EFFECTIVENESS

Query Distillation: BERT-based Distillation for Ensemble Ranking (2020.coling-industry)

Challenge:	Multi-agent systems powered by large language models still face challenges . tutorial focuses on three core components to build effective and efficient systems .
Approach:	This tutorial introduces recent advances in building effective and efficient multi-agent LLM systems . it focuses on three core components: model distillation, dynamic routing, memory- and compute efficient serving .
Outcome:	This tutorial introduces state-of-the-art techniques for building efficient and efficient multi-agent LLM systems . it covers coordination and communication among agents, crucial for collective performance .

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Wangshu Zhang, Junhong Liu, Zujie Wen, Yafang Wang, Gerard de Melo

Sliding Windows Are Not the End: Exploring Full Ranking with Long-Context Large Language Models (2025.acl-long)

Challenge:	Recent years have witnessed substantial progress in the development of neural ranking networks, but an increasingly heavy computational burden due to growing numbers of parameters and the adoption of model ensembles.
Approach:	They propose a two-stage distillation method that allows a smaller student model to be trained while benefiting from the better performance of the teacher model.
Outcome:	The proposed method shows higher-quality rankings compared to the teacher model.

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Wenhan Liu, Xinyu Ma, Yutao Zhu, Ziliang Zhao, Shuaiqiang Wang, Dawei Yin, Zhicheng Dou

Match, Compare, or Select? An Investigation of Large Language Models for Entity Matching (2025.coling-main)

Challenge:	Existing methods for listwise passage ranking use sliding window approach, which is inefficient as it requires repetitive and serialized processing.
Approach:	They propose a listwise label construction approach and importance-aware learning objective for full ranking.
Outcome:	The proposed method outperforms existing methods in listwise ranking tasks.

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Tianshu Wang, Xiaoyang Chen, Hongyu Lin, Xuanang Chen, Xianpei Han, Le Sun, Hao Wang, Zhenyu Zeng

CLEME2.0: Towards Interpretable Evaluation by Disentangling Edits for Grammatical Error Correction (2025.acl-long)

Challenge:	Entity matching (EM) is a critical step in entity resolution (ER).
Approach:	They propose a method that incorporates record interactions from different perspectives.
Outcome:	The proposed framework improves on 8 ER datasets and 10 LLMs and achieves higher efficiency and effectiveness.

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Jingheng Ye, Zishan Xu, Yinghui Li, Linlin Song, Qingyu Zhou, Hai-Tao Zheng, Ying Shen, Wenhao Jiang, Hong-Gee Kim, Ruitong Liu, Xin Su, Zifei Shan

Syntactic Text Processing

Challenge:	Existing studies have focused on the interpretability of Grammatical Error Correction (GEC) evaluation metrics, but the interpretabilty of these metrics has been neglected.
Approach:	They propose a reference-based metric that describes four aspects of GEC systems: hit-correction, wrong-corrections, under-correcties, and over-corrects.
Outcome:	The proposed metric reveals critical qualities and locates drawbacks of GEC systems.

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

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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

UniICL: An Efficient ICL Framework Unifying Compression, Selection, and Generation (2025.acl-long)

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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Jun Gao, Qi Lv, Zili Wang, Tianxiang Wu, Ziqiang Cao, Wenjie Li

Continual Few-shot Intent Detection (2022.coling-1)

Challenge:	Existing methods to improve reasoning abilities of Large Language Models (LLMs) have limitations due to excessive growth in context length, causing large hardware burden.
Approach:	They propose a novel Unified ICL framework that unifies demonstration compression, demonstration selection, and final response generation.
Outcome:	The proposed framework unifies demonstration compression, demonstration selection, and final response generation.

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Guodun Li, Yuchen Zhai, Qianglong Chen, Xing Gao, Ji Zhang, Yin Zhang

Enhancing Self-Attention via Knowledge Fusion: Deriving Sentiment Lexical Attention from Semantic-Polarity Scores (2024.starsem-1)

Challenge:	Existing intent detection systems are trained with lots of labeled data over a predefined set of intent classes.
Approach:	They propose a prefix-guided lightweight encoder with three auxiliary strategies to prevent catastrophic forgetting and negative knowledge transfer across tasks.
Outcome:	The proposed system prevents catastrophic forgetting and encourages positive knowledge transfer across tasks.

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Dongjun Jang, Jinwoong Kim, Hyopil Shin

LLM-as-a-Coauthor: Can Mixed Human-Written and Machine-Generated Text Be Detected? (2024.findings-naacl)

Challenge:	Existing methods to inject lexical features into self-attention mechanisms have shown remarkable performance across various downstream tasks in NLP.
Approach:	They propose to inject lexical features into the self-attention mechanism of Transformer-based models by injecting lexicon-based Sentiment Lexical Attention into the attention scores throughout the training process.
Outcome:	The proposed method shows significant performance improvements on the NSMC sentiment classification benchmark and is able to perform in out-of-domain tasks.

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Qihui Zhang, Chujie Gao, Dongping Chen, Yue Huang, Yixin Huang, Zhenyang Sun, Shilin Zhang, Weiye Li, Zhengyan Fu, Yao Wan, Lichao Sun

Seeking Rational Demonstrations for Large Language Models: A Domain Generalization Approach to Unsupervised Cross-Domain Keyphrase Generation (2025.acl-short)

Challenge:	Current research focuses on purely MGT detection without adequately addressing mixed scenarios including AI-revised Human-Written Text (HWT) and human-revealed MGT.
Approach:	They define mixtext, a form of mixed text involving both AI and human-generated content, and then use a MixSet dataset to assess their effectiveness.
Outcome:	The proposed detectors struggle to identify mixtext, particularly in dealing with subtle modifications and style adaptability.

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Guangzhen Zhao, Yu Yao, Dechang Kong, Zhenjiang Dong

Towards Hierarchical Spoken Language Disfluency Modeling (2024.eacl-long)

Challenge:	Unsupervised cross-domain keyphrase generation is crucial in real-world natural language processing scenarios, but its accuracy is limited by the distribution shift between source and target domain.
Approach:	They propose to seek rational demonstrations from the source domain and to use them to improve their ability in the unsupervised cross-domain keyphrase generation setting.
Outcome:	The proposed model achieves state-of-the-art on widely used cross-domain KG benchmarks and the results are published in the journal Nature.

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Jiachen Lian, Gopala Anumanchipalli

LLMBox: A Comprehensive Library for Large Language Models (2024.acl-demos)

Challenge:	Existing solutions to speech dysfluency modeling are limited and expensive for low-income families.
Approach:	They propose a hierarchical unconstrained dysfluency modeling approach that addresses both dysfluencies transcription and detection to eliminate the need for extensive manual annotation.
Outcome:	The proposed approach eliminates the need for extensive manual annotation and improves the accuracy of the proposed model in phonetic transcription.

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Tianyi Tang, Hu Yiwen, Bingqian Li, Wenyang Luo, ZiJing Qin, Haoxiang Sun, Jiapeng Wang, Shiyi Xu, Xiaoxue Cheng, Geyang Guo, Han Peng, Bowen Zheng, Yiru Tang, Yingqian Min, Yushuo Chen, Jie Chen, Ranchi Zhao, Luran Ding, Yuhao Wang, Zican Dong, Xia Chunxuan, Junyi Li, Kun Zhou, Xin Zhao, Ji-Rong Wen

A Robust Semantics-based Watermark for Large Language Model against Paraphrasing (2024.findings-naacl)

Challenge:	a library to facilitate the development, use, and evaluation of large language models (LLMs) is presented.
Approach:	They propose a unified library to facilitate the development, use and evaluation of large language models (LLMs).
Outcome:	The proposed library is based on extensive experiments in a variety of evaluation settings.

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Jie Ren, Han Xu, Yiding Liu, Yingqian Cui, Shuaiqiang Wang, Dawei Yin, Jiliang Tang

Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models (2024.emnlp-main)

Challenge:	Existing methods to detect LLM-generated content use simple hashes of precedent tokens to partition vocabulary.
Approach:	They propose a semantics-based watermark framework to enhance the robustness against paraphrase.
Outcome:	The proposed framework is robust under different paraphrases and the semantic meaning of the sentences will be likely preserved under paraphrase.

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Zihan Wang, Deli Chen, Damai Dai, Runxin Xu, Zhuoshu Li, Yu Wu

SBERT studies Meaning Representations: Decomposing Sentence Embeddings into Explainable Semantic Features (2022.aacl-main)

Challenge:	Existing studies on parameter-efficient fine-tuning (PEFT) for dense-architecture LLMs are lacking.
Approach:	They propose an expert-specialized fine-tuning method that tunes the experts most relevant to downstream tasks while freezing the other experts.
Outcome:	The proposed method matches or surpasses full-parameter fine-tuning.

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Juri Opitz, Anette Frank

GenPilot: A Multi-Agent System for Test-Time Prompt Optimization in Image Generation (2025.findings-emnlp)

Challenge:	Abstract Meaning Representation (S3BERT) embeddings are composed of explainable sub-embeddings that emphasize various sentence meaning features.
Approach:	They propose to induce Semantically Structured Sentence BERT embeddings (S3BERT) that emphasize various sentence meaning features.
Outcome:	The proposed model shows high correlation to human similarity ratings, but lacks interpretability.

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Wen Ye, Zhaocheng Liu, Gui Yuwei, Tingyu Yuan, Yunyue Su, Bowen Fang, Chaoyang Zhao, Qiang Liu, Liang Wang

IPIGuard: A Novel Tool Dependency Graph-Based Defense Against Indirect Prompt Injection in LLM Agents (2025.emnlp-main)

Challenge:	Existing methods for text-to-image synthesis lack systematic error analysis and refinement strategies, resulting in limited reliability and effectiveness.
Approach:	They propose a plug-and-play multi-agent system called GenPilot that integrates error analysis, clustering-based adaptive exploration, fine-grained verification and a memory module for iterative optimization.
Outcome:	The proposed method improves text consistency and structural coherence on images with a plug-and-play system.

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Hengyu An, Jinghuai Zhang, Tianyu Du, Chunyi Zhou, Qingming Li, Tao Lin, Shouling Ji

Neural Collective Entity Linking (C18-1)

Challenge:	Existing methods for detecting Indirect Prompt Injection (IPI) attacks rely on assumptions about the model's inherent security, which lacks structural constraints on agent behaviors.
Approach:	They propose a novel task execution paradigm that models the agents’ task execution process as a traversal over a planned Tool Dependency Graph (TDG).
Outcome:	The proposed model reduces unintended tool invocations triggered by injected instructions, enhancing robustness against IPI attacks.

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Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu

Knowledge Representation

Challenge:	Entity linking aims to link entity mentions in texts to knowledge bases, but existing methods rely on local contexts to resolve entities independently.
Approach:	They propose a neural model for collective entity linking that integrates local contextual features and global coherence information to improve the computation efficiency.
Outcome:	The proposed model improves its performance on five publicly available datasets and can be used to train on Wikipedia hyperlinks to avoid overfitting and domain bias.

DatawiseAgent: A Notebook-Centric LLM Agent Framework for Adaptive and Robust Data Science Automation (2025.emnlp-main)

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Ziming You, Yumiao Zhang, Dexuan Xu, Yiwei Lou, Yandong Yan, Wei Wang, Huamin Zhang, Yu Huang

Monte Carlo Tree Search Based Prompt Autogeneration for Jailbreak Attacks against LLMs (2025.coling-main)

Challenge:	Existing large language model (LLM) agents for data science automation are limited by narrow task scopes, limited generalization across tasks and models, and over-reliance on state-of-the-art (SOTA) LLMs.
Approach:	They propose a notebook-centric LLM agent framework for adaptive and robust data science automation.
Outcome:	The proposed framework surpasses baselines such as AutoGen and TaskWeaver in performance tests across diverse data science scenarios and models.

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Suhuang Wu, Huimin Wang, Yutian Zhao, Xian Wu, Yefeng Zheng, Wei Li, Hui Li, Rongrong Ji

Client-Customized Adaptation for Parameter-Efficient Federated Learning (2023.findings-acl)

Challenge:	Jailbreak attacks craft specific prompts or append adversarial suffixes to prompts, thereby inducing language models to generate harmful or unethical content and bypassing the model’s safety guardrails.
Approach:	They propose a Monte Carlo Tree Search (MCTS) based Prompt Auto-generation (MPA) method to generate adversarial suffixes for valid jailbreak attacks.
Outcome:	The proposed method outperforms existing methods on open-source and closed-source models and shows that it can generate harmful responses.

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Yeachan Kim, Junho Kim, Wing-Lam Mok, Jun-Hyung Park, SangKeun Lee

Continual Prompt Tuning for Dialog State Tracking (2022.acl-long)

Challenge:	Pre-trained language models have a large memory footprint and are difficult to use in federated learning (FL)
Approach:	They propose a hypernetwork-based FL framework that generates client-specific adapters by conditioning the client information.
Outcome:	The proposed framework maximizes the utility of shared model parameters while minimizing divergence caused by client heterogeneity.

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Qi Zhu, Bing Li, Fei Mi, Xiaoyan Zhu, Minlie Huang

Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning (2022.emnlp-main)

Challenge:	Existing methods to train a model on a sequence of tasks are not efficient enough to mitigate catastrophic forgetting.
Approach:	They propose a parameter-efficient framework that prevents forgetting and enables knowledge transfer between tasks by learning and freezing a pre-trained model.
Outcome:	The proposed framework avoids forgetting and enables knowledge transfer between tasks.

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Wenbin An, Feng Tian, Ping Chen, Siliang Tang, Qinghua Zheng, QianYing Wang

Multimodal Prompt Learning with Missing Modalities for Sentiment Analysis and Emotion Recognition (2024.acl-long)

Challenge:	Existing methods for novel category discovery focus on the scenario where known and novel categories are of the same granularity.
Approach:	They propose a novel scenario for fine-grained category discovery under coarse-grain supervision that allows for adapting models to categories of different granularity from known ones.
Outcome:	The proposed model can adapt models to categories of different granularity from known ones and reduce labeling cost.

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Zirun Guo, Tao Jin, Zhou Zhao

DAWN-ICL: Strategic Planning of Problem-solving Trajectories for Zero-Shot In-Context Learning (2025.naacl-long)

Challenge:	Existing methods for multimodal sentiment analysis often fail due to equipment failure, data corruption, privacy issues and the like.
Approach:	They propose a multimodal Transformer framework using prompt learning to address the issue of missing modalities.
Outcome:	The proposed framework outperforms existing methods significantly across evaluation metrics.

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Xinyu Tang, Xiaolei Wang, Xin Zhao, Ji-Rong Wen

Root Defense Strategies: Ensuring Safety of LLM at the Decoding Level (2025.acl-long)

Challenge:	Existing methods to conduct in-context learning without using human-annotated demonstrations are unreliable and lead to error accumulation.
Approach:	They propose a method to conduct in-context learning without using human-annotated demonstrations.
Outcome:	The proposed method outperforms existing methods using human-annotated demonstrations.

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Xinyi Zeng, Yuying Shang, Jiawei Chen, Jingyuan Zhang, Yu Tian

Multi-Scale Prompt Memory-Augmented Model for Black-Box Scenarios (2024.naacl-long)

Challenge:	Existing methods to detect harmful outputs from prefill-level lacks utilization of the model’s decoding outputs, leading to relatively lower effectiveness and robustness.
Approach:	They propose a robust decoding mechanism that corrects harmful queries directly rather than rejecting them outright.
Outcome:	The proposed model improves model security without compromising reasoning speed.

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Xiaojun Kuang, C. L. Philip Chen, Shuzhen Li, Tong Zhang

Enriching Non-Autoregressive Transformer with Syntactic and Semantic Structures for Neural Machine Translation (2021.eacl-main)

Challenge:	Existing methods for few-shot text classification require numerous LMs’ calls to search optimal prompts, thus resulting in overfitting performance and increasing computational cost.
Approach:	They propose a multi-scale knowledge prompt-based memory model that extracts instance-level and class-level knowledge and stores them in memory banks during training.
Outcome:	Experiments on different benchmarks and parameter analysis demonstrate the effectiveness and efficiency of MuSKPrompt in black-box few-shot text classification tasks.

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Ye Liu, Yao Wan, Jianguo Zhang, Wenting Zhao, Philip Yu

Machine Translation

Challenge:	Existing non-autoregressive models have boosted the efficiency of neural machine translation, but their performance is significantly worse than that of autoregressive counterparts.
Approach:	They propose to incorporate syntactic and semantic structures among natural languages into a non-autoregressive Transformer for the task of neural machine translation.
Outcome:	The proposed model achieves faster speed and keeps translation quality compared with other models.

Hybrid Inverted Index Is a Robust Accelerator for Dense Retrieval (2023.emnlp-main)

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Peitian Zhang, Zheng Liu, Shitao Xiao, Zhicheng Dou, Jing Yao

Divergence-Based Domain Transferability for Zero-Shot Classification (2023.findings-eacl)

Challenge:	Inverted file structure is a common technique for accelerating dense retrieval, but its lossy nature degrades it.
Approach:	They propose a hybrid index where embedding clusters and salient terms work collaboratively to accelerate dense retrieval.
Outcome:	The proposed method achieves lossless retrieval quality with competitive efficiency across index settings.

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Alexander Pugantsov, Richard McCreadie

Efficient Model Development through Fine-tuning Transfer (2025.emnlp-main)

Challenge:	a recent study shows that fine-tuning of neural models can improve performance on language-based tasks without brute-force searching effective task combinations.
Approach:	They propose to use divergence measures to estimate whether one task pair will perform better than another . they use 58 tasks and 6,600 task pair combinations to study the effect of different tuning methods .
Outcome:	The proposed method reduces end-to-end runtime by 40% by estimating transferability . the proposed method is based on 58 tasks and over 6,600 task pair combinations .

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Pin-Jie Lin, Rishab Balasubramanian, Fengyuan Liu, Nikhil Kandpal, Tu Vu

ArcaneQA: Dynamic Program Induction and Contextualized Encoding for Knowledge Base Question Answering (2022.coling-1)

Challenge:	Modern large language models face a major bottleneck: each new version of a pre-trained model requires expensive and repetitive alignment.
Approach:	They propose a method that transfers fine-tuning updates across model versions . they extract the diff vector, which is the difference in parameters induced by fine-uning, from a source model and apply it to the base of a different target model.
Outcome:	The proposed method reduces training costs while maintaining model performance.

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Yu Gu, Yu Su

Gaussian Process based Deep Dyna-Q approach for Dialogue Policy Learning (2021.findings-acl)

Challenge:	Existing ranking-based KBQA models struggle with flexibility in predicting complicated queries and have impractical running time.
Approach:	They propose a new generation-based question answering on knowledge bases model that addresses both large search space and ambiguities in schema linking.
Outcome:	The proposed model overcomes two intertwined challenges on popular KBQA datasets and is highly competitive and efficient.

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Guanlin Wu, Wenqi Fang, Ji Wang, Jiang Cao, Weidong Bao, Yang Ping, Xiaomin Zhu, Zheng Wang

Dialogue Systems & Conversational Agents

Challenge:	Reinforcement learning (RL) is the main dialogue policy learning method in recent years.
Approach:	They propose a Gaussian Process based Deep Dyna-Q approach to dialogue policy learning . they propose evaluating the quality of experiences generated by the world model using a discriminator .
Outcome:	The proposed approach improves the effectiveness and efficiency of dialogue policy learning by 20% with fewer human-machine interactions.

HiCL: Hierarchical Contrastive Learning of Unsupervised Sentence Embeddings (2023.findings-emnlp)

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Zhuofeng Wu, Chaowei Xiao, VG Vinod Vydiswaran

Compressing then Matching: An Efficient Pre-training Paradigm for Multimodal Embedding (2026.acl-long)

Challenge:	Existing methods that encode a sequence in its entirety for contrast with others often neglect local representation learning.
Approach:	They propose a hierarchical contrastive learning framework, HiCL, which considers local segment-level and global sequence-level relationships to improve training efficiency and effectiveness.
Outcome:	The proposed framework improves training efficiency and effectiveness by dividing a sequence into several segments and using local and global contrastive learning to model relationships.

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Da Li, Yuxiao Luo, Keping Bi, Jiafeng Guo, Wei Yuan, Biao Yang, Yan Wang, Fan Yang, Tingting Gao, Guorui Zhou

Two Languages Are Better than One: Bilingual Enhancement for Chinese Named Entity Recognition (2022.coling-1)

Challenge:	Recent approaches demonstrate that MLLMs can be adapted into competitive embedding models via large-scale contrastive learning.
Approach:	They propose a compressed pre-training phase which serves as a warm-up stage for contrastive learning.
Outcome:	The proposed model achieves state-of-the-art among MLLMs of comparable size on the MMEB, realizing optimization in both efficiency and effectiveness.

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Jinzhong Ning, Zhihao Yang, Zhizheng Wang, Yuanyuan Sun, Hongfei Lin, Jian Wang

Exploring Language Model’s Code Generation Ability with Auxiliary Functions (2024.findings-naacl)

Challenge:	Existing studies focus on internal features of Chinese named entity recognition, but neglect other lingual modalities.
Approach:	They propose a bilingual enhancement module for Chinese Named Entity Recognition . they integrate rich English information into Chinese representation and use it to learn the interaction between bilinguals and dependent information within Chinese.
Outcome:	The proposed model can learn the interaction of bilinguals and dependent information within Chinese.

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Seonghyeon Lee, Sanghwan Jang, Seongbo Jang, Dongha Lee, Hwanjo Yu

CodeRetriever: A Large Scale Contrastive Pre-Training Method for Code Search (2022.emnlp-main)

Challenge:	Auxiliary function is a useful component to improve language model’s code generation ability, but a systematic exploration of how they affect has yet to be done.
Approach:	They construct a human-crafted evaluation set which contains examples of two functions where one function assists the other to examine their ability in a multifaceted way.
Outcome:	The proposed model is underutilized to call the auxiliary function, suggesting future directions to enhance their implementation by eliciting the supplementary function call ability encoded in the models.

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Xiaonan Li, Yeyun Gong, Yelong Shen, Xipeng Qiu, Hang Zhang, Bolun Yao, Weizhen Qi, Daxin Jiang, Weizhu Chen, Nan Duan

From Implicit Exploration to Structured Reasoning: Guideline and Refinement for LLMs (2025.findings-emnlp)

Challenge:	Existing code pre-training approaches often adopt (masked) language modeling as the training objective which targets on learning to predict (macked) tokens in a given code context.
Approach:	They propose a code-text contrastive learning model which learns function-level code semantic representations through large-scale code corpus.
Outcome:	The proposed model achieves new state-of-the-art with significant improvement over existing pre-trained models on eleven domain/language-specific code search tasks with six programming languages in different code granularity.

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Jiaxiang Chen, Zhuo Wang, Mingxi Zou, Zhucong Li, Zhijian Zhou, Song Wang, Zenglin Xu

Variance-reduced First-order Meta-learning for Natural Language Processing Tasks (2021.naacl-main)

Challenge:	Existing models rely on implicit exploration, which leads to unstable reasoning paths and lack of error correction.
Approach:	They propose a framework that shifts from implicit exploration to structured reasoning through guideline and refinement.
Outcome:	The proposed model outperforms strong baselines on the Big-Bench Hard benchmark.

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Lingxiao Wang, Kevin Huang, Tengyu Ma, Quanquan Gu, Jing Huang

Enhanced Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet Extraction (2022.acl-long)

Challenge:	Existing studies show that meta-learning can overfit to some specific adaptation when we have heterogeneous tasks.
Approach:	They propose to reduce the variance of the gradient estimator used in task adaptation by adding a new variance reduction term to the gradient estimation.
Outcome:	Experiments on few-shot text classification and multi-domain dialog state tracking show that the proposed method outperforms existing methods.

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Hao Chen, Zepeng Zhai, Fangxiang Feng, Ruifan Li, Xiaojie Wang

Learning Numeracy: A Simple Yet Effective Number Embedding Approach Using Knowledge Graph (2021.findings-emnlp)

Challenge:	Existing methods to extract aspect triplets ignore the relationships between words . Enhanced Multi-Channel Graph Convolutional Network model can be used to learn relation-aware node representations.
Approach:	They propose an Enhanced Multi-Channel Graph Convolutional Network model to fully utilize the relations between words for ASTE task.
Outcome:	The proposed model outperforms state-of-the-art methods significantly on a benchmark dataset.

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Hanyu Duan, Yi Yang, Kar Yan Tam

Scaling Laws for Multilingual Language Models (2025.findings-acl)

Challenge:	Existing models for numeracy-intensive applications fail to learn numerability . existing models fail to handle numbers, resulting in performance problems .
Approach:	They propose a number embedding approach that embeds numbers into dimensional space . they construct a knowledge graph consisting of number entities and magnitude relations .
Outcome:	The proposed method is easy to implement and shows that it performs well on numeracy-related tasks.

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Yifei He, Alon Benhaim, Barun Patra, Praneetha Vaddamanu, Sanchit Ahuja, Parul Chopra, Vishrav Chaudhary, Han Zhao, Xia Song

Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis (2022.naacl-main)

Challenge:	Existing scaling laws for language models are limited to a limited number of languages, but they can be applied to arbitrary number of different languages.
Approach:	They propose a scaling law for general-purpose decoder-only language models trained on multilingual data that shifts focus from individual languages to language families.
Outcome:	The proposed scaling law can be applied to models trained on multilingual data . it can be used to predict performance across multiple languages and models .

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Yiwei Wang, Muhao Chen, Wenxuan Zhou, Yujun Cai, Yuxuan Liang, Dayiheng Liu, Baosong Yang, Juncheng Liu, Bryan Hooi

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

Challenge:	Existing studies rely on entity information for sentence-level relation extraction (RE) but this can leak superficial and spurious clues of relations.
Approach:	They propose to use entity mentions to extract relations from textual context . they use a causal graph to model dependencies between variables in RE models .
Outcome:	The proposed method yields significant gains on both effectiveness and generalization for RE.

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Zitai Qiu, Congbo Ma, Jia Wu, Jian Yang

TRAVEL: Tag-Aware Conversational FAQ Retrieval via Reinforcement Learning (2023.emnlp-main)

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.

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Yue Chen, Dingnan Jin, Chen Huang, Jia Liu, Wenqiang Lei

Entropy-Gated Branching for Efficient Test-Time Reasoning (2026.eacl-long)

Challenge:	Existing methods aim to fully utilize the dynamic conversation context to enhance the semantic association between the user query and FAQ questions, but they are limited by noise and e.g., users may click questions they don't like, leading to inaccurate semantics modeling.
Approach:	They propose to introduce tags of FAQ questions to reduce noise in the conversation context and integrate them into a reinforcement learning framework to minimize the negative impact of irrelevant information.
Outcome:	The proposed method can eliminate irrelevant information and minimize negative impact of irrelevant information in the dynamic conversation context.

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Xianzhi Li, Ethan Callanan, Abdellah Ghassel, Xiaodan Zhu

Can Large Language Models Automatically Score Proficiency of Written Essays? (2024.lrec-main)

Challenge:	Empirical results show that branching at low uncertainty points can improve reasoning capabilities of large language models . however, these methods require substantially more computational resources, causing errors in high-stakes domains .
Approach:	They propose an inference technique that selectively expands prediction sequences at points of high uncertainty.
Outcome:	Empirical results show that the proposed method improves accuracy by 22.6% over standard inference while operating 31%-75% faster across math benchmarks.

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Watheq Ahmad Mansour, Salam Albatarni, Sohaila Eltanbouly, Tamer Elsayed

EXIT: Context-Aware Extractive Compression for Enhancing Retrieval-Augmented Generation (2025.findings-acl)

Challenge:	Automated essay scoring (AES) is one of the earliest research problems in natural language processing.
Approach:	They propose to use large language models to analyze and score written essays using four different prompts.
Outcome:	The proposed models show comparable performance on four different prompts and a slight advantage over the state-of-the-art models.

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Taeho Hwang, Sukmin Cho, Soyeong Jeong, Hoyun Song, SeungYoon Han, Jong C. Park

Question Answering

Challenge:	Current retrieval-augmented generation systems struggle when retrieval models fail to rank the most relevant documents . existing extractive methods reduce latency but rely on independent, non-adaptive sentence selection .
Approach:	They introduce an extractive context compression framework that enhances retrieval-augmented generation in question answering.
Outcome:	EXIT surpasses existing compression methods and uncompressed baselines in QA accuracy . the framework reduces inference time and token count while preserving contextual dependencies .

MoDification: Mixture of Depths Made Easy (2025.naacl-long)

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Chen Zhang, Meizhi Zhong, Qimeng Wang, Xuantao Lu, Zheyu Ye, Chengqiang Lu, Yan Gao, Yao Hu, Kehai Chen, Min Zhang, Dawei Song

TreeMAN: Tree-enhanced Multimodal Attention Network for ICD Coding (2022.coling-1)

Challenge:	Long-context efficiency is a trending topic in large language model (LLM) serving.
Approach:	They propose a method to combine long-context efficiency and mixture of depths to bring down both latency and memory.
Outcome:	The proposed method achieves 1.2 speedup in latency and 1.8 reduction in memory compared to original LLMs especially in long-context applications.

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Zichen Liu, Xuyuan Liu, Yanlong Wen, Guoqing Zhao, Fen Xia, Xiaojie Yuan

Two Birds with One Stone: Unified Model Learning for Both Recall and Ranking in News Recommendation (2022.findings-acl)

Challenge:	Existing methods to automatically assign ICD codes ignore crucial information contained in structured medical data, which is hard to be captured from the noisy clinical notes.
Approach:	They propose to use a Tree-enhanced multimodal attention network to fuse tabular features and textual features into multimodal representations by enhancing the text representations with tree-based features.
Outcome:	The proposed method outperforms state-of-the-art methods on two MIMIC datasets.

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Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang

Representation Learning

Challenge:	Existing news recommender systems conduct news recall and ranking separately with different models, but maintaining multiple models leads to high computational cost and high latency.
Approach:	They propose a unified method for recall and ranking in news recommendation that uses historical news click behaviors to extract user embeddings for ranking from the user's attention query.
Outcome:	The proposed method improves recall and ranking efficiency and effectiveness on a benchmark dataset.

Learning from Context or Names? An Empirical Study on Neural Relation Extraction (2020.emnlp-main)

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Hao Peng, Tianyu Gao, Xu Han, Yankai Lin, Peng Li, Zhiyuan Liu, Maosong Sun, Jie Zhou

LATENTLOGIC: Learning Logic Rules in Latent Space over Knowledge Graphs (2023.findings-emnlp)

Challenge:	Existing datasets may leak shallow heuristics via entity mentions, thus contributing to the high performance on RE benchmarks.
Approach:	They propose an entity-masked contrastive framework for relation extraction to gain a deeper understanding on textual context and type information while avoiding rote memorization of entities.
Outcome:	The proposed framework improves the effectiveness and robustness of neural models in different RE scenarios.

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Junnan Liu, Qianren Mao, Chenghua Lin, Yangqiu Song, Jianxin Li

Fusion or Defusion? Flexible Vision-and-Language Pre-Training (2023.findings-acl)

Challenge:	Existing methods for learning logic rules for knowledge graph reasoning face limitations such as searching in vast search space and inefficient optimization.
Approach:	They propose a framework to efficiently mine logic rules by controllable generation in the latent space by a pre-trained VAE and a discriminator.
Outcome:	The proposed framework efficiently mines logic rules by controllable generation in the latent space.

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Rongyi Sun, Ziran Li, Yifeng Ding, Qifan Wang, Jingang Wang, Haitao Zheng, Wei Wu, Yunsen Xian

Before Generation, Align it! A Novel and Effective Strategy for Mitigating Hallucinations in Text-to-SQL Generation (2024.findings-acl)

Challenge:	Existing approaches to vision-and-language pretraining (VLP) lack effectiveness and efficiency in downstream multimodal tasks.
Approach:	They propose a flexible vision-and-language pre-training model by incorporating cross-modal fusions into a dual-encoder architecture and a cross-module knowledge transfer strategy to guide the training process.
Outcome:	The proposed model is well-equipped with effectiveness and efficiency compared with other strong VLP models.

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Ge Qu, Jinyang Li, Bowen Li, Bowen Qin, Nan Huo, Chenhao Ma, Reynold Cheng

Programming Languages in NLP

Challenge:	Large Language Models (LLMs) driven by In-Context Learning (ICL) have improved performance of text-to-SQL.
Approach:	They propose a strategy to mitigate hallucinations in large language models driven by In-Context Learning (ICL) they propose TA-SQL, a text-to-Sql framework that encourages LLMs to take advantage of similar tasks rather than starting from scratch.
Outcome:	The proposed framework improves the performance of the GPT-4 model by 21.23% on BIRD dev.

Generative Deduplication For Socia Media Data Selection (2024.findings-emnlp)

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Xianming Li, Jing Li

Self-DC: When to Reason and When to Act? Self Divide-and-Conquer for Compositional Unknown Questions (2025.naacl-long)

Challenge:	Social media data exhibits severe redundancy due to its noisy nature, leading to increased training time and model bias in its processing.
Approach:	They propose a new framework for deduplication of social media data by removing semantically duplicate data from the model and add time-dimensional Gaussian noise to reduce training complexity.
Outcome:	The proposed framework can reduce training samples while improving performance over baselines.

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Hongru Wang, Boyang Xue, Baohang Zhou, Tianhua Zhang, Cunxiang Wang, Huimin Wang, Guanhua Chen, Kam-Fai Wong

Unlocking General Long Chain-of-Thought Reasoning Capabilities of Large Language Models via Representation Engineering (2025.acl-long)

Challenge:	Existing studies focus on leveraging internal knowledge of Large Language Models (LLMs) to answer known questions.
Approach:	They propose a framework that allows LLMs to choose between internal and external knowledge . they use a dataset to analyze compositional questions that are composed of unknown sub-questions .
Outcome:	The proposed framework can achieve comparable or even better performance with much fewer external calls compared with several strong baselines.

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Xinyu Tang, Xiaolei Wang, Zhihao Lv, Yingqian Min, Xin Zhao, Binbin Hu, Ziqi Liu, Zhiqiang Zhang

How to Determine the Most Powerful Pre-trained Language Model without Brute Force Fine-tuning? An Empirical Survey (2023.findings-emnlp)

Challenge:	Existing work finds that long CoT reasoning can be efficiently elicited by tuning on only a few examples and can easily transfer to other tasks.
Approach:	They propose a representation engineering method to unleash the general long CoT reasoning capabilities of LLMs.
Outcome:	The proposed method is effective in in-domain and cross-domain scenarios.

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Jun Bai, Xiaofeng Zhang, Chen Li, Hanhua Hong, Xi Xu, Chenghua Lin, Wenge Rong

LLM-Forest: Ensemble Learning of LLMs with Graph-Augmented Prompts for Data Imputation (2025.findings-acl)

Challenge:	Transferability estimation has been a topic of great interest in computer vision fields . a lack of a comprehensive comparison between these estimation methods is a problem .
Approach:	They conduct a thorough survey of existing methods to find the most suitable model . they also outline difficulties of consideration of training details and applicability to text generation .
Outcome:	The proposed methods perform well with superiorities in effectiveness and efficiency.

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Xinrui He, Yikun Ban, Jiaru Zou, Tianxin Wei, Curtiss Cook, Jingrui He

MiniELM: A Lightweight and Adaptive Query Rewriting Framework for E-Commerce Search Optimization (2025.findings-acl)

Challenge:	Existing frameworks for missing data imputation are lacking in a finetuning-free process and mitigating biases and uncertainty in LLM outputs.
Approach:	They propose a framework for imputation of large language models with a forest of few-shot learning LLM "trees" they use bipartite information graphs to identify relevant neighboring entries with feature and value granularity.
Outcome:	The proposed framework is based on a concept of bipartite information graphs to identify high-quality relevant neighboring entries with both feature and value granularity.

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Duy A. Nguyen, Rishi Kesav Mohan, Shimeng Yang, Pritom Saha Akash, Kevin Chen-Chuan Chang

As easy as PIE: understanding when pruning causes language models to disagree (2025.findings-naacl)

Challenge:	Existing methods for rewriting query terms struggle with natural language understanding . generative methods face high inference latency and cost in offline settings .
Approach:	They propose a hybrid pipeline for rewriting query queries using offline knowledge distillation and online reinforcement learning.
Outcome:	The proposed pipeline improves query relevance, diversity, adaptability and cost-effective evaluation without manual annotations on Amazon ESCI dataset.

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Pietro Tropeano, Maria Maistro, Tuukka Ruotsalo, Christina Lioma

SpecVLM: Enhancing Speculative Decoding of Video LLMs via Verifier-Guided Token Pruning (2025.emnlp-main)

Challenge:	Language Model pruning reduces the model's efficiency by removing weights, nodes, or other parts of its architecture.
Approach:	They propose to prune Language Models (LMs) to produce smaller, hence more efficient models with small loss to their effectiveness.
Outcome:	The proposed pruning method hurts data points that matter the most when pruning . the proposed pruning technique is based on a new study of NLP datasets .

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Yicheng Ji, Jun Zhang, Heming Xia, Jinpeng Chen, Lidan Shou, Gang Chen, Huan Li

VCSUM: A Versatile Chinese Meeting Summarization Dataset (2023.findings-acl)

Challenge:	Video large language models (Vid-LLMs) rely on dense video token representations and require substantial memory and computational overhead in both prefilling and decoding.
Approach:	They propose a training-free speculative decoding framework that prunes up to 90% of video tokens to enable efficient speculation without sacrificing accuracy.
Outcome:	The proposed framework achieves 2.68 speedup on LLaVA-OneVision-72B and 2.11 speed up on Qwen2.5-VL-32B.

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Han Wu, Mingjie Zhan, Haochen Tan, Zhaohui Hou, Ding Liang, Linqi Song

Edge-free but Structure-aware: Prototype-Guided Knowledge Distillation from GNNs to MLPs (2025.coling-main)

Challenge:	Compared to news and chat summarization, meeting summarizing is decelerated by the limited data.
Approach:	They propose a Chinese meeting summarization dataset that provides annotations for each transcript and a set of benchmark models to facilitate further research.
Outcome:	The proposed model can be used to summarize the content of meeting transcripts in Chinese.

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Taiqiang Wu, Zhe Zhao, Jiahao Wang, Xingyu Bai, Lei Wang, Ngai Wong, Yujiu Yang

Personalized Transformer for Explainable Recommendation (2021.acl-long)

Challenge:	Existing methods to train low-latency multilayer perceptrons (MLPs) on graph tasks are based on graph nodes and lack graph structural information.
Approach:	They propose to distill graph structural information from Graph Neural Networks (GNNs) to low-latency multilayer perceptrons (MLPs) on graph tasks.
Outcome:	The proposed method does not require graph edges (edge-free setting) yet learns structure-aware MLPs.

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Lei Li, Yongfeng Zhang, Li Chen

Explainability & Interpretability in NLP

Challenge:	Recent years have witnessed the successful application of natural language generation.
Approach:	They propose a model that uses user and item IDs to predict the words in the target explanation to make personalized Transformer.
Outcome:	The proposed model outperforms BERT on the explainable recommendation task in terms of effectiveness and efficiency.

Revisiting Model Interpolation for Efficient Reasoning (2026.acl-long)

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Taiqiang Wu, Runming Yang, Tao Liu, Jiahao Wang, Ngai Wong

KnowLA: Enhancing Parameter-efficient Finetuning with Knowledgeable Adaptation (2024.naacl-long)

Challenge:	Existing models that interpolate weights of two specialized models can be abused for efficient reasoning.
Approach:	They propose to merge two specialized models and create a model that combines efficiency and efficiency.
Outcome:	The proposed method outperforms existing models on efficiency and effectiveness.

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Xindi Luo, Zequn Sun, Jing Zhao, Zhe Zhao, Wei Hu

RepEval: Effective Text Evaluation with LLM Representation (2024.emnlp-main)

Challenge:	Existing methods for parameter-efficient finetuning (PEFT) are limited and only finetune a small number of parameters using limited instruction data.
Approach:	They propose a method that inserts an adaptation layer into an LLM to integrate embeddings of entities appearing in the input text.
Outcome:	The proposed method can activate parameterized knowledge in an LLM without changing its parameters or input prompts.

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Shuqian Sheng, Yi Xu, Tianhang Zhang, Zanwei Shen, Luoyi Fu, Jiaxin Ding, Lei Zhou, Xiaoying Gan, Xinbing Wang, Chenghu Zhou

Representation Learning

Challenge:	Traditional metrics for automatic text evaluation are tailored to specific tasks, while LLM-based evaluation metrics are costly.
Approach:	They propose a metric that leverages projections of LLM representations for evaluation.
Outcome:	The proposed metric exhibits higher correlation with human judgments than previous methods on 14 datasets.

GBT: Generative Boosting Training Approach for Paraphrase Identification (2023.findings-emnlp)

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Rui Peng, Zhiling Jin, Yu Hong

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

Challenge:	Paraphrase Identification (PI) is a fundamental natural language understanding task with non-trivial challenges.
Approach:	They propose a Generative Boosting Training approach for Paraphrase Identification (PI) they use a seq2seq model to perform DA on misclassified instances periodically .
Outcome:	The proposed method outperforms state-of-the-art PI models on English and Chinese PI tasks with good efficiency and effectiveness.

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

Learning to Instruct: Fine-Tuning a Task-Aware Instruction Optimizer for Black-Box LLMs (2025.findings-emnlp)

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

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Yunzhe Qi, Jinjin Tian, Tianci Liu, Ruirui Li, Tianxin Wei, Hui Liu, Xianfeng Tang, Monica Xiao Cheng, Jingrui He

Discriminative Deep Dyna-Q: Robust Planning for Dialogue Policy Learning (D18-1)

Challenge:	Learning to Instruct is a new paradigm for black-box LLMs with inaccessible internal states.
Approach:	They propose a new paradigm that formulates instruction optimization as an LLM fine-tuning objective for a white-box “instruction engineer” LLM.
Outcome:	The proposed framework outperforms strong baselines in performance and efficiency.

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Shang-Yu Su, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen

Strong and Efficient Baselines for Open Domain Conversational Question Answering (2023.findings-emnlp)

Challenge:	Existing approaches to improve the effectiveness and robustness of Deep Dyna-Q (DDQ) are based on a discriminator to control the quality of simulated experiences and to improve learning.
Approach:	They propose to use an RNN-based discriminator to control the quality of simulated experience to improve the effectiveness and robustness of Deep Dyna-Q.
Outcome:	The proposed framework outperforms DDQ by controlling the quality of simulated experience used for planning.

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Andrei Coman, Gianni Barlacchi, Adrià de Gispert

Towards Adaptive Memory-Based Optimization for Enhanced Retrieval-Augmented Generation (2025.findings-acl)

Challenge:	Current Open Domain Question Answering (ODQA) approaches to reevaluating baselines for efficiency and effectiveness have been limited due to various limitations.
Approach:	They propose and evaluate strong yet simple and efficient baselines by introducing a fast reranking component between the retriever and the reader and performing targeted finetuning steps.
Outcome:	The proposed method improves the SotA results while reducing the reader’s latency by 60%.

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Qitao Qin, Yucong Luo, Yihang Lu, Zhibo Chu, Xiaoman Liu, Xianwei Meng

LEMON: Reviving Stronger and Smaller LMs from Larger LMs with Linear Parameter Fusion (2024.acl-long)

Challenge:	Existing methods for enhancing response accuracy and accuracy struggle with open-domain QA tasks because they perform independent retrieval operations without maintaining a summarizing memory or using adaptive retrieval strategies.
Approach:	They propose a method that integrates non-parametric knowledge from external knowledge bases into models to enhance response accuracy while mitigating factual errors and hallucinations.
Outcome:	The proposed method improves on open-domain QA datasets and reduces noise and hallucinations due to redundant information and insufficient information integration.

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Yilong Chen, Junyuan Shang, Zhenyu Zhang, Shiyao Cui, Tingwen Liu, Shuohuan Wang, Yu Sun, Hua Wu

CGBridge: Bridging Code Graphs and Large Language Models for Better Structure-Aware Code Understanding (2026.findings-acl)

Challenge:	Existing methods to train a stronger and smaller model with the help of large models are limited by the model size and performance.
Approach:	They propose to learn competent initial points for smaller models by fusing parameters from larger models and introduce controllable receptive fields to model prior parameter characteristics.
Outcome:	The proposed method outperforms baselines in terms of effectiveness and efficiency.

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Zeqi Chen, Zhaoyang Chu, Yi Gui, Feng Guo, Yao Wan, Chuan Shi

SafeInt: Shielding Large Language Models from Jailbreak Attacks via Safety-Aware Representation Intervention (2025.findings-emnlp)

Challenge:	Existing structure-aware approaches treat structure as serialized text prompts or auxiliary training objectives, failing to provide explicit guidance during inference.
Approach:	They propose a plug-and-play method that enhances Large Language Models with Code Graph information through an external, trainable Bridge module.
Outcome:	The proposed method decouples structural reasoning from textual generation without updating the backbone.

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Jiaqi Wu, Chen Chen, Chunyan Hou, Xiaojie Yuan

An Empirical Study of Frame Selection for Text-to-Video Retrieval (2023.findings-emnlp)

Challenge:	Jailbreak attacks exploit vulnerabilities in large language models to induce undesirable behavior . existing defenses cannot dynamically adjust representations based on harmfulness of queries .
Approach:	They propose a representation-aware representation method that shields LLMs from jailbreak attacks . SafeInt relocates jailbreak-related representations into the rejection region .
Outcome:	The proposed method outperforms baseline defenses while maintaining utility . it relocates jailbreak-related representations into the rejection region .

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Mengxia Wu, Min Cao, Yang Bai, Ziyin Zeng, Chen Chen, Liqiang Nie, Min Zhang

2D-DPO: Scaling Direct Preference Optimization with 2-Dimensional Supervision (2025.findings-naacl)

Challenge:	Existing methods for text-to-video retrieval select a subset of frames to represent video content . current methods only explore video contents while ignoring relevancy to texts .
Approach:	They propose to use a subset of frames to represent video content for TVR . they analyze six different frame selection methods to determine their effectiveness .
Outcome:	The proposed method improves retrieval efficiency without sacrificing visual details . the proposed method explores the video contents while ignoring relevancy to texts .

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Shilong Li, Yancheng He, Hui Huang, Xingyuan Bu, Jiaheng Liu, Hangyu Guo, Weixun Wang, Jihao Gu, Wenbo Su, Bo Zheng

Challenge:	Existing methods that optimize for scalar scores or ranking reward ignore multi-dimensional nature of human preferences.
Approach:	They propose to extend the preference of Direct Preference Optimization to two dimensions: segments and aspects.
Outcome:	The proposed framework decomposes the overall objective into multi-segment and multi-aspect objectives.

Knowledge Association with Hyperbolic Knowledge Graph Embeddings (2020.emnlp-main)

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Zequn Sun, Muhao Chen, Wei Hu, Chengming Wang, Jian Dai, Wei Zhang

RealVul: Can We Detect Vulnerabilities in Web Applications with LLM? (2024.emnlp-main)

Challenge:	Existing methods for knowledge graphs (KGs) depend on high embedding dimensions and hierarchical structures to achieve expressiveness.
Approach:	They propose a hyperbolic relational graph neural network for KG embedding and capture knowledge associations with a high-dimensional transformation.
Outcome:	Experiments on entity alignment and type inference show the proposed method is effective and efficient.

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Di Cao, Yong Liao, Xiuwei Shang

Activation Scaling for Steering and Interpreting Language Models (2024.findings-emnlp)

Challenge:	a lack of research specifically focused on vulnerabilities in the PHP language hinders the model’s ability to effectively capture the characteristics of specific vulnerabilities.
Approach:	They propose a framework that can isolate potential vulnerability triggers while streamlining code and eliminating unnecessary semantic information.
Outcome:	The proposed framework can isolate potential vulnerability triggers while streamlining the code and eliminating unnecessary semantic information.

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Niklas Stoehr, Kevin Du, Vésteinn Snæbjarnarson, Robert West, Ryan Cotterell, Aaron Schein

Enhancing Structure-aware Encoder with Extremely Limited Data for Graph-based Dependency Parsing (2022.coling-1)

Challenge:	a successful intervention should flip the correct with the wrong token, while remaining sparse.
Approach:	They propose to use activation scaling to flip the correct with the wrong token . they use gradient-based optimization to learn and evaluate a specific kind of efficient intervention .
Outcome:	The proposed method performs comparable with steering vectors but is much less minimal.

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Yuanhe Tian, Yan Song, Fei Xia

Efficiency and Effectiveness in Task-Oriented Dialogue: On Construction Repetition, Information Rate, and Task Success (2024.lrec-main)

Challenge:	Dependency parsing is an important natural language processing task which analyzes the syntactic structure of an input sentence.
Approach:	They propose a structure-aware encoder pre-trained on auto-parsed data to improve dependency parsing . they propose combining gold dependency trees with existing parsers to improve parser performance .
Outcome:	The proposed approach outperforms baselines under different parsers and dependency standards under different parameters and model architectures.

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Jun Sen Yee, Mario Giulianelli, Arabella J. Sinclair

Dialogue Systems & Conversational Agents

Challenge:	Repetition of constructions in task-oriented dialogue can have negative and positive effects on information rate and delivery, but is also predictive of task success.
Approach:	They investigate the role that efficiency and effectiveness play in speakers’ repetition of shared word sequences, or constructions, in task-oriented dialogue.
Outcome:	The results show that repeating constructions has negative and positive effects on information rate and delivery and that information rate managing strategies are predictive of task success.

Learning to Align Multi-Faceted Evaluation: A Unified and Robust Framework (2025.findings-acl)

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Kaishuai Xu, Tiezheng Yu, Yi Cheng, Wenjun Hou, Liangyou Li, Xin Jiang, Lifeng Shang, Qun Liu, Wenjie Li

On Efficient Retrieval of Top Similarity Vectors (D19-1)

Challenge:	Existing methods for fine-tuning open-source LLMs are limited to text-based analysis under predefined general criteria.
Approach:	They propose a framework that fine-tunes LLMs to replicate the evaluation explanations and judgments of proprietary models.
Outcome:	The proposed evaluation framework outperforms existing fine-tuned evaluation methods in effectiveness and robustness.

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Shulong Tan, Zhixin Zhou, Zhaozhuo Xu, Ping Li

Semantic Role Labeling from Chinese Speech via End-to-End Learning (2024.findings-acl)

Challenge:	Existing representation learning methods such as Word2vec represent word embeddings in the semantic space.
Approach:	They propose an efficient method for searching vectors via a non-metric matching function: inner product.
Outcome:	Experiments on data representations learned for different machine learning tasks show the proposed method outperforms existing methods.

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Huiyao Chen, Xinxin Li, Meishan Zhang, Min Zhang

DetermLR: Augmenting LLM-based Logical Reasoning from Indeterminacy to Determinacy (2024.acl-long)

Challenge:	Semantic role labeling (SRL) has traditionally focused on text input.
Approach:	They propose an end-to-end approach for SRL from speech integrating ASR and SRL in a joint-learning framework, focusing on the Chinese language.
Outcome:	The proposed model improves on the Chinese Proposition Bank 1.0 dataset and the existing model with improved performance.

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Hongda Sun, Weikai Xu, Wei Liu, Jian Luan, Bin Wang, Shuo Shang, Ji-Rong Wen, Rui Yan

MoQAE: Mixed-Precision Quantization for Long-Context LLM Inference via Mixture of Quantization-Aware Experts (2025.acl-long)

Challenge:	Recent advances in large language models (LLMs) have revolutionized the landscape of reasoning tasks.
Approach:	They propose a new approach that rethinks the reasoning process as an evolution from indeterminacy to determinacy.
Outcome:	The proposed model surpasses all baselines on various logical reasoning benchmarks.

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Wei Tao, Haocheng Lu, Xiaoyang Qu, Bin Zhang, Kai Lu, Jiguang Wan, Jianzong Wang

A Generative Framework for Simultaneous Machine Translation (2021.emnlp-main)

Challenge:	Existing approaches to optimize large language models for long-context inference are inefficient and consume memory.
Approach:	They propose a mixed-precision quantization method via mixture of experts that inputs tokens into router chunk by chunk to reduce inference overhead.
Outcome:	The proposed method outperforms state-of-the-art KV cache quantization methods on multiple benchmark datasets.

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Yishu Miao, Phil Blunsom, Lucia Specia

Multi-Stage Pre-training for Automated Chinese Essay Scoring (2020.emnlp-main)

Challenge:	Existing approaches use a fixed number of source words to translate or learn dynamic policies for the number of sources by reinforcement learning.
Approach:	They propose a generative framework that uses a latent variable to model read or translate actions at every time step and integrates out to consider all possible translation policies.
Outcome:	The proposed framework achieves the best BLEU scores on benchmark datasets.

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Wei Song, Kai Zhang, Ruiji Fu, Lizhen Liu, Ting Liu, Miaomiao Cheng

MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models (2024.acl-long)

Challenge:	Existing methods for automatic essay scoring are based on hand-crafted surface-level features, but recent advances in representation learning have improved performance.
Approach:	They propose a pre-training based automated Chinese essay scoring method with weakly supervised pre- training, supervised cross- prompt fine-tuning and supervised target- prompt refine-tuneing.
Outcome:	The proposed method improves a state-of-the-art neural essay scorer in terms of effectiveness and domain adaptation ability, while in-depth analysis also reveals its limitations.

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Yilin Wen, Zifeng Wang, Jimeng Sun

STINMatch: Semi-Supervised Semantic-Topological Iteration Network for Financial Risk Detection via News Label Diffusion (2023.emnlp-main)

Challenge:	Large language models suffer from limitations such as difficulty in incorporating new knowledge, generating hallucinations, and explaining their reasoning process.
Approach:	They propose a pipeline that leverages knowledge graphs to enhance LLMs’ inference and transparency by eliciting the mind map of LLM's, which reveals their reasoning pathways based on the ontology of knowledge.
Outcome:	The proposed pipeline enables LLMs to comprehend KG inputs and infer with a combination of implicit and external knowledge.

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Xurui Li, Yue Qin, Rui Zhu, Tianqianjin Lin, Yongming Fan, Yangyang Kang, Kaisong Song, Fubang Zhao, Changlong Sun, Haixu Tang, Xiaozhong Liu

Dynamic-prototype Contrastive Fine-tuning for Continual Few-shot Relation Extraction with Unseen Relation Detection (2025.coling-main)

Challenge:	Commercial news provides rich semantics and timely information for automated financial risk detection.
Approach:	They propose a semi-supervised Semantic-Topological Iteration Network, STINMatch, along with a news-enterprise knowledge graph to endorse the risk detection enhancement.
Outcome:	The proposed model outperforms existing models in terms of generalization and semantics and annotation.

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Si Miao Zhao, Zhen Tan, Ning Pang, Wei Dong Xiao, Xiang Zhao

ALinFiK: Learning to Approximate Linearized Future Influence Kernel for Scalable Third-Parity LLM Data Valuation (2025.naacl-long)

Challenge:	Existing approaches to learn relations from labeled data overlook task interference in continual learning and memory requirements for different relations.
Approach:	They propose a framework to learn new relations from limited labeled data while preserving knowledge about previously learned relations.
Outcome:	The proposed framework is more practical and comprehensive for real-world scenarios.

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Yanzhou Pan, Huawei Lin, Yide Ran, Jiamin Chen, Xiaodong Yu, Weijie Zhao, Denghui Zhang, Zhaozhuo Xu

FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning (2022.acl-long)

Challenge:	Large Language Models (LLMs) heavily rely on high-quality training data, making data valuation crucial for optimizing model performance.
Approach:	They propose a third-party data valuation approach that assesses the value of individual data samples and proposes a learning strategy to approximate LinFiK.
Outcome:	The proposed approach surpasses baselines in effectiveness and efficiency, showing significant scalability advantages as LLM parameters increase.

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Jing Zhou, Yanan Zheng, Jie Tang, Li Jian, Zhilin Yang

TF-Mamba: Text-enhanced Fusion Mamba with Missing Modalities for Robust Multimodal Sentiment Analysis (2025.findings-emnlp)

Challenge:	Existing methods for text data augmentation are limited to simple tasks and weak baselines.
Approach:	They propose a data augmentation method FlipDA that uses a generative model and a classifier to generate label-flipped data.
Outcome:	The proposed method improves many tasks while not negatively affecting the others.

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Xiang Li, Xianfu Cheng, Dezhuang Miao, Xiaoming Zhang, Zhoujun Li

DORA: A Dual-Objective Reinforcement Learning Framework for Effective and Efficient Multimodal Agentic Search (2026.acl-long)

Challenge:	Existing Transformer-based methods with missing modalities are difficult to use and have quadratic complexity.
Approach:	They propose a text-enhanced Fusion Mamba framework for robust MSA with missing modalities . a Text-aware Modality Enhancement module aligns and enriches non-text modality while reconstructing missing text semantics.
Outcome:	The proposed method is efficient under missing modalities and can be used in long-range modeling and multimodal fusion scenarios.

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Guangming Qin, Yuhao Deng, Yukun Zhao, Zhenyang Li, Junfeng Wang, Dawei Yin, Ye Yuan, Guoren Wang, Yizhou Yan, Chengliang Chai, Lei Cao

Causal Intervention Improves Implicit Sentiment Analysis (2022.coling-1)

Challenge:	Existing methods to train large language models overlook quality of intermediate search results . existing methods often invoke search calls during reasoning, making inference inefficient .
Approach:	They propose a dual-objective reinforcement learning framework to improve search strategies of MLLMs . DORA outperforms state-of-the-art methods, achieving up to 8.4% higher accuracy .
Outcome:	The proposed model outperforms state-of-the-art methods while reducing search calls by 9.7%.

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Siyin Wang, Jie Zhou, Changzhi Sun, Junjie Ye, Tao Gui, Qi Zhang, Xuanjing Huang

Android Coach: Improve Online Agentic Training Efficiency with Single State Multiple Actions (2026.acl-long)

Challenge:	Existing neural models struggle with implicit sentiment analysis because they latch onto spurious correlations, resulting in poor generalization and robustness.
Approach:	They propose a CausaL intervention model for implicit sEntiment ANalysis using instrumental variable to eliminate confounding causal effects and extract the pure causal effect between sentence and sentiment.
Outcome:	The proposed model extracts the pure causal effect between sentence and sentiment using instrumental variable.

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Guo Gan, Yuxuan Ding, Cong Chen, Yuwei Ren, Yin Huang, Hong Zhou

Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning (2024.findings-acl)

Challenge:	Existing reinforcement learning methods are expensive due to high latency and sample inefficiency . Currently, RL is limited to one-to-one state-action pairs .
Approach:	They propose a framework that shifts the training paradigm to Single State Multiple Actions and introduce a group-wise advantage estimator based on the averaged critic outputs.
Outcome:	The proposed framework achieves 7.5% and 8.3% success rate improvements on AndroidLab and AndroidWorld over UI-TARS-1.5-7B and attains 1.4x higher training efficiency than existing methods.

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Xiaohu Du, Ming Wen, Jiahao Zhu, Zifan Xie, Bin Ji, Huijun Liu, Xuanhua Shi, Hai Jin

AgentInit: Initializing LLM-based Multi-Agent Systems via Diversity and Expertise Orchestration for Effective and Efficient Collaboration (2025.findings-emnlp)

Challenge:	Existing CodePre-trained models struggle to generalize due to superficial mapping from source code to labels instead of understanding the root causes of code vulnerabilities.
Approach:	They propose a framework that integrates multi-task learning with Large Language Models to effectively mine deep-seated vulnerability features.
Outcome:	The proposed framework surpasses seven state-of-the-art models in effectiveness, generalization, and robustness.

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Chunhao Tian, Yutong Wang, Xuebo Liu, Zhexuan Wang, Liang Ding, Miao Zhang, Min Zhang

LLMs Meet Isolation Kernel: Lightweight, Learning-free Binary Embeddings for Fast Retrieval (2026.findings-acl)

Challenge:	Existing MAS initialization methods do not fully account for the collaborative needs of the generated agents in subsequent stages.
Approach:	They propose to use a Natural Language to Format mechanism to optimize the structure of agent teams and incorporate a natural language to format mechanism to ensure consistency and standardization.
Outcome:	The proposed method outperforms state-of-the-art initialization methods and pre-defined strategies across various frameworks and tasks while reducing token consumption.

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Zhibo Zhang, Yang Xu, Kai Ming Ting, Cam-Tu Nguyen

Graceful Forgetting in Generative Language Models (2025.emnlp-main)

Challenge:	Large language models (LLMs) embeddings are typically high-dimensional, leading to substantial storage and retrieval overhead.
Approach:	They propose a learning-free method that transforms an LLM embedding into a binary embeddable using Isolation Kernel (IKE).
Outcome:	The proposed method performs 16.7 faster retrieval and 16 lower memory usage than the original LLM embeddings while maintaining comparable accuracy.

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Chunyang Jiang, Chi-Min Chan, Yiyang Cai, Yulong Liu, Wei Xue, Yike Guo

TeamLoRA: Boosting Low-Rank Adaptation with Expert Collaboration and Competition (2025.acl-long)

Challenge:	Recent studies show that pre-trained models do not provide all knowledge needed for fine-tuning tasks.
Approach:	They propose a framework to achieve graceful forgetting in generative language models by pre-training a model on large-scale correlating datasets.
Outcome:	The proposed framework improves the learning plasticity of the target task by selectively discarding irrelevant knowledge.

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Tianwei Lin, Jiang Liu, Wenqiao Zhang, Yang Dai, Haoyuan Li, Zhelun Yu, Wanggui He, Juncheng Li, Jiannan Guo, Hao Jiang, Siliang Tang, Yueting Zhuang

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback (2025.findings-acl)

Challenge:	Existing methods for fine-tuning are resource-efficient, but performance often falls short . a new approach, TeamLoRA, integrates collaborative and competitive modules to improve performance.
Approach:	They propose to introduce task-specific LoRA as domain experts to improve learning efficiency . teamLoRA integrates collaborative and competition modules to improve model learning .
Outcome:	Experiments show that TeamLoRA improves performance in multi-task learning . teamLorea integrates collaborative and competitive modules to improve performance .

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Thai Quoc Hoang, Kung-Hsiang Huang, Shirley Kokane, Jianguo Zhang, Zuxin Liu, Ming Zhu, Jake Grigsby, Tian Lan, Michael S Ryoo, Chien-Sheng Wu, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong, Juan Carlos Niebles

Challenge:	Large Action Models (LAMs) face challenges due to the need for high-quality training data, especially for multi-steps tasks that involve planning, executing tool calls, and responding to feedback.
Approach:	They propose a framework for online exploration of agentic tasks with high-quality feedback . they use a dynamic task query generator and an extensive collection of tools to create a high-level feedback environment for LLM Agents.
Outcome:	The proposed framework achieves 49.3% performance improvement over baselines on toolbench and CRMArena.

PECAN: LLM-Guided Dynamic Progress Control with Attention-Guided Hierarchical Weighted Graph for Long-Document QA (2025.findings-acl)

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Xinyu Wang, Yanzheng Xiang, Lin Gui, Yulan He

HEGEL: Hypergraph Transformer for Long Document Summarization (2022.emnlp-main)

Challenge:	Long-document Question Answering (QA) challenges with large-scale text and long-distance dependencies.
Approach:	They propose a method that leverages large language models to control retrieval process . they propose 'attention-based' retrieval methods that construct hierarchical graphs .
Outcome:	The proposed method achieves LLM-level performance while maintaining computational complexity comparable to RAG methods.

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Haopeng Zhang, Xiao Liu, Jiawei Zhang

Mistake Notebook Learning: Batch-Clustered Failures for Training-Free Agent Adaptation (2026.findings-acl)

Challenge:	Abstract: Extractive summarization for long documents is challenging due to the extended structured input context.
Approach:	They propose a hypergraph neural network for extractive summarization by capturing cross-sentence relations.
Outcome:	The proposed model can capture cross-sentence relations and latent topics and keywords coreference, and section structure, and can be applied to scientific papers.

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Xuanbo Su, Yingfang Zhang, Hao Luo, Xiaoteng Liu, Leo Huang

Text Clustering

Challenge:	Mistake Notebook Learning (MNL) is a new memory framework for large language model agents . it allows agents to distill shared error patterns into structured "mistake notes"
Approach:	They propose a new memory framework that enables agents to self-curate generalizable guidance from batch-clustered failures.
Outcome:	The proposed framework achieves competitive performance compared to existing memory mechanisms.

CoLA: Collaborative Low-Rank Adaptation (2025.findings-acl)

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Yiyun Zhou, Chang Yao, Jingyuan Chen

From Knowledge to Treatment: Large Language Model Assisted Biomedical Concept Representation for Drug Repurposing (2025.findings-emnlp)

Challenge:	The scaling law of Large Language Models (LLMs) reveals diminishing return on performance as model scale increases.
Approach:	They propose a more flexible LoRA architecture with an efficient initialization scheme . they propose combining three collaborative strategies to enhance performance .
Outcome:	The proposed model outperforms existing methods in low-sample scenarios.

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Chengrui Xiang, Tengfei Ma, Xiangzheng Fu, Yiping Liu, Bosheng Song, Xiangxiang Zeng

Monitoring Decoding: Mitigating Hallucination via Evaluating the Factuality of Partial Response during Generation (2025.findings-acl)

Challenge:	Existing methods for drug repurposing ignore common-sense biomedical concept knowledge in real-world labs, such as mechanistic priors indicating that certain drugs are fundamentally incompatible with specific treatments.
Approach:	They propose a Large Language Model-assisted framework for Drug Repurposing which improves the representation of biomedical concepts within KGs.
Outcome:	The proposed framework improves the representation of biomedical concepts within KGs by extracting treatment-related textual representations of biomedic entities from large language models and fine-tuning knowledge graph embedding models.

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Yurui Chang, Bochuan Cao, Lu Lin

IRT-Router: Effective and Interpretable Multi-LLM Routing via Item Response Theory (2025.acl-long)

Challenge:	Existing methods to mitiga hallucinations rely on sampling multiple full-length generations, which introduces significant response latency and becomes ineffective when the model consistently produces hallucines.
Approach:	They propose a framework that dynamically monitors the generation process and selectively applies in-process interventions to revise hallucination-prone tokens.
Outcome:	The proposed framework outperforms self-consistency-based approaches in both effectiveness and efficiency, achieving higher factual accuracy while significantly reducing computational overhead.

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Wei Song, Zhenya Huang, Cheng Cheng, Weibo Gao, Bihan Xu, GuanHao Zhao, Fei Wang, Runze Wu

Linguistic Theories

Challenge:	Large language models have demonstrated exceptional performance across a wide range of tasks . however, selecting the optimal LLM to respond to a user query often necessitates a delicate balance between performance and cost.
Approach:	They propose a multi-LLM routing framework that efficiently routes user queries to the most suitable LLM.
Outcome:	The proposed framework outperforms baseline methods in terms of effectiveness and interpretability.

Curriculum Consistency Learning for Conditional Sentence Generation (2024.emnlp-main)

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Liangxin Liu, Xuebo Liu, Lian Lian, Shengjun Cheng, Jun Rao, Tengfei Yu, Hexuan Deng, Min Zhang

UniKER: A Unified Framework for Combining Embedding and Definite Horn Rule Reasoning for Knowledge Graph Inference (2021.emnlp-main)

Challenge:	Consistency learning (CL) has proven to be a valuable technique for improving the robustness of conditional sentence generation models.
Approach:	They propose a strategy that guides models to learn consistency in alignment with their current capacity to differentiate between features.
Outcome:	The proposed strategy delivers +2.0 accuracy point improvement compared with vanilla IT and +0.7 COMET scores over traditional CL methods in MT tasks.

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Kewei Cheng, Ziqing Yang, Ming Zhang, Yizhou Sun

Structured Data in NLP

Challenge:	Knowledge graph inference has been studied extensively due to its wide applications.
Approach:	They propose a framework that restricts logical rules to be definite Horn rules and can exploit the knowledge in logical rule-based reasoning and KGE in an extremely efficient way.
Outcome:	The proposed framework can exploit the knowledge in logical rules and improve KGE in an extremely efficient way.

Improving Machine Translation with Large Language Models: A Preliminary Study with Cooperative Decoding (2024.findings-acl)

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Jiali Zeng, Fandong Meng, Yongjing Yin, Jie Zhou

Multilinguality

Challenge:	Commercial machine translation engines are proficient in addressing the majority of translation requirements.
Approach:	They propose to combine NMT and MT-oriented LLMs to achieve superior translation quality by combining their strengths.
Outcome:	The proposed model can handle complex scenarios beyond the capability of NMT alone.

Train Once, Deploy Anywhere: Matryoshka Representation Learning for Multimodal Recommendation (2024.findings-emnlp)

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Yueqi Wang, Zhenrui Yue, Huimin Zeng, Dong Wang, Julian McAuley

Rethinking Negative Pairs in Code Search (2023.emnlp-main)

Challenge:	integrating rich multimodal knowledge into recommender systems remains a challenge . despite performance improvements, different recommendation scenarios often require varying granularities.
Approach:	They propose a framework that captures item features at different granularities and learns informative representations for efficient recommendation across multiple dimensions.
Outcome:	The proposed framework achieves superior performance over state-of-the-art models on multiple benchmark datasets.

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Haochen Li, Xin Zhou, Anh Luu, Chunyan Miao

USSA: A Unified Table Filling Scheme for Structured Sentiment Analysis (2023.acl-long)

Challenge:	Comparative learning is a key component in fine-tuning code search models . however, negative samples of InfoNCE may deteriorate its representation learning .
Approach:	They propose a loss function that inserts weight terms into InfoNCE to improve contrastive learning.
Outcome:	The proposed loss function is a special case of Soft-InfoNCE, the authors show . it is more accurate than other loss functions, and it is faster than other models.

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Zepeng Zhai, Hao Chen, Ruifan Li, Xiaojie Wang

Structured Data in NLP

Challenge:	Structured Sentiment Analysis (SSA) is a problem of bi-lexical dependency parsing . previous studies have cast it as a bottleneck because of overlap and discontinuity issues .
Approach:	They propose a bi-lexical dependency parsing graph and a table-filling scheme that addresses overlap and discontinuity issues.
Outcome:	The proposed framework outperforms state-of-the-art methods on benchmark datasets.

Cross-modality Information Check for Detecting Jailbreaking in Multimodal Large Language Models (2024.findings-emnlp)

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Yue Xu, Xiuyuan Qi, Zhan Qin, Wenjie Wang

Scalable Efficient Training of Large Language Models with Low-dimensional Projected Attention (2024.emnlp-main)

Challenge:	Multimodal Large Language Models (MLLMs) are susceptible to jailbreak attacks, authors say . multimodal information increases the risk of attacks, but also provides additional data .
Approach:	They propose a jailbreaking detector that detects maliciously perturbed image inputs . cross-modality information detector is designed to detect cross-modal similarity between harmful queries and adversarial images.
Outcome:	a new tool can detect maliciously perturbed image inputs without modification or computation cost.

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Xingtai Lv, Ning Ding, Kaiyan Zhang, Ermo Hua, Ganqu Cui, Bowen Zhou

DynaThink: Fast or Slow? A Dynamic Decision-Making Framework for Large Language Models (2024.emnlp-main)

Challenge:	Existing studies have found that low-rank pre-training often compromises effectiveness.
Approach:	They propose to apply low-dimensional module only to the attention layer to improve both effectiveness and efficiency.
Outcome:	The proposed model saves 12.4% time while improving test perplexity and on downstream tasks compared with vanilla Transformer.

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Jiabao Pan, Yan Zhang, Chen Zhang, Zuozhu Liu, Hongwei Wang, Haizhou Li

Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub (2025.acl-long)

Challenge:	Large language models (LLMs) have emerged as prominent foundation models for diverse applications due to their outstanding ability to understand and generate humanlike text.
Approach:	They propose a dynamic decision-making framework that categorizes tasks into two distinct pathways: 'Fast' and 'Slow' they propose 'self-consistency' strategy to replace the straight-forward decoding method used in COT prompting .
Outcome:	The proposed method achieves more than 3% increase in accuracy with lower cost on five popular reasoning benchmarks.

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Bohan Lyu, Xin Cong, Heyang Yu, Pan Yang, Cheng Qian, Zihe Wang, Yujia Qin, Yining Ye, Yaxi Lu, Chen Qian, Zhong Zhang, Yukun Yan, Yankai Lin, Zhiyuan Liu, Maosong Sun

Knowledge-enhanced Prompt Tuning for Dialogue-based Relation Extraction with Trigger and Label Semantic (2024.lrec-main)

Challenge:	Existing approaches lack flexibility to address diverse and ever-evolving user queries in open domains.
Approach:	They propose to evaluate LLMs on open-domain knowledge that requires tools to solve diverse and ever-evolving user queries.
Outcome:	The proposed system outperforms baselines in the open domain task-solving benchmark.

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Hao An, Zhihong Zhu, Xuxin Cheng, Zhiqi Huang, Yuexian Zou

Cut Your Losses! Learning to Prune Paths Early for Efficient Parallel Reasoning (2026.acl-long)

Challenge:	Existing methods to determine semantic relation between two arguments in dialogues are limited due to the low information density of text.
Approach:	They propose a Knowledge-Enhanced Prompt-Tuning method to enhance DRE model by exploiting trigger and label semantics.
Outcome:	The proposed method achieves state-of-the-art in F1 and F1c scores on a DialogRE dataset.

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Jiaxi Bi, Tongxu Luo, Wenyu Du, Zhengyang Tang, Benyou Wang

RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment (2025.findings-acl)

Challenge:	Parallel reasoning enhances Large Reasoning Models but incurs prohibitive costs due to futile paths caused by early errors.
Approach:	They propose a systematic taxonomy of path pruning to categorize methods by signal source and learnability.
Outcome:	The proposed model improves LRMs but incurs prohibitive costs due to futile paths caused by early errors.

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Zhuoran Jin, Hongbang Yuan, Tianyi Men, Pengfei Cao, Yubo Chen, Jiexin Xu, Huaijun Li, Xiaojian Jiang, Kang Liu, Jun Zhao

CMR Scaling Law: Predicting Critical Mixture Ratios for Continual Pre-training of Language Models (2024.emnlp-main)

Challenge:	Existing retrieval augmented language models often overlook effective alignment with human preferences.
Approach:	They propose a benchmark to evaluate RMs in retrieval augmented language models . they incorporate 18 RAG subsets, six retrievers, and 24 RALMs to increase diversity .
Outcome:	The proposed benchmark combines 18 RAG subsets, six retrievers, and 24 RALMs to increase diversity of data sources.

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Jiawei Gu, Zacc Yang, Chuanghao Ding, Rui Zhao, Fei Tan

Distract Large Language Models for Automatic Jailbreak Attack (2024.emnlp-main)

Challenge:	Large Language Models (LLMs) excel in diverse tasks but often underperform in specialized fields due to limited domain-specific or proprietary corpus.
Approach:	They propose a power-law relationship between loss, mixture ratio, and training tokens scale and formalize the trade-off between general and domain-specific capabilities.
Outcome:	The proposed model achieves the desired domain transfer while maintaining general ability and highest utilization of available resources.

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Zeguan Xiao, Yan Yang, Guanhua Chen, Yun Chen

CoRec: An Easy Approach for Coordination Recognition (2023.emnlp-main)

Challenge:	Commercial large language models (LLMs) have made great progress in various NLP tasks.
Approach:	They propose a black-box jailbreak framework for automated red teaming of Large language models using an iterative optimization algorithm to conceal malicious content and memory reframing.
Outcome:	The proposed framework outperforms existing jailbreak defense methods and highlights the need to develop more effective and practical defense strategies.

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Qing Wang, Haojie Jia, Wenfei Song, Qi Li

Syntactic Text Processing

Challenge:	Existing syntactic parsers are slow and suffer from errors, especially for long and complicated sentences.
Approach:	They propose a pipeline model COordination RECognizer with coordinator identifier and conjunct boundary detector.
Outcome:	The proposed model improves the yield of state-of-the-art Open IE models by reducing errors and slow processing time.

FacLens: Transferable Probe for Foreseeing Non-Factuality in Fact-Seeking Question Answering of Large Language Models (2025.emnlp-main)

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Yanling Wang, Haoyang Li, Hao Zou, Jing Zhang, Xinlei He, Qi Li, Ke Xu

From Outcomes to Processes: Guiding PRM Learning from ORM for Inference-Time Alignment (2025.acl-long)

Challenge:	Existing non-factuality detection methods require response generation, which incurs significant computational overhead.
Approach:	They propose a lightweight model called Factuality Lens which effectively probes hidden representations of fact-seeking questions for the NFP task.
Outcome:	The proposed model is able to probe hidden representations of fact-seeking questions and reduce development costs.

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Bin Xie, Bingbing Xu, Yige Yuan, Shengmao Zhu, Huawei Shen

Summarization

Challenge:	Existing approaches to align large language models with human preferences suffer from inconsistent scoring and suboptimal alignment.
Approach:	They propose a dual-consistency framework that aligns partial sequences with human preferences.
Outcome:	The proposed framework significantly reduces granularity discrepancies and improves GPT-4 evaluation scores.

E2LLM: Encoder Elongated Large Language Models for Long-Context Understanding and Reasoning (2025.emnlp-main)

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Zihan Liao, Jun Wang, Hang Yu, Lingxiao Wei, Jianguo Li, Jun Wang, Wei Zhang

Mitigating Hallucinations in LM-Based TTS Models via Distribution Alignment Using GFlowNets (2025.emnlp-main)

Challenge:	Considerable efforts have been and are still being put into increasing the context length of Large Language Models (LLMs)
Approach:	They propose an approach that divides long contexts into chunks, compresses each into soft prompts using a pretrained text encoder, and aligns these representations with a decoder-only LLM via an adapter.
Outcome:	The proposed approach outperforms 8 state-of-the-art methods in effectiveness and efficiency for document summarization and question answering, and achieves the best performance on LongBench v2 among models of comparable size.

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Chenlin Liu, Minghui Fang, Patrick Zhang, Wei Zhou, Jie Gao, Jiqing Han

Mitigating Hallucinations in VLMs: Enhancing Visual Attention via Head-Wise Perturbation (2026.findings-acl)

Challenge:	Existing mitigation strategies for Text-to-Speech systems require excessive training resources or inference latency.
Approach:	They propose a GFlOwNet-guided distribution AlignmenT framework that mitigates hallucinations without relying on massive resources or inference latency.
Outcome:	The proposed framework reduces over 50% character error rates and lowers uncertainty by up to 58% on challenging test cases.

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Zhenghua Wang, Yixin Wu, Feiran Zhang, Qi Qian, Changze Lv, Xuanjing Huang, Xiaoqing Zheng

PruneVid: Visual Token Pruning for Efficient Video Large Language Models (2025.findings-acl)

Challenge:	Vision–Language Models (VLMs) have demonstrated strong capabilities in tasks that require joint understanding of text and images.
Approach:	They propose a strategy that incorporates head-wise attention perturbation via continuous multiplicative noise coupled with a visual-guided loss focused on vision-sensitive text tokens to promote a more balanced attention distribution.
Outcome:	The proposed approach outperforms baseline models on three benchmarks and consistently outperformed the baseline model.

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Xiaohu Huang, Hao Zhou, Kai Han

Diagnosing Failures in Large Language Models’ Answers: Integrating Error Attribution into Evaluation Framework (2025.findings-acl)

Challenge:	Existing approaches to video token pruning face significant computational challenges due to the redundancy inherent in video data.
Approach:	They propose a training-free visual token pruning method that reduces the redundancy inherent in video data and leverages LLMs’ inherent ability to selectively prune visual tokens irrelevant to specific queries.
Outcome:	The proposed method can prune over 80% of tokens while maintaining competitive performance when combined with different video LLMs.

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Zishan Xu, Shuyi Xie, Qingsong Lv, Shupei Xiao, Linlin Song, Sui Wenjuan, Fan Lin

Mitigating Lost-in-Retrieval Problems in Retrieval Augmented Multi-Hop Question Answering (2025.acl-long)

Challenge:	Existing evaluation models lack error attribution capability due to their proprietary nature.
Approach:	They propose a misattribution framework with 6 primary and 15 secondary categories to facilitate in-depth analysis.
Outcome:	The proposed framework is based on a dataset specifically designed for error attribution, along with the corresponding scores and feedback.

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Rongzhi Zhu, Xiangyu Liu, Zequn Sun, Yiwei Wang, Wei Hu

Prompt-based Zero-shot Relation Extraction with Semantic Knowledge Augmentation (2024.lrec-main)

Challenge:	Empirical results show that ChainRAG consistently outperforms baselines in both effectiveness and efficiency.
Approach:	They propose a method which sequentially handles each sub-question by completing missing key entities and retrieving relevant sentences from a sentence graph for answer generation.
Outcome:	The proposed method outperforms baselines on three multi-hop QA datasets.

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Jiaying Gong, Hoda Eldardiry

Low-Resource NLP

Challenge:	Existing approaches to recognize unseen relations for which there are no training instances are lacking in the real-world setting.
Approach:	They propose a prompt-based model with semantic knowledge augmentation to recognize unseen relations under zero-shot setting.
Outcome:	The proposed model outperforms existing methods under zero-shot setting on three datasets.

TASO: Task-Aligned Sparse Optimization for Parameter-Efficient Model Adaptation (2025.emnlp-main)

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Daiye Miao, Yufang Liu, Jie Wang, Changzhi Sun, Yunke Zhang, Demei Yan, Shaokang Dong, Qi Zhang, Yuanbin Wu

ICL-Bandit: Relevance Labeling in Advertisement Recommendation Systems via LLM (2025.findings-emnlp)

Challenge:	Existing studies have shown that LoRA introduces substantial parameter redundancy, which not only increases the number of trainable parameters but also hinders the effectiveness of fine-tuning.
Approach:	They propose a method that leverages importance information from the pretrained model’s weights to mitigate LoRA redundancy.
Outcome:	The proposed method significantly reduces the number of trainable parameters required for task adaptation while providing a task-aligned perspective for LoRA redundancy reduction.

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Lu Wang, Chiming Duan, Pu Zhao, Fangkai Yang, Yong Shi, Xuefeng Luo, Bingjing Xu, Weiwei Deng, Qingwei Lin, Dongmei Zhang

SmartTrim: Adaptive Tokens and Attention Pruning for Efficient Vision-Language Models (2024.lrec-main)

Challenge:	In-context learning (ICL) is a common practice to enhance LLM performance on domain-specific tasks.
Approach:	They propose a method that leverages large language models to enhance query-ad relevance labeling . they identify and provide superior demonstrations for ICL, thereby improving labeling performance .
Outcome:	The proposed method improves query-ad relevance labeling performance by providing demonstrations.

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Zekun Wang, Jingchang Chen, Wangchunshu Zhou, Haichao Zhu, Jiafeng Liang, Liping Shan, Ming Liu, Dongliang Xu, Qing Yang, Bing Qin

CogSteer: Cognition-Inspired Selective Layer Intervention for Efficiently Steering Large Language Models (2025.findings-acl)

Challenge:	Experimental results show that SmartTrim accelerates the original model by 2-3 times with minimal performance degradation.
Approach:	They propose an adaptive acceleration framework which prunes redundant token representations and attention heads within each layer of the original model.
Outcome:	The proposed framework accelerates the original model by 2-3 times with minimal performance degradation across vision-language tasks.

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Xintong Wang, Jingheng Pan, Liang Ding, Longyue Wang, Longqin Jiang, Xingshan Li, Chris Biemann

On-Policy Self-Distillation for Efficient Diffusion Language Models with Early-Stage Calibration (2026.findings-acl)

Challenge:	Large Language Models (LLMs) achieve excellent performance through pretraining on extensive data.
Approach:	They propose an efficient selective layer intervention based on parameter-efficient fine-tuning methods to select the optimal steering layer to modulate LLM semantics.
Outcome:	The proposed approach is based on a model-agnostic framework and is safe to deploy.

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Huaisheng Zhu, MingYu Liu, Junze Liu, Zhen Ge, Tian Wang, Jiri Gesi, Dakuo Wang, Weiqi Zhang, Houyu Zhang, Yufan Guo, Xian Li, Bing Yin, Sujay Sanghavi

EGSS: Entropy-guided Stepwise Scaling for Reliable Software Engineering (2026.acl-long)

Challenge:	Recent studies have demonstrated that masked diffusion models (MDMs) can surpass autoregressive models (ARMs) in various tasks.
Approach:	They propose a method to calibrate early token predictions without demonstration data by distilling an unnormalized target distribution into the original model.
Outcome:	Experiments on math, planning, and RLHF tasks show that COPSD improves both effectiveness and efficiency, and further enhances performance when combined with supervised fine-tuning.

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Chenhui Mao, Yuanting Lei, Zhixiang Wei, Ming Liang, Zhixiang Wang, Jingxuan Xu, Dajun Chen, Wei Jiang, Yong Li

Pre-trained Models Perform the Best When Token Distributions Follow Zipf’s Law (2025.emnlp-main)

Challenge:	Entropy-Guided Stepwise Scaling (EGSS) is a novel TTS framework for software engineering tasks.
Approach:	They propose an entropy-guided stepwise scaling framework that balances efficiency and effectiveness through entropic-guide encoding and robust test-suite augmentation.
Outcome:	EGSS boosts performance by 5–10% across all evaluated models, and reduces inference-time token usage by over 28% . compared to existing methods, EGS reduces token usage and reduce inference time by over 20% .

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Yanjin He, Qingkai Zeng, Meng Jiang

Bypass Back-propagation: Optimization-based Structural Pruning for Large Language Models via Policy Gradient (2025.acl-long)

Challenge:	Existing large language models typically fix a vocabulary size in advance, then use Byte Pair Encoding (BPE) to construct the tokenizer.
Approach:	They propose a method for determining the vocabulary size by analyzing token frequency distributions through Zipf’s law and propose to use it to optimize model performance.
Outcome:	The proposed method improves model efficiency and effectiveness across NLP, genomics, and chemistry.

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Yuan Gao, Zujing Liu, Weizhong Zhang, Bo Du, Gui-Song Xia

SpeechMedAssist: Efficiently and Effectively Adapting Speech Language Models for Medical Consultation (2026.acl-long)

Challenge:	Recent pruning methods rely on heuristically hand-crafted metrics, leading to suboptimal performance.
Approach:	They propose a method that optimizes pruning masks by minimizing back-propagation . they learn an underlying Bernoulli distribution to sample binary pruning mask samples .
Outcome:	The proposed method is able to support global and heterogeneous pruning without back-propagation.

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Sirry Chen, Jieyi Wang, Wei Chen, Zhongyu Wei

Optimizing User Profiles via Contextual Bandits for Retrieval-Augmented LLM Personalization (2026.acl-long)

Challenge:	Recent advances in speech language models have enabled more natural speech-based interactions, but the scarcity of medical speech data and the inefficiency of fine-tuning on speech data hinder adoption of SpeechLMs in medical consultation.
Approach:	They propose a SpeechLM natively capable of conducting speech-based multi-turn interactions with patients.
Outcome:	The proposed model outperforms baselines in both effectiveness and robustness in most evaluation settings.

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Linfeng Du, Ye Yuan, Zichen Zhao, Fuyuan Lyu, Emiliano Penaloza, Xiuying Chen, Zipeng Sun, Jikun Kang, Laurent Charlin, Xue Liu, Haolun Wu

A Parameter-Efficient and Fine-Grained Prompt Learning for Vision-Language Models (2025.acl-long)

Challenge:	Existing approaches for personalizing large language models require modifying parameters.
Approach:	They propose a lightweight approach to personalizing large language models via retrieval augmentation . relevance serves as an unreliable proxy for utility, they argue .
Outcome:	The proposed framework outperforms strong heuristic and retrieval-augmented baselines on nine personalization tasks.

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Yongbin Guo, Shuzhen Li, Zhulin Liu, Tong Zhang, C.L.Philip Chen

KGE Calibrator: An Efficient Probability Calibration Method of Knowledge Graph Embedding Models for Trustworthy Link Prediction (2025.emnlp-main)

Challenge:	Current vision-language models extract semantic information from large-scale cross-modal associations, limiting performance and efficiency.
Approach:	They propose a detail-oriented prompt learning method to implement fine-grained multi-modal semantic alignment with merely 0.25M trainable parameters.
Outcome:	The proposed method implements fine-grained multi-modal semantic alignment with merely 0.25M trainable parameters.

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Yang Yang, Mohan Timilsina, Edward Curry

Neural Parameter Search for Slimmer Fine-Tuned Models and Better Transfer (2025.acl-long)

Challenge:	Existing methods for probability calibration of knowledge graph embedding models are ill-suited for KGEs.
Approach:	They propose a method to calibrate knowledge graph embedding models for ranking-based link prediction using a Jump Selection Strategy and Multi-Binning Scaling to enhance reliability.
Outcome:	Experiments show that the KGEC outperforms existing calibration methods in terms of effectiveness and efficiency.

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Guodong Du, Zitao Fang, Jing Li, Junlin Li, Runhua Jiang, Shuyang Yu, Yifei Guo, Yangneng Chen, Sim Kuan Goh, Ho-Kin Tang, Daojing He, Honghai Liu, Min Zhang

Zero-Shot Detection of LLM-Generated Text using Temperature Sensitivity (2026.acl-long)

Challenge:	Foundational models and their checkpoints have advanced deep learning, boosting performance across applications.
Approach:	They propose a method for pruning fine-tuned models by calculating differences between them and original model.
Outcome:	The proposed method can improve performance across vision, NLP, and multi-modal benchmarks.

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Shixuan Ma, Jiahao Li, Zhendong Mao, Quan Wang

CodeRipple: Wavelet-Based Detection of LLM-Generated Code (2026.acl-long)

Challenge:	Existing methods for detecting LLM-generated text rely on statistical features that are insufficient for reliable detection.
Approach:	They propose a temperature-sensitive detector that modulates decoding temperature and monitors how probability distributions respond to temperature.
Outcome:	The proposed method is based on a temperature sensitivity feature and a simple zero-shot detector built upon normalized temperature sensitivity.

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Xingyu Yao, Zhendong Mao, Quan Wang

Programming Languages in NLP

Challenge:	Existing training-free detectors rely on global statistics of the Token Perplexity Sequence (TPS) and struggle with code.
Approach:	They propose a training-free detection framework that characterizes TPS morphology across scales.
Outcome:	The proposed framework outperforms existing training-free detectors on three challenging benchmarks spanning programming languages, multiple generating LLMs, and various evasion strategies.

AIPO: Adaptive Information Guided Token-Level Reinforcement Learning for Large Language Model Reasoning (2026.acl-long)

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Bin Chen, Hongfei Ye, Huiyang Wang, Wenxi Liu, Yu Zhang, Furui Liu

Prompt Optimization for Relation Extraction using Reinforcement Learning (2026.findings-acl)

Challenge:	Existing RLVR methods focus on all generated tokens rather than on which tokens contribute to reasoning.
Approach:	They propose to use a Random–Fourier approximation of the Hilbert–Schmidt Independence Criterion to focus updates on decisive tokens discovered on the fly to improve the efficiency of mutual-information estimation.
Outcome:	The proposed approach yields +20% accuracy over strong RLVR baselines while updating merely 10% of tokens, demonstrating superior efficiency and effectiveness.

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Ying Liu, Dong Shuai, Cui Zibo, TengQi Ye, Gang Wu

SynthRL: Scaling Visual Reasoning with Verifiable Data Synthesis (2026.findings-acl)

Challenge:	Existing prompt-based methods rely heavily on large-scale annotated datasets limiting their applicability in domain-specific and low-resource scenarios.
Approach:	They propose a reinforcement learning-based automated prompt optimization framework for domain relation extraction that optimizes prompt quality through interaction with a black-box LLM.
Outcome:	The proposed framework outperforms existing prompt-based methods and supervised baselines on multiple extraction datasets across medical, financial, legal, and news domains.

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Zijian Wu, Jinjie Ni, Xiangyan Liu, Zichen Liu, Hang Yan, Michael Qizhe Shieh