Papers by Yang Qiu

DORE: Document Ordered Relation Extraction based on Generative Framework (2022.findings-emnlp)

Challenge:	Best-of-N (BoN) sampling generates multiple responses and selects the best one, achieving improved performance but with a high computational cost.
Approach:	They propose a framework that integrates a speculative tree-search strategy into Best-of-N (BoN) Sampling.
Outcome:	The proposed framework outperforms Best-of-N (BoN) sampling but has high computational cost . tree-search strategy reduces computational overhead while maintaining high output quality .

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Qipeng Guo, Yuqing Yang, Hang Yan, Xipeng Qiu, Zheng Zhang

Information Extraction & Text Mining

Challenge:	Existing generative methods do not fit document-level relation extraction tasks where there are multiple entities and relational facts.
Approach:	They propose to generate a symbolic and ordered sequence from relation matrix which is easier to learn and introduce several negative sampling strategies to improve the performance with balanced signals.
Outcome:	The proposed method can improve the performance of the generative DocRE models on four datasets.

LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models (2023.emnlp-main)

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Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang, Lili Qiu

An AMR-based Link Prediction Approach for Document-level Event Argument Extraction (2023.acl-long)

Challenge:	Large language models (LLMs) are increasingly lengthy and require longer prompts . this paper presents a coarse-to-fine prompt compression method to reduce cost and increase performance.
Approach:	They propose a coarse-to-fine prompt compression method that maintains semantic integrity under high compression ratios and a token-level iterative compression algorithm to better model the interdependence between compressed contents.
Outcome:	The proposed method yields state-of-the-art performance and allows for up to 20x compression with little performance loss over four datasets from different scenarios.

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Yuqing Yang, Qipeng Guo, Xiangkun Hu, Yue Zhang, Xipeng Qiu, Zheng Zhang

Representation Learning

Challenge:	Recent work has introduced Abstract Meaning Representation (AMR) for Document-level Event Argument Extraction (Doc-level EAE) however, in these works AMR is used only implicitly, for instance, as additional features or training signals.
Approach:	They propose a novel AMR-based graph structure which uses graph neural networks to find event arguments from unstructured text.
Outcome:	The proposed graph structure outperforms the state-of-the-art models by 3.63pt and 2.33pt F1 and reduces inference time by 56%.

Text is All You Need: LLM-enhanced Incremental Social Event Detection (2025.acl-long)

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Zitai Qiu, Congbo Ma, Jia Wu, Jian Yang

Information Extraction & Text Mining

Challenge:	Existing state-of-the-art (SOTA) SED models rely on graph neural networks (GNNs) Existing SED frameworks rely heavily on GNNs, which require complex graph construction and time-consuming training processes.
Approach:	They propose a framework that leverages the rich background knowledge of large language models to formalize and disambiguate short texts by completing abbreviations and summarizing informal expressions.
Outcome:	The proposed framework outperforms existing models on two challenging real-world datasets.

Beyond Memorization: A Rigorous Evaluation Framework for Medical Knowledge Editing (2026.eacl-long)

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Shigeng Chen, Linhao Luo, Zhangchi Qiu, Yanan Cao, Carl Yang, Shirui Pan

Challenge:	Existing knowledge editing methods show promising results on general-domain benchmarks, but their effectiveness in the medical domain remains largely unexplored.
Approach:	They propose a framework to evaluate medical knowledge editing using model-generated rationales as editing targets.
Outcome:	The proposed method improves editing efficacy and generalization in medical models without full retraining.

Knowledge-Empowered Representation Learning for Chinese Medical Reading Comprehension: Task, Model and Resources (2021.findings-acl)

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Taolin Zhang, Chengyu Wang, Minghui Qiu, Bite Yang, Zerui Cai, Xiaofeng He, Jun Huang

Towards Unified Prompt Tuning for Few-shot Text Classification (2022.findings-emnlp)

Challenge:	MRC is a popular task in NLP, aiming to understand a passage and answer the relevant questions.
Approach:	They propose a multi-target machine learning task for the medical domain that predicts answers to medical questions and corresponding support sentences from medical information sources simultaneously.
Outcome:	The proposed model outperforms baselines by fusing context-aware and knowledge-awful token representations.

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Jianing Wang, Chengyu Wang, Fuli Luo, Chuanqi Tan, Minghui Qiu, Fei Yang, Qiuhui Shi, Songfang Huang, Ming Gao

Latent Inter-User Difference Modeling for LLM Personalization (2025.emnlp-main)

Challenge:	Prompt-based fine-tuning has boosted performance of Pre-trained Language Models (PLMs) on few-shot text classification, but PLMs are unfamiliar with prompt-style expressions during pre-training, which limits the few- shot learning performance on downstream tasks.
Approach:	They propose a framework for prompt-based fine-tuning that captures prompting semantics from non-target NLP datasets and propose 'Prompt-Options-Verbalizer' for joint prompt learning across different NLP tasks.
Outcome:	Experiments show that the proposed framework outperforms state-of-the-art prompt-based fine-tuning frameworks on few-shot text classification tasks.

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Yilun Qiu, Tianhao Shi, Xiaoyan Zhao, Fengbin Zhu, Yang Zhang, Fuli Feng

GLM: General Language Model Pretraining with Autoregressive Blank Infilling (2022.acl-long)

Challenge:	Large language models (LLMs) are increasingly integrated into users’ daily lives, leading to a growing demand for personalized outputs.
Approach:	They propose a framework that models inter-user differences in the latent space instead of relying on language-based prompts.
Outcome:	The proposed framework outperforms baseline methods on personalized review generation.

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Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, Jie Tang

Enhancing Dialogue Generation with Conversational Concept Flows (2023.findings-eacl)

Challenge:	Existing pretraining frameworks do not perform well for all tasks of three main categories, such as natural language understanding (NLU), unconditional generation, and conditional generation.
Approach:	They propose a general language model based on autoregressive blank infilling to address this challenge.
Outcome:	The proposed model outperforms BERT, T5, and GPT on a wide range of tasks across NLU, conditional and unconditional generation tasks.

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Siheng Li, Wangjie Jiang, Pengda Si, Cheng Yang, Qiu Yao, Jinchao Zhang, Jie Zhou, Yujiu Yang

Dialogue Systems & Conversational Agents

Challenge:	Existing studies show that explicitly modeling concept flows with a large commonsense knowledge graph improves response quality, but there is a gap between the knowledge graph and the conversation.
Approach:	They propose to model human conversational concept flows with a commonsense knowledge graph . they extract abundant concepts and relations from natural conversations and build a conversation-aware knowledge graph.
Outcome:	The proposed method performs better than baselines on a large-scale reddit conversation dataset.

HERMES: KV Cache as Hierarchical Memory for Efficient Streaming Video Understanding (2026.acl-long)

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Haowei Zhang, Shudong Yang, Jinlan Fu, See-Kiong Ng, Xipeng Qiu

ETR: Entropy Trend Reward for Efficient Chain-of-Thought Reasoning (2026.acl-long)

Challenge:	Existing models struggle to maintain stable understanding performance and low GPU memory overhead.
Approach:	They propose a training-free architecture for real-time and accurate understanding of video streams . HERMES reuses a compact KV cache, enabling efficient streaming understanding .
Outcome:	The proposed architecture achieves 10 faster TTFT compared to prior SOTA.

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Xuan Xiong, Huan Liu, Li Gu, Zhixiang Chi, Yue Qiu, Yuanhao YU, Yang Wang

pEBR: A Probabilistic Approach to Embedding Based Retrieval (2025.emnlp-industry)

Challenge:	Existing methods to shorten CoTs use length penalties or global entropy reduction . Existing approaches to CoT reasoning have significant practical drawbacks .
Approach:	They propose a method that shortens CoTs by length penalties or global entropy reduction . they integrate ETR into Group Relative Policy Optimization and evaluate it .
Outcome:	The proposed objective improves accuracy–efficiency trade-off by +9.9% while reducing CoT length by 67% across four benchmarks.

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Han Zhang, Yunjiang Jiang, Mingming Li, Haowei Yuan, Yiming Qiu, Wen-Yun Yang

AudioVSR: Enhancing Video Speech Recognition with Audio Data (2024.emnlp-main)

Challenge:	Existing embedding-based retrieval systems rely on heuristic and suboptimal cutoffs for item retrieval.
Approach:	They propose a probabilistic Embedding-Based Retrieval framework that learns a shared semantic representation space for both queries and items.
Outcome:	The proposed framework improves retrieval precision and recall, and ablation studies show it captures the differences between head-to-tail queries.

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Xiaoda Yang, Xize Cheng, Jiaqi Duan, Hongshun Qiu, Minjie Hong, Minghui Fang, Shengpeng Ji, Jialong Zuo, Zhiqing Hong, Zhimeng Zhang, Tao Jin

Temporal Consistency for LLM Reasoning Process Error Identification (2025.findings-emnlp)

Challenge:	Recent work has shown poor performance with non-Indo-European languages . previous work primarily utilizes video information to build VSR models .
Approach:	They propose a generative model for data inflation that integrates synthetic data with authentic visual data to enhance the VSR model.
Outcome:	The proposed model improves on the audio-visual alignment problem in audio-video tasks.

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Jiacheng Guo, Yue Wu, Jiahao Qiu, Kaixuan Huang, Xinzhe Juan, Ling Yang, Mengdi Wang

Boosting Scientific Concepts Understanding: Can Analogy from Teacher Models Empower Student Models? (2024.emnlp-main)

Challenge:	Empirical evaluations show consistent performance improvements over baseline methods . 7B/8B distilled models outperform all 70B/72B models and GPT-4o on ProcessBench .
Approach:	They propose a temporal consistency method that leverages consistency in a sequence of self-reflection actions to improve verification accuracy.
Outcome:	The proposed method outperforms existing methods on three benchmarks . it leverages consistency in a sequence of self-reflection actions to improve accuracy .

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Siyu Yuan, Cheng Jiayang, Lin Qiu, Deqing Yang

IEvoAgent: Evolving Conversational Agent based on User Implicit Feedback (2026.acl-long)

Challenge:	Analogical reasoning plays a critical role in human cognition, enabling us to understand new concepts by associating them with familiar ones.
Approach:	They propose to use free-form analogies to aid students in understanding scientific concepts . they also show that analogies generated by student LMs can improve their own performance .
Outcome:	The proposed model can help students understand scientific concepts, the authors show .

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Yichen Cai, Jiayang Li, Junyuan Qiu, Jingya Guo, Weitao You, Changyuan Yang, Lingyun Sun, Pei Chen

Dialogue Systems & Conversational Agents

Challenge:	Existing approaches to optimize conversational agents often rely on explicit preference pairs and expert evaluations.
Approach:	They propose a conversational agent framework that leverages the structured dependency between agent responses and user reactions to extract implicit feedback.
Outcome:	The proposed framework improves on MT-Bench-101, WildBench, and FB-Bech, and shows that mining implicit feedback supports better multi-turn alignment under evolving user preferences.

WESR: A Benchmark and Strong Baseline for Word-level Event-Speech Recognition (2026.findings-acl)

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Chenchen Yang, Kexin Huang, Liwei Fan, Qian Tu, Botian Jiang, Dong Zhang, Linqi Yin, Shimin Li, Zhaoye Fei, Qinyuan Cheng, Xipeng Qiu

Improving Automatic Grammatical Error Annotation for Chinese Through Linguistically-Informed Error Typology (2025.coling-main)

Challenge:	aaron carroll: the precise localization of non-verbal vocal events remains a critical yet under-explored challenge. carroll says current methods suffer from insufficient task definitions with limited category coverage. carrol: knowing exactly where an event occurred is not enough; knowing exactly what it happened is.
Approach:	They propose a taxonomy of 21 vocal events with a new categorization into discrete versus continuous types.
Outcome:	The proposed model disentangles ASR errors from event detection while maintaining ASR quality.

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Yang Gu, Zihao Huang, Min Zeng, Mengyang Qiu, Jungyeul Park

Syntactic Text Processing

Challenge:	In educational settings, GEC systems provide immediate and consistent feedback to both native (L1) and non-native (L2) language learners.
Approach:	They propose a framework that provides detailed feedback on 12-16% of all errors by identifying them under a new error typology, specific enough to uncover subtle differences in error patterns between L1 and L2 writings.
Outcome:	The proposed framework can provide detailed feedback on 12-16% of all errors, revealing subtle differences in error patterns between L1 and L2 writings.

AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)

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Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang

Multi-Programming Language Sandbox for LLMs (2025.acl-demo)

Challenge:	Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents.
Approach:	They propose a framework that features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction.
Outcome:	The proposed framework features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction.

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Shihan Dou, Jiazheng Zhang, Jianxiang Zang, Yunbo Tao, Weikang Zhou, Haoxiang Jia, Shichun Liu, Yuming Yang, Shenxi Wu, Zhiheng Xi, Muling Wu, Rui Zheng, Changze Lv, Limao Xiong, Shaoqing Zhang, Lin Zhang, Wenyu Zhan, Rongxiang Weng, Jingang Wang, Xunliang Cai, Yueming Wu, Ming Wen, Yixin Cao, Tao Gui, Xipeng Qiu, Qi Zhang, Xuanjing Huang

Multi-Agent Collaboration via Cross-Team Orchestration (2025.findings-acl)

Challenge:	MPLSandbox is an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Language Models (LLMs).
Approach:	They propose a multi-programming language sandbox that provides unified feedback from compilers and analysis tools for Large Language Models.
Outcome:	The proposed multi-language sandbox can provide comprehensive feedback from compilers and analysis tools for large language models (LLMs).

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Zhuoyun Du, Chen Qian, Wei Liu, Zihao Xie, YiFei Wang, Rennai Qiu, Yufan Dang, Weize Chen, Cheng Yang, Ye Tian, Xuantang Xiong, Lei Han

Challenge:	Large Language Models (LLMs) have significantly impacted various domains, especially through organized LLM-driven autonomous agents.
Approach:	They propose a framework that enables orchestrated teams to jointly propose various task-oriented solutions and interact with their insights in a self-independence while cross-team collaboration environment for superior solutions generation.
Outcome:	Experiments show that the framework can generate better software quality compared to state-of-the-art frameworks.

LASA: Language-Agnostic Semantic Alignment at the Semantic Bottleneck for LLM Safety (2026.acl-long)

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Junxiao Yang, Haoran Liu, Jinzhe Tu, Jiale Cheng, Zhexin Zhang, Shiyao Cui, Jiaqi Weng, Jialing Tao, Hui Xue, Hongning Wang, Han Qiu, Minlie Huang

Challenge:	Large language models (LLMs) have demonstrated better safety performance in high-resource languages than in low-resourced languages.
Approach:	They propose language-agnostic semantic alignment (LASA) which anchors safety alignment directly in semantic bottlenecks.
Outcome:	The proposed approach significantly improves safety across all languages: average attack success rate drops from 24.7% to 2.8% on LLaMA-3.1-8B-Instruct and remains within 3–4% across Qwen2.5 and Qwend3 Instruct models (7B–32B).

Firewall Routing: Blocking Leads to Better Hybrid Inference for LLMs (2025.emnlp-main)

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Runyu Peng, Yunhua Zhou, Kai Lv, Yang Gao, Qipeng Guo, Xipeng Qiu

Challenge:	Large language models have significantly enhanced performance across various NLP tasks . high computational costs and latency associated with deploying such models pose bottlenecks .
Approach:	They propose a dynamic hybrid inference framework that efficiently selects between a strong and a weak LLM based on the complexity of the query.
Outcome:	The proposed method outperforms existing routing strategies by up to 5.29% in APGR . large models often introduce higher latency, making them less suitable for real-time or resource-constrained applications.

Efficient Pretraining Data Selection for Language Models via Multi-Actor Collaboration (2025.acl-long)

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Tianyi Bai, Ling Yang, Zhen Hao Wong, Fupeng Sun, Xinlin Zhuang, Jiahui Peng, Chi Zhang, Lijun Wu, Qiu Jiantao, Wentao Zhang, Binhang Yuan, Conghui He

Can Language Models Follow Multiple Turns of Entangled Instructions? (2025.findings-emnlp)

Challenge:	Efficient data selection is crucial to accelerate the pretraining of language models . limited research has addressed the inherent conflicts between data selection methods .
Approach:	They propose a multi-actor collaborative data selection mechanism that prioritizes data based on its specific criterion and updates prioritization rules using the current state of the model.
Outcome:	The proposed model accelerates convergence in LM pretraining and achieves an average relative performance gain of 10.5% across multiple language model benchmarks.

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Chi Han, Xin Liu, Haodong Wang, Shiyang Li, Jingfeng Yang, Haoming Jiang, Zhengyang Wang, Qingyu Yin, Liang Qiu, Changlong Yu, Yifan Gao, Zheng Li, Bing Yin, Jingbo Shang, Heng Ji

Beyond the Last Frame: Process-aware Evaluation for Generative Video Reasoning (2026.acl-long)

Challenge:	Despite of significant achievements in improving instruction-following capabilities of large language models, the ability to process multiple potentially entangled or conflicting instructions remains a considerable challenge.
Approach:	They construct multi-turn instruction with 1.1K high-quality multi-turned conversations using the human-in-the-loop approach and examine their capabilities.
Outcome:	The proposed model shows that it is difficult to integrate multiple turns and balance competing objectives when instructions intersect or conflict.

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Yifan Li, YuKai Gu, Yingqian Min, Zikang Liu, Yifan Du, Kun Zhou, Min Yang, Xin Zhao, Minghui Qiu

Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce (P18-2)

Challenge:	Existing evaluation frameworks often rely on single-frame assessments, which can lead to outcome-hacking.
Approach:	They propose a process-aware evaluation paradigm that uses a hierarchical rubric to evaluate the validity of the intermediate steps and the final result.
Outcome:	The proposed model achieves POC@1.0 only about 20% and exhibits significant outcome-hacking.

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Minghui Qiu, Liu Yang, Feng Ji, Wei Zhou, Jun Huang, Haiqing Chen, Bruce Croft, Wei Lin

Natural Language Interfaces

Challenge:	Recent researches focus on deep learning and reinforcement learning for multi-turn information seeking conversation systems.
Approach:	They propose an efficient and effective multi-turn conversation model based on convolutional neural networks and extend it to adapt the knowledge learned from a resource-rich domain to enhance the performance.
Outcome:	The proposed model performs better than the existing model on an industrial chatbot called AliMe Assist.

Mitigate Position Bias in LLMs via Scaling a Single Hidden States Channel (2025.findings-acl)

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Yijiong Yu, Huiqiang Jiang, Xufang Luo, Qianhui Wu, Chin-Yew Lin, Dongsheng Li, Yuqing Yang, Yongfeng Huang, Lili Qiu

Challenge:	Long-context language models exhibit position bias, also known as "lost in the middle" research shows that even long-contemporary LLMs fail to utilize all context information effectively .
Approach:	They propose a method to mitigate position bias by scaling positional hidden states . they propose to use a channel of hidden states to modify positional Hidden states a LCLM's positional bias .
Outcome:	The proposed method can improve performance by 15.2% in a "lost in the middle" benchmark.

ProLongVid: A Simple but Strong Baseline for Long-context Video Instruction Tuning (2025.emnlp-main)

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Rui Wang, Bohao Li, Xiyang Dai, Jianwei Yang, Yi-Ling Chen, Zhen Xing, Yifan Yang, Dongdong Chen, Xipeng Qiu, Zuxuan Wu, Yu-Gang Jiang

Refined Evaluation for End-to-End Grammatical Error Correction Using an Alignment-Based Approach (2025.coling-main)

Challenge:	Existing approaches to adapt image-focused models for video understanding have not been successful in analyzing long video sequences.
Approach:	They propose a video instruction dataset that outperforms existing video instruction data for fine-tuning MLLMs by incrementally increasing input context length.
Outcome:	The proposed model outperforms existing models on video benchmarks and outperformed proprietary models on VideoMME even with a compact 7B model.

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Junrui Wang, Mengyang Qiu, Yang Gu, Zihao Huang, Jungyeul Park

Syntactic Text Processing

Challenge:	errant is a new evaluation tool that can be used to evaluate end-to-end grammatical error correction systems.
Approach:	They propose a method to assess end-to-end grammatical error correction systems using alignment-based alignment methods that reproduce and improve results from existing evaluation tools.
Outcome:	The proposed method reproduces and improves results from existing evaluation tools, such as errant, even when applied to raw text input.

PLAWBENCH: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice (2026.acl-long)

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Yuzhen Shi, Huanghai Liu, Yiran HU, Song Gaojie, Xu Xinran, Yubo Ma, Tianyi Tang, Li Zhang, Qingjing Chen, Feng Di, Wenbo Lv, Weiheng Wu, Kexin Yang, Sen Yang, Wei Wang, Rongyao Shi, Qiu Yuanyang, Yuemeng Qi, Zhang Jingwen, Sui Xiaoyu, Yifan Chen, Zhang Yi, An Yang, Bowen Yu, Dayiheng Liu, Junyang Lin, Weixing Shen, Bing Zhao, Charles L. A. Clarke, HU Wei

Attribution-Based Analysis and Optimization of Modular Agentic Workflows (2026.findings-acl)

Challenge:	Existing benchmarks for large language models (LLMs) are coarse, single-dimensional metrics and do not explicitly assess fine-grained legal reasoning.
Approach:	They propose a Practical Law Benchmark to evaluate large language models in real-world legal practice scenarios.
Outcome:	The proposed model is based on 850 questions and 13 scenarios with expert-designed evaluation rubrics.

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Yingxuan Yang, Bo Huang, Siyuan Qi, Chao Feng, Haoyi Hu, Yuxuan Zhu, Jinbo Hu, Haoran Zhao, Ziyi He, Xiao Liu, ZongYu Wang, Muning Wen, Lin Qiu, Xuezhi Cao, Xunliang Cai, Yong Yu, Weinan Zhang

Challenge:	Large Language Models (LLMs) have driven the rise of agentic workflows . yet, how can we attribute performance gains to individual upgrades and their interactions?
Approach:	They propose a game-theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values.
Outcome:	The proposed framework provides interaction-aware attribution and recommendation for model allocation under a fixed workflow structure.

XY-Tokenizer: Mitigating the Semantic-Acoustic Conflict in Low-Bitrate Speech Codecs (2026.acl-long)

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Yitian Gong, Luozhijie Jin, Kuangwei Chen, Dong Zhang, Ruifan Deng, Xiaogui Yang, Xin Zhang, Zhaoye Fei, Qinyuan Cheng, Shimin Li, Xipeng Qiu

Exploring Multimodal Challenges in Toxic Chinese Detection: Taxonomy, Benchmark, and Findings (2025.findings-acl)

Challenge:	Existing speech codecs struggle to balance these objectives at low bitrates . XY-Tokenizer achieves stronger semantic alignment than representative semantic-distillation codec .
Approach:	They propose a low-bitrate speech codec that aligns discrete speech representations with text while preserving fine-grained acoustic details for reconstruction.
Outcome:	The proposed codec outperforms existing low-bitrate speech codecs in speech understanding and generation tasks.

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Shujian Yang, Shiyao Cui, Chuanrui Hu, Haicheng Wang, Tianwei Zhang, Minlie Huang, Jialiang Lu, Han Qiu

DeltaNet: Conditional Medical Report Generation for COVID-19 Diagnosis (2022.coling-1)

Challenge:	Recent studies show that character substitutions in toxic Chinese text can confuse state-of-the-art LLMs.
Approach:	They propose a taxonomy of 3 perturbation strategies and 8 specific approaches in Chinese text to assess if they can detect perturbed Chinese toxic contents.
Outcome:	The proposed model can detect perturbed Chinese text with 8 different approaches . the proposed model is compared with 9 other LLMs from the US and China .

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Xian Wu, Shuxin Yang, Zhaopeng Qiu, Shen Ge, Yangtian Yan, Xingwang Wu, Yefeng Zheng, S. Kevin Zhou, Li Xiao

Full Parameter Fine-tuning for Large Language Models with Limited Resources (2024.acl-long)

Challenge:	X-ray and CT are the gold standard for COVID-19 diagnosis and treatment . however, due to the excessive number of patients, writing reports becomes a heavy burden for radiologists.
Approach:	They propose to use X-ray and CT to generate medical reports automatically . they evaluate DeltaNet on a COVID-19 dataset, where it outperforms state-of-the-art approaches .
Outcome:	The proposed system outperforms state-of-the-art methods on a COVID-19 dataset.

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Kai Lv, Yuqing Yang, Tengxiao Liu, Qipeng Guo, Xipeng Qiu

Improving Contrastive Learning of Sentence Embeddings from AI Feedback (2023.findings-acl)

Challenge:	Large Language Models (LLMs) require massive GPU resources for training.
Approach:	They propose a parameter-efficient optimization that fuses the gradient computation and parameter update in one step to reduce memory usage.
Outcome:	The proposed method reduces memory usage to 10.8% compared to the standard approach.

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Qinyuan Cheng, Xiaogui Yang, Tianxiang Sun, Linyang Li, Xipeng Qiu

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

Challenge:	Existing methods to learn sentence embeddings with rich semantics are limited due to the discrete nature of natural language.
Approach:	They propose to use AI feedback to improve contrastive learning of sentence embeddings by combining human feedback and AI feedback.
Outcome:	The proposed method achieves state-of-the-art performance on several semantic textual similarity and transfer learning tasks compared to other unsupervised and supervised contrastive learning methods.

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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 .

Two Birds One Stone: Dynamic Ensemble for OOD Intent Classification (2023.acl-long)

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Yunhua Zhou, Jianqiang Yang, Pengyu Wang, Xipeng Qiu

Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards (2024.acl-long)

Challenge:	Out-of-domain (OOD) intent classification is an active field of natural language understanding . previous studies have suggested that PTMs would be "overthinking" the semantic features of the sample in the open-world scenario .
Approach:	They propose a method that allows the model to decide whether to make a decision on OOD classification early during inference.
Outcome:	The proposed method can improve inference speed and achieve significant performance improvements.

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Haoxiang Wang, Yong Lin, Wei Xiong, Rui Yang, Shizhe Diao, Shuang Qiu, Han Zhao, Tong Zhang

Challenge:	Reinforcement Learning from Human Feedback (RLHF) relies on scalar rewards to capture user preferences.
Approach:	They propose a framework that integrates multi-objective reward modeling to represent diverse preference profiles.
Outcome:	The proposed method improves performance across reward objectives and targets.

LeanK: Learnable K Cache Channel Pruning for Efficient Decoding (2025.emnlp-main)

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Yike Zhang, Zhiyuan He, Huiqiang Jiang, Chengruidong Zhang, Yuqing Yang, Jianyong Wang, Lili Qiu

Green & Sustainable NLP

Challenge:	Existing efforts to optimize the key-value (KV) cache include: (1) Eviction, which discards cache of less important tokens; (2) Selection, which retains the full KV cache but selectively reads relevant entries.
Approach:	They propose a learning-based method that prunes unimportant key (K) cache channels by leveraging static channel sparsity.
Outcome:	Experiments show that LeanK reduces GPU memory and accelerates decoding without sacrificing accuracy.

TOXIFRENCH: Benchmarking and Enhancing Language Models via CoT Fine-Tuning for French Toxicity Detection (2026.findings-acl)

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Axel Delaval, Shujian Yang, Haicheng Wang, Han Qiu, Jialiang LU

Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalization (2025.findings-acl)

Challenge:	toxicity detection in French remains underdeveloped due to the lack of culturally relevant, human-annotated, large-scale datasets.
Approach:	They propose a method that generalizes French online comments using a semi-automated annotation pipeline that reduces manual labeling to only 10% through high-confidence LLM-based pre-annotation and human verification.
Outcome:	The proposed model outperforms GPT-4o and DeepSeek-R1 on the benchmark while maintaining cross-lingual capabilities.

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Yilun Qiu, Xiaoyan Zhao, Yang Zhang, Yimeng Bai, Wenjie Wang, Hong Cheng, Fuli Feng, Tat-Seng Chua

Challenge:	Extensive experiments on real-world datasets demonstrate that DPL significantly enhances LLM personalization.
Approach:	They propose a novel approach that emphasizes extracting inter-user differences to enhance LLM personalization.
Outcome:	The proposed approach extracts inter-user differences to enhance LLM personalization.

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

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

GLIMPSE: Do Large Vision-Language Models Truly Think With Videos or Just Glimpse at Them? (2025.emnlp-main)

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.

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Yiyang Zhou, Linjie Li, Shi Qiu, Zhengyuan Yang, Yuyang Zhao, Siwei Han, Yangfan He, Kangqi Li, Haonian Ji, Zihao Zhao, Haibo Tong, Lijuan Wang, Huaxiu Yao

Accelerating Prefilling via Decoding-time Contribution Sparsity (2026.findings-acl)

Challenge:	Existing video benchmarks often resemble image-based questions with scans of only a few key frames, without deep temporal reasoning.
Approach:	They propose a video benchmark to assess whether large vision-language models can genuinely think with videos rather than perform superficial frame-level analysis.
Outcome:	The proposed benchmark consists of 3,269 videos and over 4,342 highly visual-centric questions across 11 categories, including Trajectory Analysis, Temporal Reasoning, and Forensics Detection.

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Zhiyuan He, Yike Zhang, Chengruidong Zhang, Huiqiang Jiang, Yuqing Yang, Lili Qiu

Challenge:	Existing acceleration methods exploit attention score sparsity by estimating blocks with high attention scores and applying dynamic sparse attention.
Approach:	They propose a method which replaces dense attention with Triangle attention in a subset of layers to reduce the time needed to decode.
Outcome:	Experiments show that TriangleMix achieves near-lossless performance on long-context and long-constrast reasoning benchmarks while significantly improving efficiency.

Failure makes the agent stronger: Enhancing Accuracy through Structured Reflection for Reliable Tool Interactions (2026.findings-acl)

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Junhao Su, Yuanliang Wan, Junwei Yang, Hengyu Shi, Tianyang Han, Yurui Qiu, Junfeng Luo

Knowledge-Augmented Question Error Correction for Chinese Question Answer System with QuestionRAG (2025.emnlp-industry)

Challenge:	Existing approaches to self-reflection rely on heuristic prompting or unidirectional reasoning traces.
Approach:	They propose a structured reflection method that transforms the "from error to repair" process into a first-class, controllable, and trainable action.
Outcome:	The proposed method improves multi-turn tool-call success rates and error recovery while reducing redundant calls.

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Longpeng Qiu, Ting Li, Shuai Mao, Nan Yang, Xiaohui Yan

Explainability & Interpretability in NLP

Challenge:	Large language models struggle with input errors, often failing to interpret user intent or altering the original question’s structure (over-correction).
Approach:	They propose a framework that uses reinforcement learning to address misinterpretation and over-correction by integrating external knowledge with the input.
Outcome:	The proposed framework unlocks the full potential of LLMs for the question correction task.

VehicleWorld: A Highly Integrated Multi-Device Environment for Intelligent Vehicle Interaction (2025.findings-emnlp)

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Jie Yang, Jiajun Chen, Zhangyue Yin, Shuo Chen, Yuxin Wang, Yiran Guo, Yuan Li, Yining Zheng, Xuanjing Huang, Xipeng Qiu

Challenge:	Traditional Function Calling (FC) approaches operate statelessly, requiring multiple exploratory calls to build environmental awareness before execution, leading to inefficiency and limited error recovery.
Approach:	They propose a state-based function call approach that maintains explicit system state awareness and implements direct state transitions to achieve target conditions.
Outcome:	The proposed approach outperforms traditional function calling approaches, achieving superior execution accuracy and reduced latency.

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

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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

EEE-QA: Exploring Effective and Efficient Question-Answer Representations (2024.lrec-main)

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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Zhanghao Hu, Yijun Yang, Junjie Xu, Yifu Qiu, Pinzhen Chen

When Audio and Text Disagree: Revealing Text Bias in Large Audio-Language Models (2025.emnlp-main)

Challenge:	Current approaches to question answering rely on pre-trained language models like RoBERTa.
Approach:	They propose a pooling approach that embeds all answer candidates with the question . they also propose enabling cross-reference between answer choices .
Outcome:	The proposed methods improve throughput and memory efficiency with little sacrifice in performance.

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Cheng Wang, Gelei Deng, Xianglin Yang, Han Qiu, Tianwei Zhang

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

Challenge:	Large Audio-Language Models (LALMs) are augmented with the ability to perceive audio, but their reliability when faced with conflicting inputs remains largely unexplored.
Approach:	They examine how LALMs prioritize information when presented with inconsistent audio-text pairs.
Outcome:	The proposed models display a significant bias toward textual input when presented with inconsistent audio-text pairs.

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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

RouterHGC: Optimized Router for LLM-based Multi-Agent Systems via Heterogeneous Graph Contrastive Learning (2026.findings-acl)

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.

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Yitao Xiao, Shaoyong Guo, Guoming Yang, Qingnan Wang, Yinlin Ren, Xuesong Qiu, Qi Feng

Position Engineering: Boosting Large Language Models through Positional Information Manipulation (2024.emnlp-main)

Challenge:	Large Language Models (LLMs)-driven Multi-Agent Systems (MAS) have demonstrated remarkable scalability and generalizability across complex tasks.
Approach:	They propose a new framework for routing using large language models . they formalize routing as node selection through edge-weight prediction .
Outcome:	The proposed framework outperforms the best single LLM and baselines on five datasets . it achieves 0.80%–6.17% accuracy gains on MATH and HotpotQA while reducing inference cost by 27.40%.

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Zhiyuan He, Huiqiang Jiang, Zilong Wang, Yuqing Yang, Luna Qiu, Lili Qiu

MGR: Multi-generator Based Rationalization (2023.acl-long)

Challenge:	Recent advances in Large Language Models (LLMs) have demonstrated significant strides towards achieving artificial general intelligence.
Approach:	They propose a technique termed position engineering which alters the positional information in the prompt without modifying the text itself.
Outcome:	The proposed technique significantly improves on the baseline in retrieval-augmented generation and in-context learning scenarios.

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Wei Liu, Haozhao Wang, Jun Wang, Ruixuan Li, Xinyang Li, YuanKai Zhang, Yang Qiu

Challenge:	Existing approaches to explain NLP models have two key challenges: spurious correlation and degeneration.
Approach:	They propose a rationalization framework using a generator and a predictor to construct a self-explaining NLP model with spurious correlation and degeneration as key challenges.
Outcome:	The proposed method improves the F1 score by 20.9% compared to state-of-the-art methods.

QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization (2021.naacl-main)

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Ming Zhong, Da Yin, Tao Yu, Ahmad Zaidi, Mutethia Mutuma, Rahul Jha, Ahmed Hassan Awadallah, Asli Celikyilmaz, Yang Liu, Xipeng Qiu, Dragomir Radev

Text Generation

Challenge:	Existing work on meeting summarization tasks is limited to short summaries that cover all the content of a meeting.
Approach:	They propose a query-based multi-domain meeting summarization task that generates a single short summary of meetings based on a transcript.
Outcome:	The proposed task is based on 1,808 query-summary pairs over 232 meetings in multiple domains.

N-GLARE: An Non-Generative Latent Representation-Efficient LLM Safety Evaluator (2026.acl-long)

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Zheyu Lin, Jirui Yang, Yukui Qiu, Yubing Bao, Hengqi Guo, Yao Guan

BrainLoc: Brain Signal-Based Object Detection with Multi-modal Alignment (2025.findings-emnlp)

Challenge:	Evaluating the safety robustness of LLMs is critical for their deployment.
Approach:	They propose to use latent representations to characterize hidden layer dynamics by analyzing the APT of latent models and introducing the JSS metric.
Outcome:	The proposed method exploits the APT (Angular-Probabilistic Trajectory) of latent representations and introduces the JSS (Jensen-Shannon Separability) metric.

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Jiaqi Duan, Xiaoda Yang, Kaixuan Luan, Hongshun Qiu, Weicai Yan, Xueyi Zhang, Youliang Zhang, Zhaoyang Li, Donglin Huang, JunYu Lu, Ziyue Jiang, Xifeng Yang

SueNes: A Weakly Supervised Approach to Evaluating Single-Document Summarization via Negative Sampling (2022.naacl-main)

Challenge:	BrainLoc is a lightweight object detection model guided by fMRI signals.
Approach:	They propose a brain-based object detection model guided by fMRI signals . they employ a multi-modal alignment strategy that enhances fmr feature extraction .
Outcome:	The proposed model improves fMRI-based object detection accuracy and convenience.

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Forrest Bao, Ge Luo, Hebi Li, Minghui Qiu, Yinfei Yang, Youbiao He, Cen Chen

SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text Mining (2021.acl-long)

Challenge:	Existing studies on automatic summary evaluation metrics focus on lexical similarity and require a reference summary which is expensive to obtain.
Approach:	They propose to use a weakly supervised summary evaluation approach without the presence of reference summaries to transform existing summarization datasets into corrupted reference summarizers.
Outcome:	The proposed method outperforms baselines and shows that it improves linguistic quality over all metrics.

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Taolin Zhang, Zerui Cai, Chengyu Wang, Minghui Qiu, Bite Yang, Xiaofeng He

Think Both Ways: Teacher-Student Bidirectional Reasoning Enhances MCQ Generation and Distractor Quality (2025.findings-acl)

Challenge:	Existing knowledge-based PLMs are based on linked-entity information, but they only use linked-enemy information as auxiliary information.
Approach:	They propose to integrate semantic knowledge from neighbours of linked-entity into a medical PLM that integrates heterogeneous-entities into the homogeneously neighbouring entity structure.
Outcome:	Experiments show that SMedBERT outperforms baselines in knowledge-intensive Chinese medical tasks.

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Yimiao Qiu, Yang Deng, Quanming Yao, Zhimeng Zhang, Zhiang Dong, Chang Yao, Jingyuan Chen

EmoAgent: Assessing and Safeguarding Human-AI Interaction for Mental Health Safety (2025.emnlp-main)

Challenge:	Existing methods for generating high-quality MCQs struggle with contextual relevance and plausible distractors.
Approach:	They propose a framework that integrates bidirectional reasoning perspectives to generate contextually relevant questions and plausible distractors while student reasoning evaluates question clarity and the misleading nature of distractors.
Outcome:	The proposed framework outperforms existing methods in generating text-grounded questions and high-quality distractors for narrative contexts.

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Jiahao Qiu, Yinghui He, Xinzhe Juan, Yimin Wang, Yuhan Liu, Zixin Yao, Yue Wu, Xun Jiang, Ling Yang, Mengdi Wang

Submodular-based In-context Example Selection for LLMs-based Machine Translation (2024.lrec-main)

Challenge:	EmoAgent evaluates and mitigates mental health hazards in human-AI interactions, especially for vulnerable human users with psychological disorders.
Approach:	EmoAgent is a multi-agent AI framework designed to evaluate and mitigate mental health hazards in human-AI interactions.
Outcome:	EmoAgent evaluates and mitigates mental health hazards in human-AI interactions.

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Baijun Ji, Xiangyu Duan, Zhenyu Qiu, Tong Zhang, Junhui Li, Hao Yang, Min Zhang

Machine Translation

Challenge:	Prior studies have focused on the role of well-chosen examples in in-context learning .
Approach:	They propose to use multiple translational factors for in-context example selection by using monotone submodular function maximization.
Outcome:	The proposed approach outperforms random selection and robust single-factor baselines across various NLP tasks.

PKU-SafeRLHF: Towards Multi-Level Safety Alignment for LLMs with Human Preference (2025.acl-long)

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Jiaming Ji, Donghai Hong, Borong Zhang, Boyuan Chen, Josef Dai, Boren Zheng, Tianyi Alex Qiu, Jiayi Zhou, Kaile Wang, Boxun Li, Sirui Han, Yike Guo, Yaodong Yang

Challenge:	Using large-scale annotation data, large language models can generate noise, errors and biases, leading to unexpected behaviours.
Approach:	They propose a dataset to promote safety alignment in large language models . they separate helpfulness and harmlessness annotations for question-answering pairs .
Outcome:	The proposed dataset provides 44.6k prompts and 265k question-answer pairs with safety meta-labels for 19 harm categories and three severity levels, with answers generated by Llama-family models.

Explicit and Implicit Data Augmentation for Social Event Detection (2025.acl-long)

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Congbo Ma, Yuxia Wang, Jia Wu, Jian Yang, Jing Du, Zitai Qiu, Qing Li, Hu Wang, Preslav Nakov

DocAsRef: An Empirical Study on Repurposing Reference-based Summary Quality Metrics as Reference-free Metrics (2023.findings-emnlp)

Challenge:	Social event detection relies on labeled data, but annotation is costly and labor-intensive.
Approach:	They propose a plug-and-play dual augmentation framework that combines explicit text-based and implicit feature-space augmentation to enhance data diversity and model robustness.
Outcome:	The proposed framework outperforms the best baseline model by 17.67% on the Twitter2012 dataset and 15.57% on the twitter2018 dataset in terms of the average F1 score.

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Forrest Bao, Ruixuan Tu, Ge Luo, Yinfei Yang, Hebi Li, Minghui Qiu, Youbiao He, Cen Chen

LoRASC: Expressive and Generalizable Low-rank Adaptation for Large Models via Slow Cascaded Learning (2024.findings-emnlp)

Challenge:	Existing reference-based metrics are limited by their reliance on human input.
Approach:	They propose to adapt some reference-based metrics to assess system summary against human-written references.
Outcome:	The proposed model outperforms reference-based metrics on two datasets and is comparable to reference-free metrics.

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Siwei Li, Yifan Yang, Yifei Shen, Fangyun Wei, Zongqing Lu, Lili Qiu, Yuqing Yang

CoLLiE: Collaborative Training of Large Language Models in an Efficient Way (2023.emnlp-demo)

Challenge:	Existing low-rank adaptations have limited expressiveness, a tendency to overfit, and sensitivity to hyperparameter settings.
Approach:	They propose a technique to enhance LoRA’s expressiveness and generalization capabilities while preserving its training efficiency.
Outcome:	The proposed method outperforms baselines, mitigates overfitting, enhances model stability, and improves OOD robustness.

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Kai Lv, Shuo Zhang, Tianle Gu, Shuhao Xing, Jiawei Hong, Keyu Chen, Xiaoran Liu, Yuqing Yang, Honglin Guo, Tengxiao Liu, Yu Sun, Qipeng Guo, Hang Yan, Xipeng Qiu

ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development (2026.findings-acl)

Challenge:	Large language models (LLMs) are increasingly pivotal in a wide range of tasks . however, the resources required for training these models necessitate efficient solutions .
Approach:	They propose a library that facilitates collaborative training of large language models . they use 3D parallelism, parameter-efficient fine-tuning methods and optimizers .
Outcome:	The proposed library has proven superior training efficiency in comparison with prevalent solutions in pre-training and fine-tuning scenarios.

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Jie Yang, Honglin Guo, Li Ji, Jiazheng Zhou, Rui Zheng, Zhikai Lei, Shuo Zhang, Zhiheng Xi, Shichun Liu, Yuxin Wang, Bo Wang, Yining Zheng, Tao Gui, Xipeng Qiu

TinyAlign: Boosting Lightweight Vision-Language Models by Mitigating Modal Alignment Bottlenecks (2026.findings-acl)

Challenge:	Large Language Models (LLMs) have redefined the role of AI in software engineering . current benchmarks focus on localized code generation, but neglect dynamic, full-process requirements of real-world engineering.
Approach:	They propose a benchmark to evaluate agentic backend coding within a realistic, executable workflow.
Outcome:	The ABC-Bench benchmark evaluates agentic backend coding within a realistic, executable workflow.

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Yuanze Hu, Xinyu Wang, Zhichao Yang, Gen Li, Ye Qiu, Zhaoxin Fan, Yifan Sun, Wenjun wu, Jin Dong, Xiaotie Deng

LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression (2024.findings-acl)

Challenge:	Lightweight Vision-Language Models (VLMs) are indispensable for resource-constrained applications.
Approach:	They propose a framework that retrieves context from a memory bank to enhance alignment . they propose EMI-based approach to align vision and language models .
Outcome:	The proposed framework reduces training loss, accelerates convergence, and enhances task performance with negligible computational overhead.

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Zhuoshi Pan, Qianhui Wu, Huiqiang Jiang, Menglin Xia, Xufang Luo, Jue Zhang, Qingwei Lin, Victor Rühle, Yuqing Yang, Chin-Yew Lin, H. Vicky Zhao, Lili Qiu, Dongmei Zhang

DFAMS: Dynamic-flow guided Federated Alignment based Multi-prototype Search (2026.acl-long)

Challenge:	Existing approaches to compress prompts only leverage unidirectional context, causing suboptimal results.
Approach:	They propose a task-agnostic prompt compression method that takes tokens from context . they use a Transformer encoder to capture all essential information needed for prompt compression .
Outcome:	The proposed method is 3x-6x faster than existing prompt compression methods and faster than baselines.

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Zhibang Yang, Xinke Jiang, Rihong Qiu, Ruiqing Li, Yihang Zhang, Yue Fang, Yongxin Xu, Hongxin Ding, Xu Chu, Junfeng Zhao, Yasha Wang

New Terms, New Toxicity: Consensus-based Chinese Neologism Toxicity Detection via Search-Augmented LLMs (2026.acl-long)

Challenge:	Existing methods for ambiguous queries struggle to retrieve high-quality documents . DFAMS outperforms advanced FR methods by 14.37% in knowledge classification accuracy .
Approach:	They propose a framework that leverages dynamic information flow to identify latent query intents and construct semantically aligned knowledge partitions for accurate retrieval across heterogeneous sources.
Outcome:	The proposed framework outperforms existing methods in classification accuracy and retrieval recall tests.

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Shiyao Cui, QingLin Zhang, Di Wang, Yida Lu, Zhexin Zhang, Jinhua Gao, Jinglin Yang, Min He, Han Qiu, Minlie Huang

Decoder-Only LLMs can be Masked Auto-Encoders (2025.acl-short)

Challenge:	Neologisms can foster new linguistic consensus by stabilizing shared meanings and usage in common communicative norms.
Approach:	They propose a taxonomy that captures the origins and consensus-verification criteria of toxic neologisms . they propose 'SeTox' framework that integrates real-time web context for naeologim detection .
Outcome:	The proposed framework outperforms large-scale models in detecting neologism toxicity.

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Dan Qiao, Yuan Gao, Zheming Yang, Di Yang, Ziheng Wu, Pengcheng Lu, Minghui Qiu, Juntao Li, Min Zhang

BOOKWORLD: From Novels to Interactive Agent Societies for Story Creation (2025.acl-long)

Challenge:	Modern NLP workflows require different models for generation and embedding tasks.
Approach:	They propose a method that transforms an LLM into a Uni-Directional Masked Auto-Encoder.
Outcome:	The proposed method achieves state-of-the-art under unsupervised conditions with merely 100 training steps.

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Yiting Ran, Xintao Wang, Tian Qiu, Jiaqing Liang, Yanghua Xiao, Deqing Yang

Challenge:	Recent advances in large language models (LLMs) have enabled social simulation through multi-agent systems.
Approach:	They propose a system for constructing and simulating book-based multi-agent societies that simulates established fictional worlds and characters.
Outcome:	The proposed system generates high-quality stories while maintaining fidelity to the source books, surpassing previous methods with a win rate of 75.36%.

OSCR-Attack: One-Shot Character Level Attacks through Self-Optimizing Continuous Relaxation (2026.findings-acl)

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Lingyi Kong, Zhuo Liu, Zhanghao Hu, Qilong Qiu, Yutao Yang, Jingjing Xue, Zheng Wang, Lin Gui, Feiping Nie

Robustness in NLP

Challenge:	Character-level adversarial attacks preserve semantics but are costly and inefficient . generative LLMs are gaining popularity due to their uncertainty and vulnerability to textual adversarials .
Approach:	They propose an end-to-end framework that transforms discrete choices into continuous representations and a conflict resolution strategy that maps them back into discrete insertion operations.
Outcome:	The proposed framework improves ASR by 21.45% points and accelerates the attack by 3.66 times compared to baselines.

Language Models Resist Alignment: Evidence From Data Compression (2025.acl-long)

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Jiaming Ji, Kaile Wang, Tianyi Alex Qiu, Boyuan Chen, Jiayi Zhou, Changye Li, Hantao Lou, Josef Dai, Yunhuai Liu, Yaodong Yang

Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts (2023.emnlp-main)

Challenge:	Large language models (LLMs) may exhibit undesirable behaviors due to the inevitable biases and harmful content present in training.
Approach:	They propose to investigate the elasticity of large language models by examining their performance.
Outcome:	The proposed model performance declines rapidly before reverting to the pre-training distribution, the authors show . the proposed model weight and code are available at pku-lm-res ist-alignment.github.io.

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Tengxiao Liu, Qipeng Guo, Yuqing Yang, Xiangkun Hu, Yue Zhang, Xipeng Qiu, Zheng Zhang

Meta Distant Transfer Learning for Pre-trained Language Models (2021.emnlp-main)

Challenge:	Existing methods for large language models (LLMs) have been used to prompt different reasoning thoughts, such as Chain of Thought and Program of Though.
Approach:	They propose a framework that prompts large language models with diverse reasoning thoughts by iterating between different prompting methods.
Outcome:	The proposed framework is able to generate multiple reasoning thoughts in 10 popular math reasoning datasets and is orthogonal to recent work that makes improvements on single reasoning methods and can generalise to logical reasoning domain.

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Chengyu Wang, Haojie Pan, Minghui Qiu, Jun Huang, Fei Yang, Yin Zhang

Reward Generalization in RLHF: A Topological Perspective (2025.findings-acl)

Challenge:	Notable PLMs are available for text classification tasks, but performance of PLM on downstream tasks may be limited by the availability of training set.
Approach:	They propose a meta-learning framework to learn the transferable knowledge across tasks using PLMs.
Outcome:	The proposed framework outperforms baselines on seven datasets and is task-agnostic and unbiased.

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Tianyi Alex Qiu, Fanzhi Zeng, Jiaming Ji, Dong Yan, Kaile Wang, Jiayi Zhou, Yang Han, Josef Dai, Xuehai Pan, Yaodong Yang

Challenge:	Existing alignment methods share a common topology of information flow, but their alternatives have not been thoroughly explored.
Approach:	They propose a theory of reward generalization in reinforcement learning from human feedback . they propose induced Bayesian networks to model the impact of dataset topologies on reward generalisation .
Outcome:	The proposed method achieves an average win rate of 65% on three NLP tasks.

Self-Reflective Generation at Test Time (2026.acl-long)

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Jian Mu, Qixin Zhang, Zhiyong Wang, Menglin Yang, Shuang Qiu, Chengwei Qin, Zhongxiang Dai, Yao Shu

LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression (2024.acl-long)

Challenge:	Existing self-reflection mechanisms are reactive and inefficient for large language models . a fundamental tension persists between the ability to execute complex multi-step reasoning and the ability of the model to generate coherent traces.
Approach:	They propose a test-time framework that reflects before generating at uncertain points . SRGen utilizes dynamic entropy thresholding to identify high-uncertainty tokens .
Outcome:	The proposed framework can significantly strengthen large language models' reasoning process.

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Huiqiang Jiang, Qianhui Wu, Xufang Luo, Dongsheng Li, Chin-Yew Lin, Yuqing Yang, Lili Qiu