, Toby Jia-Jun Li, Dakuo Wang

XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners (2024.naacl-long)

Challenge:	Deploying machine learning models in domain-specific scenarios is challenged by data drift and the scarcity of expert annotations.
Approach:	They propose a system that combines an LLM, an AL-assisted compact model and an automatic switch module to assist the active learning process.
Outcome:	The proposed system achieves 96–98% switch accuracy and outperforms both models used alone.

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Yun Luo, Zhen Yang, Fandong Meng, Yingjie Li, Fang Guo, Qinglin Qi, Jie Zhou, Yue Zhang

Cautious Next Token Prediction (2025.findings-acl)

Challenge:	Existing methods for active learning rely on model uncertainty or disagreement to pick unlabeled data, leading to over-confidence in superficial patterns and lack of exploration.
Approach:	They propose to use a bi-directional encoder and a uni-directional decoder to generate and score an explanation for low-resource text classification.
Outcome:	The proposed model improves on 9 strong baselines on six datasets and can generate explanations for its predictions.

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Yizhou Wang, Lingzhi Zhang, Yue Bai, Mang Tik Chiu, Zhengmian Hu, Mingyuan Zhang, Qihua Dong, Yu Yin, Sohrab Amirghodsi, Yun Fu

Challenge:	Existing methods for decoding autoregressive models are temperature scaling and nucleus sampling to balance diversity and coherence.
Approach:	They propose a training-free decoding strategy that uses a model with a low perplexity score to select the trial with the lowest perplexities as the most probable and reliable path.
Outcome:	The proposed approach outperforms existing standard decoding strategies consistently by a clear margin.

MeMoTune: A Measure and Moment-Driven Fine-Tuning Framework for Quantized Large Language Models (2025.findings-acl)

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Yun Zhang, Xue Geng, Lizi Liao, Jintong Sun, Minghe Yu, Ge Yu

GRNFormer: A Biologically-Guided Framework for Integrating Gene Regulatory Networks into RNA Foundation Models (2025.findings-acl)

Challenge:	Existing methods combine quantization with parameter-efficient fine-tuning but fail to meet practical performance requirements.
Approach:	They propose a measure and moment approach to optimize objective function for superior fine-tuning results by scaling the update process through a gradient.
Outcome:	The proposed framework outperforms state-of-the-art methods on tasks like text generation, summarization, and understanding.

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Mufan Qiu, Xinyu Hu, Fengwei Zhan, Sukwon Yun, Jie Peng, Ruichen Zhang, Bhavya Kailkhura, Jiekun Yang, Tianlong Chen

Structured Data in NLP

Challenge:	Foundation models for single-cell RNA sequencing ignore biological prior knowledge encoded in gene regulatory relationships and fail to leverage multi-omics signals.
Approach:	They propose a framework that integrates multi-scale gene regulatory networks into RNA foundation model training.
Outcome:	The proposed framework improves on state-of-the-art models on three downstream tasks . it integrates multi-scale gene regulatory networks (GRNs) from multi-omics data into training .

CLMTracing: Black-box User-level Watermarking for Code Language Model Tracing (2025.emnlp-main)

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Boyu Zhang, Ping He, Tianyu Du, Xuhong Zhang, Lei Yun, Kingsum Chow, Jianwei Yin

Robustness in NLP

Challenge:	Open-source code language models (code LMs) are a growing threat for intellectual property protection.
Approach:	They propose a black-box code LM watermarking framework that uses rule-based watermarks and utility-preserving injection method for user-level model tracing.
Outcome:	The proposed framework shows that it performs well across multiple state-of-the-art code LMs and is harmless compared to existing baselines.

RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models (2024.emnlp-main)

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Peng Xia, Kangyu Zhu, Haoran Li, Hongtu Zhu, Yun Li, Gang Li, Linjun Zhang, Huaxiu Yao

Learning to Ideate for Machine Learning Engineering Agents (2026.eacl-short)

Challenge:	Existing medical large vision language models often generate inaccurate and irrelevant answers that do not align with established medical facts.
Approach:	They propose a strategy for controlling factuality risk through calibrated selection of the number of retrieved contexts and a preference dataset to fine-tune the model.
Outcome:	The proposed model achieves an average improvement of 20.8% on three medical VQA datasets.

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Yunxiang Zhang, Kang Zhou, Zhichao Xu, Kiran Ramnath, Yun Zhou, Sangmin Woo, Haibo Ding, Lin Lee Cheong

Challenge:	Existing machine learning engineering (MLE) agents struggle to iteratively optimize their implemented algorithms for effectiveness.
Approach:	They propose a framework that separates ideation from implementation that allows an implementation agent to request strategic help from a dedicated Ideator.
Outcome:	The proposed framework outperforms implementation-only agent baselines on MLE-Bench and can be trained with reinforcement learning to generate more effective ideas.

PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain Knowledge (2020.emnlp-main)

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Yun He, Zhuoer Wang, Yin Zhang, Ruihong Huang, James Caverlee

WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learning (2025.emnlp-main)

Challenge:	Paraphrase identification requires specialized domain knowledge to perform . state-of-the-art neural models and non-expert human annotators have poor performance on PARADE .
Approach:	They propose a benchmark dataset called PARADE for paraphrase identification that requires specialized domain knowledge.
Outcome:	The proposed dataset shows state-of-the-art models and non-expert human annotators have poor performance on PARADE.

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Zhepei Wei, Wenlin Yao, Yao Liu, Weizhi Zhang, Qin Lu, Liang Qiu, Changlong Yu, Puyang Xu, Chao Zhang, Bing Yin, Hyokun Yun, Lihong Li

Challenge:	Existing work on reinforcement learning has focused on single-turn tasks such as solving math problems.
Approach:	They propose a framework that learns directly from online interactions by asynchronously generating diverse trajectories, guided by binary rewards depending on task success.
Outcome:	Experiments on the WebArena-Lite benchmark show that the framework outperforms state-of-the-art methods and strong proprietary models.

LLM-Powered Test Case Generation for Detecting Bugs in Plausible Programs (2025.acl-long)

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Kaibo Liu, Zhenpeng Chen, Yiyang Liu, Jie M. Zhang, Mark Harman, Yudong Han, Yun Ma, Yihong Dong, Ge Li, Gang Huang

Knowledge-Centric Hallucination Detection (2024.emnlp-main)

Challenge:	TrickCatcher generates test cases that pass existing tests yet contain bugs . a recent study found that tricky bugs are not detected by test suites .
Approach:	They propose an LLM-powered approach to generating test cases for uncovering bugs in plausible programs . they use a PUT and specification to generate program variants, an input generator and an Llm to construct test inputs .
Outcome:	The proposed approach achieves recall, precision, and F1 scores that are 1.80, 2.65, and 1.66 . trickCatcher generates program variants based on the program under test and its specification .

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Xiangkun Hu, Dongyu Ru, Lin Qiu, Qipeng Guo, Tianhang Zhang, Yang Xu, Yun Luo, Pengfei Liu, Yue Zhang, Zheng Zhang

Reasoning

Challenge:	Large Language Models (LLMs) have shown impressive capabilities but a tendency to hallucinate.
Approach:	They propose a framework that introduces claim-triplets to represent claims in LLM responses and evaluates them against a reference.
Outcome:	The proposed framework outperforms prior methods by 18.2 to 27.2 points on a benchmark spanning various NLP tasks and annotated 11k claim-triplets from 2.1k responses by seven LLMs.

DORM: Preference Data Weights Optimization for Reward Modeling in LLM Alignment (2025.findings-emnlp)

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Rongzhi Zhang, Chenwei Zhang, Xinyang Zhang, Liang Qiu, Haoming Jiang, Yuchen Zhuang, Qingru Zhang, Hyokun Yun, Xian Li, Bing Yin, Tuo Zhao, Chao Zhang

Challenge:	Existing approaches to align large language models with human preferences are noisy and varying in importance of preference samples.
Approach:	a new method enhances reward modeling by learning to dynamically weigh preference data.
Outcome:	a new method improves the performance of large language models with human preferences . it initializes data importance and iteratively refines them to maximize validation performance.

Astra: Activation-Space Tail-Eigenvector Low-Rank Adaptation of Large Language Models (2026.findings-acl)

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Kainan Liu, Yong Zhang, Ning Cheng, Yun Zhu, Yanmeng Wang, Shaojun Wang, Jing Xiao

Chinese Idiom Paraphrasing (2023.tacl-1)

Challenge:	Existing methods for fine-tuning pre-trained models are limited due to suboptimal activation subspaces.
Approach:	They propose a method that leverages tail eigenvectors of model output activations to construct low-rank adapters.
Outcome:	The proposed method outperforms existing methods across 16 benchmarks and surpasses full fine-tuning in certain scenarios.

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Jipeng Qiang, Yang Li, Chaowei Zhang, Yun Li, Yi Zhu, Yunhao Yuan, Xindong Wu

MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)

Challenge:	Chinese idioms are hard to understand by children and non-native speakers due to their non-compositionality and metaphorical meaning.
Approach:	They propose a task to rephrase idiom-containing sentences to non-idiomatic ones under the premise of preserving the original sentence’s meaning.
Outcome:	The proposed method has better performance than baselines based on the established dataset.

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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie

Mere Contrastive Learning for Cross-Domain Sentiment Analysis (2022.coling-1)

Challenge:	Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models.
Approach:	They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts.
Outcome:	The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings .

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Yun Luo, Fang Guo, Zihan Liu, Yue Zhang

Distorted or Fabricated? A Survey on Hallucination in Video LLMs (2026.findings-acl)

Challenge:	Existing approaches to cross-domain sentiment analysis are labor-intensive and time-consuming.
Approach:	They propose a modified contrastive objective with in-batch negative samples to allow sentence representations from the same class to be pushed closer while those from the different classes become further apart in the latent space.
Outcome:	The proposed model can achieve state-of-the-art in cross-domain and multi-domain sentiment analysis tasks while transferring knowledge learned in the source domain to the target domain.

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Yiyang Huang, Yitian Zhang, Yizhou Wang, Mingyuan Zhang, Liang Shi, Huimin Zeng, Yun Fu

The Art of (Mis)alignment: How Fine-Tuning Methods Effectively Misalign and Realign LLMs in Post-Training (2026.findings-acl)

Challenge:	Despite significant advances in video-language modeling, hallucinations remain a persistent challenge in video large language models.
Approach:	They present a systematic taxonomy that categorizes hallucinations into two core types: dynamic distortion and content fabrication.
Outcome:	The proposed taxonomy categorizes hallucinations into two core types: dynamic distortion and content fabrication.

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Rui Zhang, Hongwei Li, Yun Shen, Xinyue Shen, Wenbo Jiang, Guowen Xu, Yang Liu, Michael Backes, Yang Zhang

OpenResearcher: Unleashing AI for Accelerated Scientific Research (2024.emnlp-demo)

Challenge:	Misaligned large language models can magnify harm by exploiting them to undermine safety . et al., 2022b; Bai e.t., 2023): misalignment, realignment and model-specific resistance are important .
Approach:	They evaluate four methods to identify a mechanism asymmetry between attack and defense . they find that ORPO is most effective for misalignment, but DPO excels in realignment .
Outcome:	The proposed methods show a mechanism asymmetry between attack and defense . the proposed methods excel in realignment, but at the expense of model utility .

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Yuxiang Zheng, Shichao Sun, Lin Qiu, Dongyu Ru, Cheng Jiayang, Xuefeng Li, Jifan Lin, Binjie Wang, Yun Luo, Renjie Pan, Yang Xu, Qingkai Min, Zizhao Zhang, Yiwen Wang, Wenjie Li, Pengfei Liu

Information Retrieval

Challenge:	Global scientific publications are growing annually by about 4%-5% (Pinedo et al., 2024).
Approach:	They introduce an AI-assisted platform that answers diverse questions from researchers using Retrieval-Augmented Generation (RAG) they develop various tools to understand queries, search from the scientific literature, filter retrieved information, provide accurate and comprehensive answers, and self-refine answers.
Outcome:	OpenResearcher is built on Retrieval-Augmented Generation (RAG) to integrate Large Language Models (LLMs) with up-to-date, domain-specific knowledge.

CoCoST: Automatic Complex Code Generation with Online Searching and Correctness Testing (2024.emnlp-main)

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Xinyi He, Jiaru Zou, Yun Lin, Mengyu Zhou, Shi Han, Zejian Yuan, Dongmei Zhang

Programming Languages in NLP

Challenge:	Existing methods to improve code generation from natural language descriptions are difficult due to complex structure, subtle bugs, and lack of supplementary contents.
Approach:	They propose a framework that enhances complex code generation by online searching for more information with planned queries and correctness testing for code refinement.
Outcome:	The proposed framework improves the quality of complex code generation on the DS-1000 and ClassEval datasets.

InstructDiff: Domain-Adaptive Data Selection via Contrastive Entropy for Efficient LLM Fine-Tuning (2026.acl-long)

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Junyou Su, He Zhu, Xiao Luo, Liyu Zhang, Hong-Yu Zhou, Yun Chen, Peng Li, Yang Liu, Guanhua Chen

Transferable and Efficient Non-Factual Content Detection via Probe Training with Offline Consistency Checking (2024.acl-long)

Challenge:	Existing data selection methods suffer from severe domain specificity . existing methods for general instruction-following fail on reasoning tasks .
Approach:	They propose a framework that operationalizes contrastive entropy as a domain-adaptive selection criterion through warmup calibration, bi-directional NLL filtering, and entropic-based ranking.
Outcome:	Experiments show that InstructDiff outperforms baseline training on reasoning tasks while using only 10% of the data.

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Xiaokang Zhang, Zijun Yao, Jing Zhang, Kaifeng Yun, Jifan Yu, Juanzi Li, Jie Tang

Towards Making the Most of Cross-Lingual Transfer for Zero-Shot Neural Machine Translation (2022.acl-long)

Challenge:	Existing factuality detection methods are not effective for large language models (LLMs).
Approach:	They propose a probing model that trains on offline consistency checking results.
Outcome:	The proposed model reduces the computational burden of generating multiple responses by online consistency verification and improves on factuality detection and question answering benchmarks.

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Guanhua Chen, Shuming Ma, Yun Chen, Dongdong Zhang, Jia Pan, Wenping Wang, Furu Wei

Multilinguality

Challenge:	Existing unsupervised neural machine translation systems can degrade when labeled data is limited.
Approach:	They propose a multilingual pretraining and multilingual fine-tuning for facilitating cross-lingual transfer in zero-shot translation using a parallel dataset.
Outcome:	The proposed model outperforms state-of-the-art models on many-to-English translation by over 7.2 and 5.0 BLEU.

MICO: Selective Search with Mutual Information Co-training (2022.coling-1)

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Zhanyu Wang, Xiao Zhang, Hyokun Yun, Choon Hui Teo, Trishul Chilimbi

Text Clustering

Challenge:	Selective search is designed to reduce the latency and computation in modern large-scale search systems.
Approach:	They propose a mutual information CO-training framework for selective search with minimal supervision using the search logs.
Outcome:	The proposed framework outperforms existing competitive benchmarks on multiple metrics and significantly outperformed existing baselines.

FANNO: Augmenting High-Quality Instruction Data with Open-Sourced LLMs Only (2025.findings-acl)

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He Zhu, Yifan Ding, Yicheng Tao, Zhiwen Ruan, Yixia Li, Wenjia Zhang, Yun Chen, Guanhua Chen

Multitasking Framework for Unsupervised Simple Definition Generation (2022.acl-long)

Challenge:	Recent studies explore approaches to synthesize instruction data with open-sourced LLMs but require high-quality human-crafted seed data.
Approach:	They propose an end-to-end framework to synthesize high-quality instruction data with open-sourced LLMs and sampled unlabeled documents, eliminating the need for seed data.
Outcome:	The proposed framework synthesizes high-quality instruction data with open-sourced LLMs and sampled unlabeled documents, eliminating the need for seed data.

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Cunliang Kong, Yun Chen, Hengyuan Zhang, Liner Yang, Erhong Yang

PaCoST: Paired Confidence Significance Testing for Benchmark Contamination Detection in Large Language Models (2024.findings-emnlp)

Challenge:	Existing definition generation tasks require a dictionary with complex definitions and a corpus containing arbitrary simple texts to generate them.
Approach:	They propose a multitasking framework SimpDefiner that only requires a standard dictionary with complex definitions and a corpus containing arbitrary simple texts.
Outcome:	The proposed framework outperforms the baseline model by a 1.77 SARI score on the English dataset, and raises the proportion of the low level (HSK level 1-3) words in Chinese definitions by 3.87%.

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Huixuan Zhang, Yun Lin, Xiaojun Wan

COSMOS: Connectivity-Oriented Submodular Maximization for Optimal Subgraph Retrieval (2026.acl-long)

Challenge:	Large language models are trained on vast amounts of data, which may unintentionally or intentionally include data from commonly used benchmarks.
Approach:	They propose a set of requirements that practical contamination detection methods should follow to effectively detect benchmark contamination in large language models.
Outcome:	The proposed method detects whether the model is significantly more confident under the original benchmark.

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Boci Peng, Xiao Liu, Boren Hu, Yun Zhu, Xuanbo Fan, Yanwei Yue, Chunyu Yang, Yan Zhang

MCLE-Mol: Empowering LLM with Molecular Comprehension and Low-Cost Continual Evolution for Interpretable Property Prediction (2026.findings-acl)

Challenge:	Existing paradigms treat facts independently or employ myopic search, failing to optimize collective subgraph utility.
Approach:	They propose a framework that formalizes evidence retrieval as a constrained submodular maximization problem.
Outcome:	The proposed framework captures the trade-off between information relevance and structural complexity.

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Zhili Pu, Lantian Zhang, Hao Duan, Zhixing Zhang, Keyun Zhu, Yongqi Fan, Ruihui Hou, Tong Ruan, Yun Tang

Explainability & Interpretability in NLP

Challenge:	Large language models (LLMs) offer a new paradigm for molecular property prediction (MPP), yet a semantic gap between natural language and molecul representations limits their ability to capture structure–activity relationships (SAR).
Approach:	They propose an ML–LLM–Rule collaborative framework for MPP that injects ML-derived substructure attribution values into LLMs and calibrates them under specific chemical contexts.
Outcome:	The proposed framework outperforms baseline models on multiple benchmark datasets and is highly interpretable.

Toward Fully Exploiting Heterogeneous Corpus:A Decoupled Named Entity Recognition Model with Two-stage Training (2021.findings-acl)

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Yun Hu, Yeshuang Zhu, Jinchao Zhang, Changwen Zheng, Jie Zhou

LayAlign: Enhancing Multilingual Reasoning in Large Language Models via Layer-Wise Adaptive Fusion and Alignment Strategy (2025.findings-naacl)

Challenge:	Named Entity Recognition (NER) is a fundamental and widely used task in natural language processing.
Approach:	They propose a decoupled NER model with two-stage training to take advantage of heterogeneous corpus, including dictionaries, distantly supervised instances, and human-annotated instances.
Outcome:	Empirical results show that the proposed model improves against baselines and can be scaled to a large extent.

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Zhiwen Ruan, Yixia Li, He Zhu, Longyue Wang, Weihua Luo, Kaifu Zhang, Yun Chen, Guanhua Chen

Task Calibration: Calibrating Large Language Models on Inference Tasks (2025.findings-acl)

Challenge:	Large language models (LLMs) are pretrained on multilingual corpora but exhibit suboptimal performance on low-resource languages.
Approach:	They propose a framework that integrates representations from all encoder layers and an adaptive fusion-enhanced attention mechanism to enable layer-wise interaction between the LLM and the multilingual encoder.
Outcome:	Experiments on multilingual reasoning tasks show that the proposed framework outperforms baselines.

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Yingjie Li, Yun Luo, Xiaotian Xie, Yue Zhang

PerSphere: A Comprehensive Framework for Multi-Faceted Perspective Retrieval and Summarization (2025.acl-long)

Challenge:	Large language models (LLMs) have shown impressive zero-shot performance on inference tasks, however, they may suffer from spurious correlations between input texts and output labels, which limits their ability to reason based purely on general language understanding.
Approach:	They propose a zero-shot and inference-only calibration method inspired by mutual information which recovers LLM performance through task reformulation.
Outcome:	The proposed calibration method improves on 13 benchmarks and prompt templates and can be integrated with other calibration methods.

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Yun Luo, Yingjie Li, Xiangkun Hu, Qinglin Qi, Fang Guo, Qipeng Guo, Zheng Zhang, Yue Zhang

Information Extraction & Text Mining

Challenge:	Experimental results show that the main challenge lies in long context and perspective extraction.
Approach:	They propose a benchmark to facilitate multi-faceted perspective retrieval and summarization . they propose measurable metrics to evaluate the comprehensiveness of the retrieval pipeline .
Outcome:	The proposed system breaks free from information silos by combining two opposing claims . it can be used to extract multiple perspectives and improve performance on the platform .

FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models (2025.naacl-long)

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Xin Guo, Haotian Xia, Zhaowei Liu, Hanyang Cao, Zhi Yang, Zhiqiang Liu, Sizhe Wang, Jinyi Niu, Chuqi Wang, Yanhui Wang, Xiaolong Liang, Xiaoming Huang, Bing Zhu, Zhongyu Wei, Yun Chen, Weining Shen, Liwen Zhang

AdvancedIF: Rubric-Based Benchmarking and Reinforcement Learning for Advancing LLM Instruction Following (2026.acl-long)

Challenge:	Large language models have demonstrated outstanding performance in various natural language processing tasks, but their security capabilities in the financial domain have not been explored.
Approach:	They propose to use a benchmark to evaluate large language models' financial domain knowledge and practical abilities.
Outcome:	The proposed benchmark evaluates large language models' financial domain knowledge and practical abilities.

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Yun He, Wenzhe Li, Hejia Zhang, Songlin Li, Karishma Mandyam, Sopan Khosla, Yuanhao Xiong, Nanshu Wang, Xiaoliang Peng, Beibin Li, Shengjie Bi, Shishir G Patil, Qi Qi, Shengyu Feng, Julian Katz-Samuels, Richard Yuanzhe Pang, Sujan Kumar Gonugondla, Hunter Lang, Yue Yu, Yundi Qian, Maryam Fazel-Zarandi, Licheng Yu, Amine Benhalloum, Hany Hassan Awadalla, Manaal Faruqui

When GPT Spills the Tea: Comprehensive Assessment of Knowledge File Leakage in GPTs (2025.acl-long)

Challenge:	Recent advances in large language models (LLMs) have shown impressive performance on a range of tasks, yet advanced instruction following (IF) remains a significant challenge.
Approach:	They propose a benchmark that features over 1,600 prompts and expert-curated rubrics that assess LLMs’ ability to follow complex, multi-turn, and system-level instructions.
Outcome:	The proposed framework improves instruction-following abilities of large language models, achieving a 6.7% gain on AdvancedIF and strong results on public benchmarks.

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Xinyue Shen, Yun Shen, Michael Backes, Yang Zhang

MEGen: Generative Backdoor into Large Language Models via Model Editing (2025.findings-acl)

Challenge:	Existing studies show that adversarial prompts can induce GPTs to leak knowledge file content.
Approach:	They propose a workflow inspired by Data Security Posture Management to identify five leakage vectors for knowledge file leakage using 651,022 GPT metadata and 11,820 flows.
Outcome:	The proposed workflow analyzes 651,022 GPT metadata, 11,820 flows, and 1,466 responses to identify five leakage vectors: metadata, GPT initialization, retrieval, sandboxed execution environments, and prompts.

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Jiyang Qiu, Xinbei Ma, Zhuosheng Zhang, Hai Zhao, Yun Li, Qianren Wang

CoViPAL: Layer-wise Contextualized Visual Token Pruning for Large Vision-Language Models (2025.findings-emnlp)

Challenge:	Existing methods for training large language models are limited to yes-or-no discriminative tasks, leading users to underestimate the potential risks.
Approach:	They propose an editing-based generative backdoor that expands the backdoor to generative tasks in a unified format of any text-to-any text.
Outcome:	The proposed model achieves high attack success rate by adjusting only a small set of local parameters with few-shot samples.

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Zicong Tang, Ziyang Ma, Suqing Wang, Zuchao Li, Lefei Zhang, Hai Zhao, Yun Li, Qianren Wang

Visual Data in NLP

Challenge:	Existing methods to prune redundant vision tokens struggle in shallow layers due to the lack of contextual information.
Approach:	They propose a layer-wise contextualized visual token pruning method that uses a plug-and-play Pruning Module to prune redundant vision tokens.
Outcome:	The proposed method outperforms training-free pruning methods under equal token budgets and surpasses training based methods with comparable supervision.

Proofread: Fixes All Errors with One Tap (2024.acl-demos)

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Renjie Liu, Yanxiang Zhang, Yun Zhu, Haicheng Sun, Yuanbo Zhang, Michael Huang, Shanqing Cai, Lei Meng, Shumin Zhai