Papers by Ke Chen

P-INT: A Path-based Interaction Model for Few-shot Knowledge Graph Completion (2021.findings-emnlp)

Challenge:	Large Vision-Language Models are hindered by a systemic efficiency barrier known as visual token dominance.
Approach:	They propose a systematic taxonomy of efficiency techniques structured around the inference lifecycle . they examine visual encoding, prefilling, and decoding to understand bottlenecks .
Outcome:	The proposed techniques reveal how upstream decisions dictate downstream bottlenecks . the proposed techniques include hybrid compression and modality-aware decoding .

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Jingwen Xu, Jing Zhang, Xirui Ke, Yuxiao Dong, Hong Chen, Cuiping Li, Yongbin Liu

Magnet: Multi-turn Tool-use Data Synthesis and Distillation via Graph Translation (2025.acl-long)

Challenge:	Existing methods to encode and match entity pairs have only a few observed reference entity pairs.
Approach:	They propose a model that infers and leverages paths that can expressively encode the relation of two entities.
Outcome:	The proposed model outperforms the state-of-the-art models by 11.2– 14.2% in terms of Hits@1.

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Fan Yin, Zifeng Wang, I-Hung Hsu, Jun Yan, Ke Jiang, Yanfei Chen, Jindong Gu, Long Le, Kai-Wei Chang, Chen-Yu Lee, Hamid Palangi, Tomas Pfister

Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding (2024.acl-long)

Challenge:	Large language models have been shown to be effective in multi-turn interactions . however, their performance may be limited in complex, multi-turned interactions involving users and multiple tools.
Approach:	They propose a framework for synthesizing high-quality training trajectories to enhance the function calling capability of large language model agents in multi-turn conversations with humans.
Outcome:	The proposed model outperforms the teacher model by 68.01 on BFCL-v3 and 73.30 on ToolQuery.

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Jun Zhang, Jue Wang, Huan Li, Lidan Shou, Ke Chen, Gang Chen, Sharad Mehrotra

VoiceTextBlender: Augmenting Large Language Models with Speech Capabilities via Single-Stage Joint Speech-Text Supervised Fine-Tuning (2025.naacl-long)

Challenge:	Existing methods for accelerating Large Language Models have been criticized for their inference costs and inefficient decoding.
Approach:	They propose a self-speculative decoding approach for accelerating Large Language Models without an auxiliary model.
Outcome:	The proposed method achieves a speedup of up to 1.99 with no additional neural network training and no extra memory footprint.

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Yifan Peng, Krishna C Puvvada, Zhehuai Chen, Piotr Zelasko, He Huang, Kunal Dhawan, Ke Hu, Shinji Watanabe, Jagadeesh Balam, Boris Ginsburg

TS-Align: A Teacher-Student Collaborative Framework for Scalable Iterative Finetuning of Large Language Models (2024.findings-emnlp)

Challenge:	Recent studies have augmented large language models (LLMs) with speech capabilities, leading to the development of speech language models.
Approach:	They propose a single-stage joint speech-text SFT approach for training SpeechLMs . their model combines text-only SFT data with three types of speech-related data .
Outcome:	The proposed model outperforms previous SpeechLMs on speech-based QA tasks while maintaining original speech-only capabilities.

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Chen Zhang, Chengguang Tang, Dading Chong, Ke Shi, Guohua Tang, Feng Jiang, Haizhou Li

Towards Medical Complex Reasoning with LLMs through Medical Verifiable Problems (2025.findings-acl)

Challenge:	Mainstream approaches to aligning large language models heavily rely on human preference data.
Approach:	They propose a framework that fine-tunes a policy model using pairwise feedback data automatically mined from its outputs.
Outcome:	The proposed framework outperforms the base model with an average win rate of 69.7% across seven conversational or instruction-following datasets.

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Junying Chen, Zhenyang Cai, Ke Ji, Xidong Wang, Wanlong Liu, Rongsheng Wang, Benyou Wang

Reasoning

Challenge:	OpenAI o1 has been a significant milestone in large language model development . however, most research in reasoning has focused on mathematical tasks . medical domains require robust reasoning to provide reliable answers .
Approach:	They propose a method to verify medical reasoning using a medical verifier . they also propose RL and reinforcement learning to enhance reasoning .
Outcome:	The proposed method outperforms general and medical-specific baselines using only 40K verifiable problems.

Knowledge-augmented Self-training of A Question Rewriter for Conversational Knowledge Base Question Answering (2022.findings-emnlp)

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Xirui Ke, Jing Zhang, Xin Lv, Yiqi Xu, Shulin Cao, Cuiping Li, Hong Chen, Juanzi Li

Natural Language Interfaces

Challenge:	Recent rise of conversational applications has promoted the development of conversation KBQA (ConvKBQA).
Approach:	They propose a framework to produce a full-fledged rewritten question based on conversation history and then reason the answer by existing single-turn KBQA models.
Outcome:	The proposed framework produces a full-fledged rewritten question based on the conversation history and reasoned the answer by existing single-turn KBQA models.

Unlocking LLMs’ Self-Improvement Capacity with Autonomous Learning for Domain Adaptation (2025.findings-acl)

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Ke Ji, Junying Chen, Anningzhe Gao, Wenya Xie, Xiang Wan, Benyou Wang

EVM-QuestBench: An Execution-Grounded Benchmark for Natural-Language Transaction Code Generation (2026.acl-long)

Challenge:	Existing models that use self-supervised and instruction fine-tuning can be trained using unlabeled corpora.
Approach:	They propose to use unlabeled target corpora to adapt large language models to new domains . they propose to employ self-supervised pre-training and instruction fine-tuning methods .
Outcome:	The proposed model can adapt to new domains using only a large amount of unlabeled target corpora.

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Pei Yang, Wanyi Chen, Ke Wang, Lynn Ai, Eric Yang, Tianyu Shi

Programming Languages in NLP

Challenge:	Existing evaluations of large language models overlook execution accuracy and safety.
Approach:	They propose an execution-grounded benchmark for natural-language transaction-script generation on EVM-compatible chains.
Outcome:	The proposed benchmark finds large performance gaps in the models with 5 independent rounds.

xDial-Eval: A Multilingual Open-Domain Dialogue Evaluation Benchmark (2023.findings-emnlp)

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Chen Zhang, Luis D’Haro, Chengguang Tang, Ke Shi, Guohua Tang, Haizhou Li

Natural Language Interfaces

Challenge:	Currently, human evaluation is the most reliable way to holistically judge the quality of the dialogue.
Approach:	They propose to use English dialogue evaluation metrics to generalize them to other languages.
Outcome:	The proposed metrics outperform OpenAI’s ChatGPT in terms of average Pearson correlations over all datasets and languages.

Towards Bridging the Reward-Generation Gap in Direct Alignment Algorithms (2026.findings-acl)

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Zeguan Xiao, Yun Chen, Jian Yang, Guanhua Chen, Ke Tang

Challenge:	Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, including instruction following, mathematical problem solving, and coding generation.
Approach:	They propose a method that truncates both preferred and dispreferred responses to match the shorter one’s length.
Outcome:	The proposed approach improves over standard implementations and achieves 11.8 points in AlpacaEval 2 and overall improvements across downstream tasks.

SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis (2025.findings-naacl)

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Hengxing Cai, Xiaochen Cai, Junhan Chang, Sihang Li, Lin Yao, Wang Changxin, Zhifeng Gao, Hongshuai Wang, Li Yongge, Mujie Lin, Shuwen Yang, Jiankun Wang, Mingjun Xu, Jin Huang, Xi Fang, Jiaxi Zhuang, Yuqi Yin, Yaqi Li, Changhong Chen, Zheng Cheng, Zifeng Zhao, Linfeng Zhang, Guolin Ke

Challenge:	Existing benchmarks fail to adequately evaluate the proficiency of Large Language Models (LLMs) Existing standards do not cover the skills needed to evaluate LLMs in scientific literature analysis.
Approach:	They propose a benchmark to evaluate the proficiency of large language models in scientific literature analysis.
Outcome:	SciAssess evaluates 11 LLMs on multiple tasks across scientific fields.

QiMeng-PRepair: Precise Code Repair via Edit-Aware Reward Optimization (2026.acl-long)

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Changxin Ke, Rui Zhang, Jiaming Guo, Yuanbo Wen, Li Ding, Shuo Wang, Xuyuan Zhu, Xiong Peng, Di Huang, Zidong Du, Xing Hu, Qi Guo, Yunji Chen

Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale (2024.emnlp-main)

Challenge:	Existing approaches to program repair are based on correctness alone.
Approach:	They propose a framework that mitigates over-editing and improves repair accuracy by generating buggy programs and re-edits.
Outcome:	The proposed framework improves repair precision by 31.4% under fix1@1, a metric that considers repair correctness and extent, and significantly increases decoding throughput when combined with speculative editing.

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Junying Chen, Chi Gui, Ruyi Ouyang, Anningzhe Gao, Shunian Chen, Guiming Chen, Xidong Wang, Zhenyang Cai, Ke Ji, Xiang Wan, Benyou Wang

Transfer-Aware Data Selection for Domain Adaptation in Text Retrieval (2025.findings-emnlp)

Challenge:	Multimodal large language models (MLLMs) lack visual knowledge in medical applications due to data privacy concerns and high annotation costs.
Approach:	They refined medical image-text pairs from PubMed and employed MLLMs (GPT-4V) to denoise and reformat the data.
Outcome:	The proposed model significantly improves the MMMU Health & Medicine track and shows that it can be used in multimodal scenarios.

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Linzhu Yu, Huan Li, Ke Chen, Lidan Shou

Beyond Logits: Aligning Feature Dynamics for Effective Knowledge Distillation (2025.acl-long)

Challenge:	Existing methods to improve domain adaptation do not guarantee improved adaptability, but may negatively impact model performance.
Approach:	They propose a framework that can effectively improve model adaptability by selecting beneficial data without evaluating all source data.
Outcome:	The proposed framework improves model adaptability by selecting beneficial data without evaluating all source data.

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Guoqiang Gong, Jiaxing Wang, Jin Xu, Deping Xiang, Zicheng Zhang, Leqi Shen, Yifeng Zhang, JunhuaShu JunhuaShu, ZhaolongXing ZhaolongXing, Zhen Chen, Pengzhang Liu, Ke Zhang

Conditional Neural Generation using Sub-Aspect Functions for Extractive News Summarization (2020.findings-emnlp)

Challenge:	Knowledge distillation (KD) compresses large language models into lightweight versions called student models.
Approach:	They propose to align the entire feature dynamics between teacher and student models by using two additional loss terms to achieve this.
Outcome:	The proposed method matches the entire feature dynamics between teacher and student models rather than just the final states.

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Zhengyuan Liu, Ke Shi, Nancy Chen

Text Generation

Challenge:	Recent advances in text summarization have overcome position bias in news articles . however, there are long-standing, unresolved challenges in extractive summarizing .
Approach:	They propose a neural framework that can flexibly control summary generation by introducing a set of sub-aspect functions.
Outcome:	The proposed framework can flexibly control summary generation by introducing sub-aspect functions . extracted summaries with minimal position bias are comparable with standard models .

Response-G1: Explicit Scene Graph Modeling for Proactive Streaming Video Understanding (2026.acl-long)

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Ke Ma, Jiaqi Tang, Bin Guo, Xueting Han, Ruonan Xu, Qingfeng He, Ziheng Wang, Xu Wang, Qifeng Chen, Zhiwen Yu, Yunhao Liu

Diversifying Question Generation over Knowledge Base via External Natural Questions (2024.lrec-main)

Challenge:	Existing methods for streaming video understanding are query-agnostic and implicitly model video evidence.
Approach:	They propose a framework that establishes explicit, structured alignment between the accumulated video evidence and the query’s expected response conditions via scene graphs.
Outcome:	The proposed model achieves more interpretable and accurate response timing decisions on both proactive and reactive tasks.

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Shasha Guo, Jing Zhang, Xirui Ke, Cuiping Li, Hong Chen

Text Generation

Challenge:	Existing methods on knowledge base question generation focus on refining the quality of a single generated question.
Approach:	They propose a new diversity evaluation metric which measures the diversity among top-k generated questions for each instance while ensuring their relevance to the ground truth.
Outcome:	The proposed model outperforms pre-trained language model baselines and text-davinci-003 in diversity while achieving comparable performance with ChatGPT.

PACE: Prefix-Protected and Difficulty-Aware Compression for Efficient Reasoning (2026.findings-acl)

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Ruixiang Feng, Yuntao Wen, Silin Zhou, Ke Shi, Yifan Wang, Ran Le, Zhenwei An, Zongchao Chen, Chen Yang, Guangyue Peng, Yiming Jia, Dongsheng Wang, Tao Zhang, Lisi Chen, Yang Song, Shen Gao, Shuo Shang

A Reasoner for Real-World Event Detection: Scaling Reinforcement Learning via Adaptive Perplexity-Aware Sampling Strategy (2025.emnlp-industry)

Challenge:	Existing LRMs often suffer from "overthinking" and excessively long reasoning traces . a dual-level framework for length compression of LRM is proposed .
Approach:	They propose a framework for prefix-protected and difficulty-aware compression under hierarchical supervision.
Outcome:	The proposed framework reduces token usage while improving accuracy on math benchmarks.

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Xiaoyun Zhang, Jingqing Ruan, Xing Ma, Yawen Zhu, Jiansong Chen, Ke Zeng, Xunliang Cai

Dialogue Systems & Conversational Agents

Challenge:	Existing methods for abnormal event detection face two predominant limitations . existing methods rely on specialized small models and are limited by performance bottlenecks .
Approach:	They propose a framework that leverages the advanced reasoning capabilities of large language models for abnormal event detection.
Outcome:	The proposed framework achieves the highest F1 score and an average improvement of 9.59% in OOD transfer tests.

RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions (2025.emnlp-main)

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Wanlong Liu, Junying Chen, Ke Ji, Li Zhou, Wenyu Chen, Benyou Wang

Information Retrieval

Challenge:	Retrieval-Augmented Generation (RAG) has emerged as a key paradigm for enhancing large language models by incorporating external knowledge.
Approach:	They propose a method for synthesizing diverse and high-quality RAG instruction data based on any source corpus.
Outcome:	The proposed method outperforms existing methods in multiple tasks and achieves strong zero-shot performance.

Multilingual Neural RST Discourse Parsing (2020.coling-main)

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Zhengyuan Liu, Ke Shi, Nancy Chen

HybridKV: Hybrid KV Cache Compression for Efficient Multimodal Large Language Model Inference (2026.acl-long)

Challenge:	Existing studies on text discourse parsing for English are limited due to the lack of annotated data.
Approach:	They propose to use multilingual vector representations and segment-level translation to establish a neural, cross-lingual discourse parser.
Outcome:	The proposed model achieves state-of-the-art on cross-lingual, document-level discourse parsing on all sub-tasks.

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Bowen Zeng, Feiyang Ren, Jun Zhang, Xiaoling Gu, Ke Chen, Lidan Shou, Huan Li

Structure-Aware Zero-Shot Relational Learning for Knowledge Graphs without External Knowledge (2026.findings-acl)

Challenge:	Multimodal Large Language Models (MLLMs) are hindered by the rapid growth of key–value (KV) caches.
Approach:	They propose a hybrid KV cache compression framework that reduces KV memory by 7.9 and speeds up decoding by 1.52.
Outcome:	Experiments on 11 multimodal benchmarks show that HYBRIDKV cuts KV cache memory by 7.9 and speeds up decoding by 1.52.

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Kuan Xu, Baoxin Zhang, Shuyue Fan, Ming Chen, Zhipeng Ke, Jian Yu, Xuezhong Zhou

T2A-Feedback: Improving Basic Capabilities of Text-to-Audio Generation via Fine-grained AI Feedback (2025.acl-long)

Challenge:	Existing methods for Zero-shot Relational Learning depend on external knowledge, resulting in increased annotation costs and limited practical applicability.
Approach:	They propose a structure-aware paradigm that performs ZRL without external knowledge . it leverages intrinsic structural patterns in KGs to bridge semantic correlations for new relations with existing ones.
Outcome:	The proposed paradigm achieves 10.66% improvement in MRR while reducing annotation costs and enhancing practical applicability on three real-world benchmarks.

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Zehan Wang, Ke Lei, Chen Zhu, Jiawei Huang, Sashuai Zhou, Luping Liu, Xize Cheng, Shengpeng Ji, Zhenhui Ye, Tao Jin, Zhou Zhao

SpeechIQ: Speech-Agentic Intelligence Quotient Across Cognitive Levels in Voice Understanding by Large Language Models (2025.acl-long)

Challenge:	Text-to-audio (T2A) models still struggle to satisfy human preferences for prompt-following and acoustic quality when generating complex multi-event audio.
Approach:	They propose to use AI feedback learning to enhance basic capabilities of text-to-audio models . they use a large audio preference dataset to evaluate the model's capabilities .
Outcome:	The proposed model improves in simple and complex scenarios with AI feedback learning.

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Zhen Wan, Chao-Han Huck Yang, Yahan Yu, Jinchuan Tian, Sheng Li, Ke Hu, Zhehuai Chen, Shinji Watanabe, Fei Cheng, Chenhui Chu, Sadao Kurohashi

Beyond Templates: Dynamic Adaptation of Reasoning Demonstrations via Feasibility-Aware Exploration (2026.findings-acl)

Challenge:	SIQ quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models.
Approach:	They propose a human cognition-inspired evaluation pipeline for voice understanding large language models (LLM_Voice) that quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models.
Outcome:	The proposed framework quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models, identifies annotation errors in existing benchmarks, and detects hallucinations in LLM_Voice.

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Yong Wu, Weihang Pan, Ke Li, Chen Binhui, Ping Li, Binbin Lin

C3KG: A Chinese Commonsense Conversation Knowledge Graph (2022.findings-acl)

Challenge:	Existing reasoning datasets that are designed for powerful LLMs often lead to degraded performance when directly applied to weaker models.
Approach:	They propose a data adaptation framework that bridges the capability gap between expert reasoning trajectories and diverse SLMs by employing a selective imitation strategy guided by step-wise adaptability estimation via solution simulation.
Outcome:	The proposed framework improves generalization and data efficiency over static fine-tuning and can be applied to large models with limited model capacity.

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Dawei Li, Yanran Li, Jiayi Zhang, Ke Li, Chen Wei, Jianwei Cui, Bin Wang

Natural Language Interfaces

Challenge:	Existing commonsense knowledge bases organize tuples in an isolated manner, causing problems for chatbots .
Approach:	They create a Chinese commonsense conversation knowledge graph which integrates social commonsensm and dialog flow information.
Outcome:	The proposed graph incorporates social commonsense knowledge and dialog flow information.

GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement (2025.acl-long)

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Yifan Yang, Zheshu Song, Jianheng Zhuo, Mingyu Cui, Jinpeng Li, Bo Yang, Yexing Du, Ziyang Ma, Xunying Liu, Ziyuan Wang, Ke Li, Shuai Fan, Kai Yu, Wei-Qiang Zhang, Guoguo Chen, Xie Chen

Knowledge-Infused Multi-Bit Watermarking for RAG Knowledge Bases (2026.findings-acl)

Challenge:	GigaSpeech 2 is a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages.
Approach:	They propose a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages and an automated pipeline for data crawling, transcription, and label refinement.
Outcome:	The proposed corpus reduces the word error rate for Thai, Indonesian, and Vietnamese on a realistic YouTube test set by 25% to 40% compared to Whisper large-v3.

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Ke Yang, Shuguang Yuan, Jing Yu, Chi Chen

Pyramid: A Layered Model for Nested Named Entity Recognition (2020.acl-main)

Challenge:	Existing RAG watermarking methods are limited in their encoding capacity and potential degradation of performance or knowledge quality.
Approach:	They propose knowledge-infused and multi-bit watermarking (KMW) for RAG knowledge bases by benign knowledge completion and a tailored generative watermark algorithm.
Outcome:	The proposed method extracts watermarks from adversarial RAGs while remaining stealthy and secure.

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Jue Wang, Lidan Shou, Ke Chen, Gang Chen

Semantic Matching for Sequence-to-Sequence Learning (2020.findings-emnlp)

Challenge:	Named Entity Recognition (NER) is a fundamental NLP task.
Approach:	They propose a pyramid-like layered model for Nested Named Entity Recognition . token or text region embeddings are recursively inputted into L flat NER layers .
Outcome:	The proposed model achieves state-of-the-art F1 scores in nested NER on ACE-2004, ACE 2005, GENIA, and NNE.

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Ruiyi Zhang, Changyou Chen, Xinyuan Zhang, Ke Bai, Lawrence Carin

DeKeyNLU: Enhancing Natural Language to SQL Generation through Task Decomposition and Keyword Extraction (2025.findings-emnlp)

Challenge:	Existing models for sequence-to-sequence semantic matching do not match target sentences . Optimal partial transport (OPT) is a technique that partially matches semantically meaningful words between source and partial target sequences.
Approach:	They propose a semantic matching scheme based on the Optimal Partial Transport (OPT) to match semantically meaningful words between source and partial target sequences.
Outcome:	Extensive experiments show that the proposed approach improves over sequence-to-sequence models.

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Jian Chen, Zhenyan Chen, Xuming Hu, Peilin Zhou, Yining Hua, Han Fang, Cissy Hing Yee Choy, Xinmei Ke, Jingfeng Luo, Zixuan Yuan

Programming Languages in NLP

Challenge:	NL2SQL provides a model-centric paradigm that simplifies database access for non-technical users . challenges such as inaccurate task decomposition and keyword extraction remain major bottlenecks .
Approach:	They propose a RAG-based NL2SQL pipeline that employs three modules for query understanding, entity retrieval, and generation to improve SQL generation accuracy.
Outcome:	The proposed pipeline improves the accuracy of query generation on BIRD and Spider datasets.

Learning to Maximize Mutual Information for Chain-of-Thought Distillation (2024.findings-acl)

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Xin Chen, Hanxian Huang, Yanjun Gao, Yi Wang, Jishen Zhao, Ke Ding

PAMN: Multi-phase Correlation Modeling for Contrast-Enhanced 3D Medical Image Retrieval (2025.findings-emnlp)

Challenge:	Knowledge distillation is a technique of transferring knowledge from large, complex models to smaller ones.
Approach:	They propose a method utilizing chain-of-thought distillation to transfer knowledge from large, complex models to smaller ones by maximizing mutual information of the representation features of the two tasks.
Outcome:	The proposed method outperforms the state-of-the-art knowledge distillation method on four datasets.

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Haonan Tong, Ke Liu, Chuang Zhang, Xinglin Zhang, Tao Chen, Jenq-Neng Hwang, Lei Li

QiMeng-Attention: SOTA Attention Operator is generated by SOTA Attention Algorithm (2025.findings-acl)

Challenge:	Current 3D medical imaging models focus on spatial features, neglecting phase-specific progression detailed in clinical reports.
Approach:	They propose a framework that fuses imaging phases with clinical text to enhance 3D medical image retrieval.
Outcome:	The proposed framework outperforms state-of-the-art models on a phase-series dataset of 12,230 hospital CT scans.

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Qirui Zhou, Shaohui Peng, Weiqiang Xiong, Haixin Chen, Yuanbo Wen, Haochen Li, Ling Li, Qi Guo, Yongwei Zhao, Ke Gao, Ruizhi Chen, Yanjun Wu, Zhao Chen, Yunji Chen

Programming Languages in NLP

Challenge:	Existing LLMs cannot comprehend the complex data flow and computation process of the attention operator and utilize low-level primitive to exploit GPU performance.
Approach:	They propose an LLM-friendly Thinking Language (LLM-TL) that can decouple the generation of high-level optimization logic and low-level implementation on GPU and enhance LLMs’ understanding of attention operator.
Outcome:	The proposed method outshines existing LLMs on A100, RTX8000, and T4 GPUs, achieving a speed-up of up to 35.16.

You Can Have a Second Chance: Unbiased and Multi-bit Watermarking for Diffusion Language Models with Regret-based Remasking (2026.acl-long)

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Ke Yang, Dongyang Liang, Jing Yu, Shuguang Yuan, Chi Chen

When to Continue Thinking: Adaptive Thinking Mode Switching for Efficient Reasoning (2025.findings-emnlp)

Challenge:	Existing sequential LLMs cannot be directly applied to DLMs, as their generation order is arbitrary.
Approach:	They propose a stability-aware constraint that allows watermarking only in stable contexts and a bit-controlled, unbiased modulation to preserve the original DLM output distribution.
Outcome:	The proposed scheme achieves stable watermarking with minimal quality impact while maintaining high detection accuracy and multi-bit capacity.

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Xiaoyun Zhang, Jingqing Ruan, Xing Ma, Yawen Zhu, Haodong Zhao, Hao Li, Jiansong Chen, Ke Zeng, Xunliang Cai

Enhancing Efficiency and Exploration in Reinforcement Learning for LLMs (2025.emnlp-main)

Challenge:	Large reasoning models (LRMs) incur excessive computational overhead due to redundant reasoning, especially on simple tasks.
Approach:	They propose an Adaptive Self-Recovery Reasoning framework that suppresses unnecessary reasoning and enables implicit recovery.
Outcome:	The proposed framework suppresses unnecessary reasoning and enables implicit recovery.

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Mengqi Liao, Xiangyu Xi, Chen Ruinian, Jia Leng, Yangen Hu, Ke Zeng, Shuai Liu, Huaiyu Wan

Green & Sustainable NLP

Challenge:	Existing approaches allocate an equal number of rollouts to all questions during the RL process, which is inefficient.
Approach:	They propose a mechanism for dynamically allocating rollout budgets based on the difficulty of the problems, enabling more efficient RL training.
Outcome:	The proposed model improves response precision while preserving exploratory ability to uncover potential correct pathways.

Fin-STAR: Structure-as-Semantics to Resolve Implicitness in Financial Retrieval (2026.findings-acl)

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Yu Zou, Yan Chen, Lida He, Qi Zhou, Xiaorui Zhou, Aixi Zhong, Yi Wang, Wei Li, Qingyu Wang, Jiatao Li, Wei Gong, Jialei Zeng, Jingmei Zhao, Ke Jiang, Qing Li

Information Retrieval

Challenge:	Existing Retrieval-Augmented Generation systems treat structure as a physical navigational skeleton rather than intrinsic semantic knowledge.
Approach:	They propose a framework that redefining hierarchy as intrinsic semantics and uses snippets to enrich hierarchical lineage.
Outcome:	The proposed framework outperforms state-of-the-art hierarchical and graph-based benchmarks on FinTierQA Gold.

To Paraphrase or Not: Efficient Comment Detoxification with Unsupervised Detoxifiability Discrimination (2026.eacl-short)

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Jing Ke, Zheyong Xie, Shaosheng Cao, Tong Xu, Enhong Chen

Skip-Thinking: Chunk-wise Chain-of-Thought Distillation Enable Smaller Language Models to Reason Better and Faster (2025.emnlp-main)

Challenge:	Existing methods for detoxification of toxic comments are limited by overcorrection and data scarcity . experimental results show that DID outperforms existing methods on academic data and an industrial platform .
Approach:	They propose a paradigm that adaptively conducts filtering or paraphrasing for each toxic comment based on its detoxifiability . they propose 'detoxifiabilities-aware detoxification' that can be trained to filter or paraphrase toxic comments based upon their detoxifikatability based only on detoxificable comments .
Outcome:	Experimental results show that DID outperforms existing methods on academic and industrial data.

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Xiaoshu Chen, Sihang Zhou, Ke Liang, Xiaoyu Sun, Xinwang Liu

CharacterGLM: Customizing Social Characters with Large Language Models (2024.emnlp-industry)

Challenge:	Existing methods train small language models to learn long rationales in one iteration.
Approach:	They propose a method that uses a heuristic search to divide rationale into internal chunks . they propose CWT, which uses CWt to focus SLM on learning from only one chunk per iteration.
Outcome:	The proposed method can guide a large language model (LLM) in reasoning tasks.

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Jinfeng Zhou, Zhuang Chen, Dazhen Wan, Bosi Wen, Yi Song, Jifan Yu, Yongkang Huang, Pei Ke, Guanqun Bi, Libiao Peng, JiaMing Yang, Xiyao Xiao, Sahand Sabour, Xiaohan Zhang, Wenjing Hou, Yijia Zhang, Yuxiao Dong, Hongning Wang, Jie Tang, Minlie Huang

GUI0: Self-Evolving Foundational GUI Agents in Super App Ecosystems (2026.acl-long)

Challenge:	Character-based dialogue systems (CharacterDial) allow users to customize social characters for social interactions.
Approach:	They will collect a large-scale Chinese corpus of characters with diverse categories and behaviors and develop CharacterGLM models to address these challenges.
Outcome:	Experiments show that CharacterGLM outperforms most popular open- and closed-source LLMs and performs comparable to GPT-4.

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Xinyi Wang, Wei Dai, Kyle Qiao, Ke Wang, Peng Chen, Gang Cao, null Kangqin, Zhongpu Wang, Xiaode Zhang, Yanming Liu, Jihao Gu, Jingtao Xu, Gong Zhi

Relaxing the Constraints: A Dual-Importance Projection Mechanism for Lifelong Model Editing (2026.findings-acl)

Challenge:	Automated interaction with graphical user interfaces (GUIs) is central to general artificial intelligence, but remains challenging within Super App ecosystems.
Approach:	They propose a framework synergizing autonomous data synthesis with dual-agent co-evolution . GUI0 establishes a domain-aware foundation model via synthesized corpora and employs curriculum-driven reinforcement learning .
Outcome:	The proposed framework outperforms Gemini-2.5-Pro and Claude-4-Sonnet in the SuperAPP benchmark and has universal efficacy across base models.

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Zhenghai Chen, Senbin Xu, Jiaxi Tan, Xinhua Wu, Yan Zhang, Xiawu Zheng, Shengchuan Zhang, Ke Li, Sicheng Zhao, Liujuan Cao, Rongrong Ji

CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis (2025.findings-acl)

Challenge:	Existing knowledge editing methods rely on strict orthogonal projection to preserve previously edited knowledge, but this constraint limits gradient expressiveness, resulting in degradation of model generalization and overall performance as the number of edits increases.
Approach:	They propose a method that leverages Singular Value Decomposition to identify critical gradient subspaces and introduces a dual mechanism comprising "accumulated importance" and "projection importance"
Outcome:	Extensive experiments on five mainstream LLMs show that the proposed method achieves an average comprehensive performance improvement of 10.36% and effectively maintains the model’s general capabilities on downstream tasks.

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Junying Chen, Chi Gui, Anningzhe Gao, Ke Ji, Xidong Wang, Xiang Wan, Benyou Wang

Explainability & Interpretability in NLP

Challenge:	Existing large language models (LLMs) are proving to be effective in medical automatic diagnosis, but their interpretability remains unaddressed.
Approach:	They propose to use a "Chain-of-Diagnosis" approach to enhance the interpretability of medical automatic diagnosis by outputting the disease confidence distribution.
Outcome:	The proposed model outperforms other LLMs on automatic diagnostic tasks across three real-world benchmarks and provides interpretability while ensuring controllability in diagnostic rigor.

SAM Decoding: Speculative Decoding via Suffix Automaton (2025.acl-long)

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Yuxuan Hu, Ke Wang, Xiaokang Zhang, Fanjin Zhang, Cuiping Li, Hong Chen, Jing Zhang

Green & Sustainable NLP

Challenge:	Speculative decoding (SD) methods are inefficient and rely on single retrieval resources.
Approach:	They propose a retrieval-based speculative decoding method that adapts the suffix automaton for efficient draft generation by utilizing the generating text sequence and static text corpus.
Outcome:	The proposed method can find the longest suffix match and can be integrated with existing methods to generalize to broader domains.

LMOD: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models (2025.findings-naacl)

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Zhenyue Qin, Yu Yin, Dylan Campbell, Xuansheng Wu, Ke Zou, Ninghao Liu, Yih Chung Tham, Xiuzhen Zhang, Qingyu Chen

EquiBench: Benchmarking Large Language Models’ Reasoning about Program Semantics via Equivalence Checking (2025.emnlp-main)

Challenge:	Existing benchmarks for large vision-language models (LVLMs) are limited to ophthalmology-specific applications.
Approach:	They introduce a large-scale multimodal ophthalmology benchmark consisting of 21,993 instances across five ocular imaging modalities and 13 state-of-the-art LVLM representatives from closed-source, open-source and medical domains.
Outcome:	The proposed model shows significant performance drop in ophthalmology compared to other domains.

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Anjiang Wei, Jiannan Cao, Ran Li, Hongyu Chen, Yuhui Zhang, Ziheng Wang, Yuan Liu, Thiago S. F. X. Teixeira, Diyi Yang, Ke Wang, Alex Aiken

Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering (2022.acl-long)

Challenge:	EquiBench is a new benchmark to evaluate large language models' ability to reason about program semantics . Unlike natural language, code is executable.
Approach:	They propose a benchmark to evaluate large language models through equivalence checking . EquiBench consists of 2400 program pairs across four languages and six categories .
Outcome:	The proposed benchmark consists of 2400 program pairs across four languages and six categories.

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Jiawei Zhou, Xiaoguang Li, Lifeng Shang, Lan Luo, Ke Zhan, Enrui Hu, Xinyu Zhang, Hao Jiang, Zhao Cao, Fan Yu, Xin Jiang, Qun Liu, Lei Chen

FuseSearch: Learning Adaptive Parallel Execution for Efficient Code Localization (2026.findings-acl)

Challenge:	Existing methods to train dense passage retrieval have a large data gap between upstream and downstream relevance.
Approach:	They propose a method to pre-train the dense retriever with the text relevance induced by hyperlinks within Web documents.
Outcome:	The proposed method outperforms existing methods under different scenarios and in the open-domain question answering domain.

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Ke Xu, Siyang Xiao, Ming Liang, Yichen Yu, Zhixiang Wang, Jingxuan Xu, Dajun Chen, Wei Jiang, Yong Li

Green & Sustainable NLP

Challenge:	Existing parallel code localization agents suffer from a 34.9% redundant tool invocation rate . specialized localization agent that operate as dedicated search components is needed to achieve high localization accuracy.
Approach:	They propose a parallel code localization system that reframes parallel code execution as a quality–efficiency co-optimization problem.
Outcome:	The proposed method matches SOTA performance while being 93.6% faster.

NeKo: Cross-Modality Post-Recognition Error Correction with Tasks-Guided Mixture-of-Experts Language Model (2025.acl-industry)

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Yen-Ting Lin, Zhehuai Chen, Piotr Zelasko, Zhen Wan, Xuesong Yang, Zih-Ching Chen, Krishna C Puvvada, Ke Hu, Szu-Wei Fu, Jun Wei Chiu, Jagadeesh Balam, Boris Ginsburg, Yu-Chiang Frank Wang, Chao-Han Huck Yang