Papers by Meng Chen
WavLLM: Towards Robust and Adaptive Speech Large Language Model (2024.findings-emnlp)
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Shujie Hu, Long Zhou, Shujie Liu, Sanyuan Chen, Lingwei Meng, Hongkun Hao, Jing Pan, Xunying Liu, Jinyu Li, Sunit Sivasankaran, Linquan Liu, Furu Wei
| Challenge: | Recent advances in large language models (LLMs) have expanded their scope to encompass multimodal functions. |
| Approach: | They propose a robust and adaptive speech large language model with dual encoders . they validate the model on universal speech benchmarks and apply it to specialized speech-question-answer datasets based on a CoT approach . |
| Outcome: | The proposed model achieves state-of-the-art performance across a range of speech tasks on the same model size. |
PropGenie: A Multi-Agent Conversational Framework for Real Estate Assistance (2026.eacl-demo)
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| Challenge: | PropGenie is a multi-agent framework based on large language models (LLMs) it provides comprehensive real estate assistance in real-world scenarios . |
| Approach: | They propose a multi-agent framework based on large language models to deliver comprehensive real estate assistance in real-world scenarios. |
| Outcome: | The proposed framework outperforms a general-purpose LLM and a domain-specific chatbot in real-world scenarios. |
KnowledgeBerg: Evaluating Systematic Knowledge Coverage and Compositional Reasoning in Large Language Models (2026.findings-acl)
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| Challenge: | Existing LLMs lack systematic coverage of a bounded knowledge universe and compositional set-based reasoning over that universe. |
| Approach: | They propose a benchmark for multiple-choice questions based on 1,183 enumeration seeds . they use knowledge width, cardinality of required universe, reasoning depth to formalize the challenge . |
| Outcome: | The proposed benchmarks achieve only 5.26–36.88 F1 on universe enumeration and 16.00–44.19 accuracy on knowledge-grounded reasoning. |
Enhancing Cross-Tokenizer Knowledge Distillation with Contextual Dynamical Mapping (2025.findings-acl)
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| Challenge: | Knowledge distillation (KD) approaches focus on homogeneous architectures with identical tokenizers, constraining their applicability in cross-architecture scenarios. |
| Approach: | They propose a framework that uses contextual information to enhance sequence alignment precision and dynamically improves vocabulary mapping. |
| Outcome: | The proposed framework shows significant advantages over existing methods for model compression . it can be used across multiple model families and across multiple benchmarks . |
The JDDC Corpus: A Large-Scale Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service (2020.lrec-1)
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| Challenge: | Existing datasets for human-like dialogue tasks are deficient due to the complexity of human conversations. |
| Approach: | They construct a large-scale Chinese E-commerce conversation corpus with 1 million dialogues, 20 million utterances, and 150 million words. |
| Outcome: | The proposed dataset includes 1 million multi-turn dialogues, 20 million utterances, and 150 million words. |
D2-RAG: Dual-Decision Retrieval-Augmented Generation via Multi-Dimensional Uncertainty and Utility-Aware Decoding (2026.findings-acl)
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Jinshuo Zhang, Xiaoding Zhou, Weiyu Zhang, Guoqiang Chen, Ying Lian, Xiaoyang Meng, Yonghe Chen, Hongjiao Guan, Jiasheng Si, Wenpeng Lu
| Challenge: | Retrieval-Augmented Generation (RAG) mitigates hallucinations in large language models by incorporating external knowledge. |
| Approach: | They propose a dual-decision retrieval-augmented generation that integrates multi-dimensional uncertainty estimation to decide whether to retrieve and employs adaptive contrastive decoding to handle retrieved contexts of varying quality. |
| Outcome: | The proposed model outperforms baselines on four medical question-answering datasets while suppressing interference from noisy contexts. |
Dialog-Post: Multi-Level Self-Supervised Objectives and Hierarchical Model for Dialogue Post-Training (2023.acl-long)
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| Challenge: | a new method for dialogue representation and understanding is proposed . pre-trained language models (PLMs) are inappropriate for dialogue understanding tasks . |
| Approach: | They propose a method that trains pre-trained language models to fit dialogues . they use a hierarchical segment-wise self-attention network to model dialogues more comprehensively . |
| Outcome: | The proposed method outperforms existing models and achieves a 3.3% improvement on average. |
Autoregressive Speech Synthesis without Vector Quantization (2025.acl-long)
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Lingwei Meng, Long Zhou, Shujie Liu, Sanyuan Chen, Bing Han, Shujie Hu, Yanqing Liu, Jinyu Li, Sheng Zhao, Xixin Wu, Helen M. Meng, Furu Wei
| Challenge: | MELLE is a novel language modeling approach for text-to-speech synthesis that generates continuous tokens from text . authors demonstrate that it reduces the need for vector quantization and improves model robustness . |
| Approach: | They propose to autoregressively generate continuous mel-spectrogram frames directly from text condition, bypassing vector quantization. |
| Outcome: | The proposed model achieves superior performance across multiple metrics and is more streamlined. |
Label Anchored Contrastive Learning for Language Understanding (2022.naacl-main)
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| Challenge: | a novel approach to contrastive learning for language understanding is not fully explored . contrastive training has been widely applied to self-supervised representation learning . |
| Approach: | They propose a label anchored contrastive learning approach for language understanding using a class label. |
| Outcome: | The proposed approach improves on GLUE and CLUE benchmarks by 4.1% compared to the state-of-the-art approaches . the proposed approach also improves under the few-shot and data imbalance settings . |
Continual Learning with Semi-supervised Contrastive Distillation for Incremental Neural Machine Translation (2024.acl-long)
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| Challenge: | Multi-domain learning is a good solution for solving domain tasks but it requires retraining when adding a new domain. |
| Approach: | They propose to exploit unlabeled data from the same distributions of the older domains to avoid catastrophic forgetting. |
| Outcome: | The proposed framework exploits unlabeled data from the same distributions of the older domains to avoid catastrophic forgetting. |
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)
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Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin
| Challenge: | a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities . |
| Approach: | They present a comparative analysis to identify and distinguish LLM activities from human activities. |
| Outcome: | The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities. |
Aligning Large Language Models via Fully Self-Synthetic Data (2026.acl-long)
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| Challenge: | Existing approaches to reinforcement learning from human feedback (RLHF) require expensive human-annotated datasets and proprietary models like GPT-4 to annotate preference pairs. |
| Approach: | They propose a self-synthetic framework for LLM alignment where all training data, including prompts (i.e., user queries), responses, and preferences, are generated by the model itself. |
| Outcome: | The proposed framework enhances the model’s chat capabilities on standard benchmarks like AlpacaEval 2.0 while maintaining strong performance on downstream objective tasks. |
Hierarchical Acoustic-Semantic Modeling: Modality Separation and Semantic Coherence for Full-Duplex SLMs (2026.acl-long)
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Zhenyu Liu, Xuanyu Zhang, Yunxin li, Qixun Teng, Shenyuan Jiang, Haolan Chen, Mingjun Zhao, Fanbo Meng, Yu Xu, Yancheng He, Baotian Hu, Haizhou Li, Min Zhang
| Challenge: | despite significant progress, full-duplex SLMs are constrained by severe modality interference, authors say . modality interferes with acoustic and semantic modeling, making them unintelligent and unnatural . authors propose a hierarchical parameter separation strategy that decouples conflicting modalities in deep layers . |
| Approach: | They propose a hierarchical parameter separation strategy that decouples conflicting modalities in deep layers while preserving cross-modality coherence via a dedicated semantic alignment channel. |
| Outcome: | The proposed method significantly advances the state of the art on full-duplex benchmarks . it decouples conflicting modalities in deep layers while preserving cross-modality coherence . |
SG-FSM: A Self-Guiding Zero-Shot Prompting Paradigm for Multi-Hop Question Answering Based on Finite State Machine (2025.findings-naacl)
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Xiaochen Wang, Junqing He, Liang Chen, Gholamreza Haffari, Yiru Wang, Zhe Yang, Xiangdi Meng, Kunhao Pan, Zhifang Sui
| Challenge: | Multi-hop Question Answering (MHQA) is a challenging task that requires models to answer multiple questions with multiple passages. |
| Approach: | They propose a self-guided prompting finite state machine to improve multi-hop reasoning abilities by iterating over multiple questions and correcting itself to improve accuracy. |
| Outcome: | The proposed approach outperforms baselines on Musique and other datasets. |
Towards Making the Most of Dialogue Characteristics for Neural Chat Translation (2021.emnlp-main)
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| Challenge: | Neural Chat Translation (NCT) models that use dialogue characteristics of chat are often incoherent and speakerirrelevant. |
| Approach: | They propose to introduce the modeling of dialogue characteristics into the NCT model by capturing the inherent dialogue characteristics. |
| Outcome: | The proposed model can translate conversational text between speakers of different languages. |
Law in Silico: Simulating Legal Society with LLM-Based Agents (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are powerful tools for legal simulation, but their application remains underexplored. |
| Approach: | They propose a unified LLM-based agent framework for simulating legal scenarios . they calibrate agent behaviors against real-world crime data . |
| Outcome: | The proposed framework calibrates agent behaviors against real-world crime data. |
Generalization in Text-based Games via Hierarchical Reinforcement Learning (2021.findings-emnlp)
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| Challenge: | Reinforcement Learning (RL) based agents are promising for text-based games, but their generalization remains a challenge. |
| Approach: | They propose a hierarchical framework for reinforcement learning based on knowledge graphs . they propose to decompose the game into subtasks and execute a sub-policy in the low level to conduct goal-conditioned reinforcement learning. |
| Outcome: | The proposed framework enjoys favorable generalizability on a set of difficulty levels and is able to handle complex training tasks. |
Ladder: A Model-Agnostic Framework Boosting LLM-based Machine Translation to the Next Level (2024.emnlp-main)
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| Challenge: | General-purpose Large Language Models (LLMs) like GPT-4 have exhibited strong translation abilities. |
| Approach: | They propose to use a model-agnostic model to refine the performance of general-purpose large-language models for machine translation (MT) by utilizing Gemma-2B/7B as the backbone. |
| Outcome: | The proposed model-agnostic and cost-effective tool improves the performance of general-purpose large-language models for machine translation (MT) by integrating it with any general-use LLM. |
The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit (2025.acl-long)
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Huixue Zhou, Hengrui Gu, Zaifu Zhan, Xi Liu, Kaixiong Zhou, Yongkang Xiao, Mingfu Liang, Srinivas Prasad Govindan, Piyush Chawla, Jiyan Yang, Xiangfei Meng, Huayu Li, Buyun Zhang, Liang Luo, Wen-Yen Chen, Yiping Han, Bo Long, Rui Zhang, Tianlong Chen
| Challenge: | Existing frameworks for Large Language Models (LLMs) for Click-Through Rate prediction require a careful balance between computational efficiency and predictive accuracy. |
| Approach: | They propose a framework that integrates Retrieval-Augmented Generation with a novel multi-head early exit architecture to address both challenges. |
| Outcome: | The proposed framework reduces retrieval time while maintaining high model performance. |
DOSE: Data Selection for Multi-Modal LLMs via Off-the-Shelf Models (2026.findings-acl)
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| Challenge: | Existing data filtering methods are expensive because they are trained on the same data they are meant to screen. |
| Approach: | They propose to use off-the-shelf pretrained models that have never seen the target data to select training samples for larger and stronger multimodal models without task-specific training. |
| Outcome: | The proposed method can achieve comparable or even better results than those trained on the full dataset in standard VQA and math benchmarks. |
Diffusion Theory as a Scalpel: Detecting and Purifying Poisonous Dimensions in Pre-trained Language Models Caused by Backdoor or Bias (2023.findings-acl)
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| Challenge: | Existing methods to fine-tune pre-trained language models (PLMs) are not safe, since the fine-uning process is invisible to the user. |
| Approach: | They propose a technique to study the dynamic process of fine-tuning for finding poisonous dimensions using diffusion theory. |
| Outcome: | The proposed approach can detect poisonous dimensions with abnormal dynamics, purify them and fine-tune them on a clean dataset. |
Aligning Large Language Models with Implicit Preferences from User-Generated Content (2025.acl-long)
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Zhaoxuan Tan, Zheng Li, Tianyi Liu, Haodong Wang, Hyokun Yun, Ming Zeng, Pei Chen, Zhihan Zhang, Yifan Gao, Ruijie Wang, Priyanka Nigam, Bing Yin, Meng Jiang
| Challenge: | Existing preference learning methods rely heavily on curated data from humans or advanced LLMs, which is costly and difficult to scale. |
| Approach: | They propose a framework that leverages implicit preferences in unlabeled user-generated content to generate preference data. |
| Outcome: | The proposed framework transforms user-generated content into user queries and generates responses from the policy model. |
Spiral of Silence in Large Language Model Agents (2025.findings-emnlp)
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| Challenge: | Existing theories of Spiral of Silence do not apply to large language models . |
| Approach: | They propose an evaluation framework for examining SoS in large language models . they consider four controlled conditions that vary the availability of "History" and "Persona" signals . |
| Outcome: | The proposed framework examines the SoS-like dynamics in large language models . it shows that history and persona together produce strong majority dominance . |
AdaTooler-V: Adaptive Tool-Use for Images and Videos (2026.findings-acl)
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Chaoyang Wang, Kaituo Feng, Dongyang Chen, Zhongyu Wang, Zhixun Li, Sicheng Gao, Meng Meng, Xu Zhou, Manyuan Zhang, Yuzhang Shang, Xiangyu Yue
| Challenge: | Existing models exhibit blind tool-use reasoning patterns, which significantly increases inference overhead and degrades model performance. |
| Approach: | They propose an MLLM that performs adaptive tool-use by determining whether a visual problem truly requires tools. |
| Outcome: | The proposed model outperforms existing methods in visual reasoning tasks. |
Conditional Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing approaches to improve neural machine translation use token-level adaptive training . however, standard models make predictions on condition of previous contexts . |
| Approach: | They propose a target-context-aware metric which can be supplemented by statistical metrics . they propose an adaptive training approach based on token- and sentence-level CBMI . |
| Outcome: | The proposed model outperforms the Transformer baseline and other similar approaches on English-German and Chinese-English tasks. |
Unsupervised Multi-scale Expressive Speaking Style Modeling with Hierarchical Context Information for Audiobook Speech Synthesis (2022.coling-1)
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| Challenge: | a recent study has shown that expressiveness of audiobooks is limited by the averaged global-scale speaking style representation. |
| Approach: | They propose an unsupervised multi-scale context-sensitive text-to-speech model for audiobooks . they use hierarchical context encoder to predict global-scale contextual style embeddings . |
| Outcome: | The proposed model outperforms existing models on a real-world Mandarin audio dataset. |
A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysis (D19-1)
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| Challenge: | Existing models use aspect-independent encoders for sentence representation generation. |
| Approach: | They propose an aspect-guided deep transition model which guides the sentence encoding from scratch with a specially-designed deep transition architecture. |
| Outcome: | The proposed model outperforms existing models on multiple datasets on aspect-category sentiment analysis and aspectterm sentiment analysis without additional features. |
Knowledge-augmented Financial Market Analysis and Report Generation (2024.emnlp-industry)
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| Challenge: | Existing methods to generate financial market analysis text require extensive financial knowledge and skill of financial analysts. |
| Approach: | They propose a task to generate financial market analysis reports using financial market data using a financial knowledge graph. |
| Outcome: | The proposed framework outperforms large-scale language models and retrieval-augmented baselines in the financial market analysis generation task. |
Mitigating Lost in Multi-turn Conversation via Curriculum RL with Verifiable Accuracy and Abstention Rewards (2026.acl-long)
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Ming Li, Pei Chen, Zhenhao Zhang, Tao Yang, Xinyang Zhang, Han Li, Tianyu Cao, Ming Zeng, Zhuofeng Wu, Meng Jiang, Huasheng Li, Lihong Li, Bing Yin
| Challenge: | Large Language Models exhibit strong capabilities in single-turn instruction following but suffer from Lost-in-Conversation (LiC) when instructions are revealed progressively in multi-turn settings, models get "Lost in Conversation" |
| Approach: | They propose a framework that encourages models to generate correct answers and judge solvability in multi-turn conversations. |
| Outcome: | The proposed framework improves models' ability to balance problem-solving with abstention . it reduces premature answering behaviors that cause lost-in-conversation (LiC) |
SpatialWebAgent: Leveraging Large Language Models for Automated Spatial Information Extraction and Map Grounding (2025.acl-demo)
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| Challenge: | Understanding and extracting spatial information from text is vital for a wide range of applications, says nielsen . inherent complexity of geographic expressions in natural language presents significant hurdles for traditional extraction methods. |
| Approach: | They propose a system that leverages large language models to extract spatial information from natural language. |
| Outcome: | SpatialWebAgent is designed to extract, standardize, and ground spatial information from natural language text directly onto maps. |
A Variational Hierarchical Model for Neural Cross-Lingual Summarization (2022.acl-long)
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| Challenge: | Existing studies on cross-lingual summarization focus on pipeline methods or jointly training an end-to-end model through an auxiliary MT or MS objective. |
| Approach: | They propose a hierarchical model for the cross-lingual summarization task . the model is based on the conditional variational auto-encoder . |
| Outcome: | The proposed model generates better cross-lingual summaries than comparison models in the few-shot setting. |
StoryMI: Steerable Multi-Agent Therapeutic Dialogue Generation (2026.findings-acl)
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| Challenge: | Motivational interviewing (MI) is a directive, client-centered counseling approach for eliciting clients' motivation for behavioral change. |
| Approach: | They propose a multi-LLM agent framework for controllable MI dialogue generation . therapist and client agents generate MI-coded utterances guided by MI codes . |
| Outcome: | The proposed framework can generate fluent dialogues with minimal intervention time and a high level of evaluation. |
D2TV: Dual Knowledge Distillation and Target-oriented Vision Modeling for Many-to-Many Multimodal Summarization (2023.findings-emnlp)
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| Challenge: | Existing studies focus on improving MMS models by filtering summary-unrelated visual features with implicit learning or explicitly complex training objectives. |
| Approach: | They propose a multimodal multimodal summarization task that aims to generate summaries in any language with document inputs in any languages and the corresponding image sequence. |
| Outcome: | The proposed task can generate summaries in any language with document inputs in any languages and the corresponding image sequence. |
Comments as Natural Logic Pivots: Improve Code Generation via Comment Perspective (2024.findings-acl)
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| Challenge: | Existing studies decompose complex tasks into intermediate steps by prompting strategies, such as Chain-of-Thought and its variants. |
| Approach: | They propose to use code comments as natural logic pivot between natural language and code language to boost the code generation ability of code LLMs. |
| Outcome: | The proposed method significantly improves the code pass rate on humanEval and MBPP, while the robustness of the logical comment decoding strategy is higher than the Chain-of-thoughts prompting. |
E-ConvRec: A Large-Scale Conversational Recommendation Dataset for E-Commerce Customer Service (2022.lrec-1)
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Meihuizi Jia, Ruixue Liu, Peiying Wang, Yang Song, Zexi Xi, Haobin Li, Xin Shen, Meng Chen, Jinhui Pang, Xiaodong He
| Challenge: | Recent research has focused on developing conversational recommendation system (CRS), which provides valuable recommendations to users through conversations. |
| Approach: | They construct an authentic Chinese dialogue dataset consisting of over 25k dialogues and 770k utterances, which contains user profile, product knowledge base, and multiple sequential real conversations between users and recommenders. |
| Outcome: | The proposed dataset contains user profile, product knowledge base, and multiple sequential real conversations between users and recommenders. |
Retrieving Sequential Information for Non-Autoregressive Neural Machine Translation (P19-1)
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| Challenge: | Experimental results show that the Reinforce-NAT system surpasses the baseline NAT system by a significant margin on BLEU without decelerating the decoding speed. |
| Approach: | They propose a sequence-level training method and a Transformer decoder to fuse the target sequential information into the top layer of the decoded Transformer. |
| Outcome: | The proposed model surpasses the baseline NAT system on BLEU without decelerating the decoding speed and achieves comparable translation performance to the autoregressive Transformer model with considerable speedup. |
Enhancing Speech Large Language Models with Prompt-Aware Mixture of Audio Encoders (2025.emnlp-main)
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Weiqiao Shan, Yuang Li, Yuhao Zhang, Yingfeng Luo, Chen Xu, Xiaofeng Zhao, Long Meng, Yunfei Lu, Min Zhang, Hao Yang, Tong Xiao, JingBo Zhu
| Challenge: | Existing work on integrating audio encoders with large language models (LLMs) has focused on semantic understanding tasks, but different tasks may require distinct features that emphasize either semantic or acoustic aspects. |
| Approach: | They propose to use a prompt-aware mixture to enhance the Speech LLM that uses multiple audio encoders to extract different features based on the prompt. |
| Outcome: | The proposed approach outperforms all single-encoder Speech LLMs on ASR, speaker number verification, and AC tasks. |
Infinity-Parser: Layout-Aware Reinforcement Learning with High-quality Document Parsing Dataset (2026.findings-acl)
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Baode Wang, Biao Wu, Weizhen Li, Meng Fang, Zuming Huang, Jun Huang, Yanjie Liang, Haozhe Wang, Ling Chen, Wei Chu, Yuan Qi
| Challenge: | Existing supervised fine-tuning methods struggle to generalize across document types, leading to poor performance. |
| Approach: | They propose layoutRL, a reinforcement learning framework that optimizes layout understanding through composite rewards integrating normalized edit distance, paragraph count accuracy, and reading order preservation. |
| Outcome: | The proposed model outperforms specialized document parsing systems and general-purpose vision-language models on a broad range of document types, languages, and structural complexities. |
A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning (2020.coling-main)
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| Challenge: | Paraphrase generation is of great importance for many downstream tasks in natural language processing. |
| Approach: | They propose a method to generate sentences as learning objectives from the learned data distribution and employ reinforcement learning to combine these new learning objectives for model training. |
| Outcome: | The proposed method gains significant diversity and improves generation quality over state-of-the-art datasets. |
Benchmarking Foundation Models with Retrieval-Augmented Generation in Olympic-Level Physics Problem Solving (2025.findings-emnlp)
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| Challenge: | a new study examines the potential of retrieval-augmented generation (RAG) with foundation models to enhance expert-level reasoning. |
| Approach: | They introduce PhoPile, a high-quality multimodal dataset specifically designed for Olympiad-level physics. |
| Outcome: | The proposed model can be used to solve Olympiad-level physics problems. |
Instant Personalized Large Language Model Adaptation via Hypernetwork (2026.acl-long)
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Zhaoxuan Tan, Zixuan Zhang, Haoyang Wen, Zheng Li, Rongzhi Zhang, Pei Chen, Fengran Mo, Zheyuan Liu, Qingkai Zeng, Qingyu Yin, Meng Jiang
| Challenge: | Existing parameter-efficient fine-tuning methods require training a separate adapter for each user, making them computationally expensive and impractical for real-time updates. |
| Approach: | They propose a scalable framework that maps a user's profile directly to a full set of adapter parameters. |
| Outcome: | The proposed framework outperforms prompt-based personalization and OPPU while using substantially fewer computational resources at deployment. |
AdaMARP: An Adaptive Multi-Agent Interaction Framework for General Immersive Role-Playing (2026.findings-acl)
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| Challenge: | Existing LLMs lack immersion and adaptability, resulting in limited character orchestration and on-the-fly character introduction. |
| Approach: | They propose an LLM-based framework that allows actors to interact with users in an ongoing narrative. |
| Outcome: | The proposed framework outperforms commercial LLMs in character consistency, environment grounding, and narrative coherence. |
UniConv: Unifying Retrieval and Response Generation for Large Language Models in Conversations (2025.acl-long)
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Fengran Mo, Yifan Gao, Chuan Meng, Xin Liu, Zhuofeng Wu, Kelong Mao, Zhengyang Wang, Pei Chen, Zheng Li, Xian Li, Bing Yin, Meng Jiang
| Challenge: | Existing conversational search systems are usually built with two different models . this separation restricts the system from leveraging the model's intrinsic knowledge simultaneously . Existing studies for developing unified models cannot fully address the aspects of understanding conversational context, managing retrieval independently, and generating responses. |
| Approach: | They propose to unify dense retrieval and response generation for large language models in conversation by fine-tuning and mitigating data discrepancy. |
| Outcome: | The proposed model can outperform existing models on five conversational search datasets and reduce inconsistency risks while mitigating data discrepancy. |
TriSPrompt: A Hierarchical Soft Prompt Model for Multimodal Rumor Detection with Incomplete Modalities (2025.findings-emnlp)
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| Challenge: | Existing multimodal rumor detection methods focus on learning joint modality representations from complete multimodal training data, rendering them ineffective in addressing the common occurrence of missing modalities in real-world scenarios. |
| Approach: | They propose a hierarchical soft prompt model TriSPrompt which integrates three types of prompts to effectively detect rumors in incomplete multimodal data. |
| Outcome: | The proposed model achieves an accuracy gain of over 13% compared to state-of-the-art models. |
MSCTD: A Multimodal Sentiment Chat Translation Dataset (2022.acl-long)
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| Challenge: | Multimodal machine translation and textual chat translation have received considerable attention . however, little research has been devoted to multimodal machine translator in conversations . |
| Approach: | They propose a task to generate more accurate translations with the help of dialogue history and visual context. |
| Outcome: | The proposed task can generate more accurate translations with the help of dialogue history and visual context. |
How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) are impressive in solving tasks, but they can quickly be outdated after deployment. |
| Approach: | They provide a review of recent advances in aligning deployed large language models with the ever-changing world knowledge. |
| Outcome: | The proposed models can be used to perform various tasks directly through in-context learning or for further fine-tuning for domain-specific uses. |
Accelerating Dense LLMs via L0-regularized Mixture-of-Experts (2025.acl-short)
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| Challenge: | Existing methods for accelerating large language models (LLMs) suffer from slow and costly inference. |
| Approach: | They propose a lightweight MoE approach using cluster confusion matrix and dynamic batching to accelerate dense LLMs. |
| Outcome: | The proposed method achieves 2.5x speedup over dense models while maintaining competitive performance. |
SED-SFT: Selectively Encouraging Diversity in Supervised Fine-Tuning (2026.acl-short)
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| Challenge: | Existing studies have proposed a new approach to optimize for SFT followed by RL . existing studies have suggested a method to optimize SFT for large language models . |
| Approach: | They propose a framework that encourages diversity based on token exploration space. |
| Outcome: | Experiments show that SED-SFT significantly improves generation diversity with a negligible computational overhead increase over CE loss. |
CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Models (2023.acl-long)
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| Challenge: | Existing studies on social biases in language models have focused on only English. |
| Approach: | They propose to use a Chinese dataset for bias evaluation and mitigation of Chinese conversational language models. |
| Outcome: | The proposed dataset includes under-explored bias categories, such as ageism and appearance biases, which received less attention in previous studies. |
LCS: A Language Converter Strategy for Zero-Shot Neural Machine Translation (2024.findings-acl)
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| Challenge: | Existing LT strategies cannot indicate the desired target language on zero-shot translation, i.e., the off-target issue. |
| Approach: | They propose a language converter strategy that embeds the target language into the top encoder layers to mitigate confusion in the encoder and ensures stable language indication for the decoder. |
| Outcome: | The proposed language converter strategy significantly mitigates off-target issue on multiUN, TED, and OPUS-100 datasets. |
RetrievalQA: Assessing Adaptive Retrieval-Augmented Generation for Short-form Open-Domain Question Answering (2024.findings-acl)
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| Challenge: | Existing methods for assessing retrieval of relevant information are understudied . previous studies have neglected to evaluate ARAG methods . |
| Approach: | They propose a benchmark to evaluate existing ARAG methods that use threshold tuning to adjust retrieval for queries instead of indiscriminate retrieval. |
| Outcome: | The proposed method can be used to evaluate existing ARAG methods without calibration or training. |
Ruleformer: Context-aware Rule Mining over Knowledge Graph (2022.coling-1)
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| Challenge: | Existing work on rule mining focuses on mining rules, but how to select appropriate rules for completion of different triplets has not been discussed. |
| Approach: | They propose to take context information into consideration when selecting suitable rules . they devise a transformer-based rule mining approach, Ruleformer . |
| Outcome: | The proposed model takes context information into consideration, which helps select suitable rules for inference tasks. |
Tackling Modality Heterogeneity with Multi-View Calibration Network for Multimodal Sentiment Detection (2023.acl-long)
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| Challenge: | Existing studies focus on fusing different features but ignore the challenge of modality heterogeneity. |
| Approach: | They propose a text-guided fusion module with novel Sparse-Attention to reduce the negative impacts of redundant visual elements and a sentiment-based congruity constraint task to calibrate the feature shift in the representation space. |
| Outcome: | The proposed model is competitive against existing methods and achieves state-of-the-art results on two public benchmark datasets. |
MultiCodeAttack: Iterative Jailbreak Attacking on LLMs with Multi-Code Prompt Injection (2026.findings-acl)
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| Challenge: | Existing approaches to jailbreak rely on fixed template design and a single programming language . however, existing approaches do not consider language diversity or adaptive template evolution . |
| Approach: | They propose a structured jailbreak framework that explores and optimizes multi-language code templates. |
| Outcome: | The proposed framework outperforms existing jailbreak baselines and produces higher harmful outputs than baseline methods. |
See the World, Discover Knowledge: A Chinese Factuality Evaluation for Large Vision Language Models (2025.findings-acl)
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Jihao Gu, Yingyao Wang, Pi Bu, Chen Wang, Ziming Wang, Tengtao Song, Donglai Wei, Jiale Yuan, Yingxiu Zhao, Yancheng He, Shilong Li, Jiaheng Liu, Meng Cao, Jun Song, Yingshui Tan, Xiang Li, Wenbo Su, Xiaoyong Zhu, Bo Zheng
| Challenge: | Existing models for large vision language models do not fully reflect their knowledge capacity and reliability, resulting in erroneous outputs that do not align with the image content or provide answers lacking knowledge evidence. |
| Approach: | They propose a Chinese-based benchmark for visual factuality across 8 major topics and 56 subtopics and a multi-hop question construction. |
| Outcome: | The proposed model decouples visual factuality into two parts: seeing the world and discovering knowledge. |
Modeling Bilingual Conversational Characteristics for Neural Chat Translation (2021.acl-long)
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| Challenge: | Neural chat translation aims to translate bilingual conversational text due to its inherent characteristics such as role preference, dialogue coherence, and translation consistency. |
| Approach: | They propose to model the translation quality of conversational text by learning distributions of bilingual conversational characteristics. |
| Outcome: | The proposed approach outperforms baseline models and is widely available. |
DocLens: A Tool-Augmented Multi-Agent Framework for Long Visual Document Understanding (2026.acl-long)
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| Challenge: | Existing approaches to localizing evidence from long visual documents fail on a fundamental challenge: evidence localization. |
| Approach: | They propose a tool-augmented multi-agent framework that “zooms in” on evidence like a lens. |
| Outcome: | The proposed framework achieves state-of-the-art performance on MMLongBench-Doc and FinRAGBench-V, surpassing even human experts. |
Confidence-Aware Scheduled Sampling for Neural Machine Translation (2021.findings-acl)
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| Challenge: | Neural machine translation models are trained to maximize the likelihood of the next token given previous golden tokens as inputs, but at the inference stage, golden token is unavailable. |
| Approach: | They propose a scheduled sampling method that randomly replaces groundtruth tokens with predicted ones during training, ignoring real-time model competence. |
| Outcome: | The proposed method outperforms the Transformer and vanilla scheduled sampling on large-scale translations. |
Target-oriented Fine-tuning for Zero-Resource Named Entity Recognition (2021.findings-acl)
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| Challenge: | Named entity recognition (NER) is one of the fundamental tasks in natural language processing. |
| Approach: | They propose four practical guidelines to guide knowledge transfer and task finetuning . they propose a framework to exploit data from three aspects in a unified training manner . |
| Outcome: | The proposed framework improves on six benchmarks and shows that it is state-of-the-art in five languages. |
PresentAgent: Multimodal Agent for Presentation Video Generation (2025.emnlp-demos)
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| Challenge: | Existing methods for generating static slides or text summaries are limited to producing narrated presentations. |
| Approach: | They propose a multimodal agent that transforms long-form documents into narrated presentations. |
| Outcome: | The present agent produces fully synchronized visual and spoken content that closely mimics human-style presentations. |
Improving Large Language Models Function Calling and Interpretability via Guided-Structured Templates (2025.emnlp-main)
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Hy Dang, Tianyi Liu, Zhuofeng Wu, Jingfeng Yang, Haoming Jiang, Tao Yang, Pei Chen, Zhengyang Wang, Helen Wang, Huasheng Li, Bing Yin, Meng Jiang
| Challenge: | Large language models (LLMs) have strong reasoning and tool-use capabilities, yet fail in real-world tool-interactions due to incorrect parameterization, poor tool selection, or misinterpretation of user intent. |
| Approach: | They propose a curriculum-inspired framework that leverages structured reasoning templates to guide LLMs through more deliberate step-by-step instructions for generating function calls. |
| Outcome: | The proposed framework reduces tool-use errors and improves interpretability and transparency of tool-using agents. |
MulDimIF: A Multi-Dimensional Constraint Framework for Evaluating and Improving Instruction Following in Large Language Models (2026.findings-acl)
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Junjie Ye, Caishuang Huang, Zhuohan Chen, Wenjie Fu, Chenyuan Yang, Leyi Yang, Yilong Wu, Peng Wang, Meng Zhou, Xiaolong Yang, Tao Gui, Qi Zhang, Zhongchao Shi, Jianping Fan, Xuanjing Huang
| Challenge: | Existing research has focused on constraint categories, offering little guidance for improving instruction following abilities. |
| Approach: | They propose a multi-dimensional constraint framework that allows for instruction following . they construct 9,106 code-verifiable samples and evaluate 18 LLMs . |
| Outcome: | The proposed framework improves instruction following performance without compromising general performance. |
Why Did Apple Fall: Evaluating Curiosity in Large Language Models (2026.findings-acl)
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| Challenge: | Existing methods for evaluating curiosity-like behaviors in large language models lack curiosity-inspired features. |
| Approach: | They propose a psychology-inspired framework to evaluate curiosity in large language models . they adapt the Five-Dimensional Curiosity scale Revised (5DCR) to LLMs . |
| Outcome: | The proposed framework evaluates curiosity in large language models using questionnaires and behavioral studies. |
Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimation (2024.emnlp-main)
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Yuan Ge, Yilun Liu, Chi Hu, Weibin Meng, Shimin Tao, Xiaofeng Zhao, Mahong Xia, Zhang Li, Boxing Chen, Hao Yang, Bei Li, Tong Xiao, JingBo Zhu
| Challenge: | Existing methods for instruction data selection have limitations such as relying on fragile external APIs, being affected by biases in GPT models, or reducing the diversity of the selected instruction dataset. |
| Approach: | They propose an industrial-friendly, expert-aligned and diversity-preserved instruction data selection method: Clustering and Ranking (CaR). |
| Outcome: | The proposed method outperforms Alpaca's existing methods by 32.1% in GPT-4 evaluations. |
CM-Align: Consistency-based Multilingual Alignment for Large Language Models (2025.findings-emnlp)
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| Challenge: | Current large language models (LLMs) show a significant performance gap in alignment between English and other languages. |
| Approach: | They propose a consistency-based method to construct high-quality multilingual preference data for improving multilingual alignment. |
| Outcome: | The proposed method is based on three LLMs and three common tasks and shows that it performs better than current methods. |
Cross-Align: Modeling Deep Cross-lingual Interactions for Word Alignment (2022.emnlp-main)
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| Challenge: | Existing word alignment models capture few interactions between input sentence pairs, which severely degrades the word alignment quality. |
| Approach: | They propose to model deep interactions between input and target sentences using a two-stage training framework to train the model. |
| Outcome: | The proposed model achieves the state-of-the-art (SOTA) performance on four out of five language pairs. |
What is Stigma Attributed to? A Theory-Grounded, Expert-Annotated Interview Corpus for Demystifying Mental-Health Stigma (2025.acl-long)
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| Challenge: | Existing resources for training neural models to finely classify mental-health stigma are limited, relying primarily on social media or synthetic data without theoretical underpinnings. |
| Approach: | They propose to use an expert-annotated corpus of human-chatbot interviews to finely classify mental-health stigma. |
| Outcome: | The proposed corpus can facilitate research on computationally detecting, neutralizing, and counteracting mental-health stigma. |
Towards an On-device Agent for Text Rewriting (2024.findings-naacl)
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Yun Zhu, Yinxiao Liu, Felix Stahlberg, Shankar Kumar, Yu-Hui Chen, Liangchen Luo, Lei Shu, Renjie Liu, Jindong Chen, Lei Meng
| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities for text rewriting, however creating a smaller yet potent language model presents two formidable challenges: costly data collection and absence of emergent capabilities. |
| Approach: | They propose a new instruction tuning method to develop a mo-bile text rewriting model that leverages LLM-generated data and heuristic reinforcement learning, eliminating the need for human data collection. |
| Outcome: | The proposed model surpasses the current state-of-the-art LLMs in text rewriting while maintaining a significantly reduced model size using public benchmark EditEval and our new benchmark. |
On Pursuit of Designing Multi-modal Transformer for Video Grounding (2021.emnlp-main)
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| Challenge: | Existing methods for video grounding are not end-to-end, i.e., they rely on time-consuming post-processing steps to refine predictions. |
| Approach: | They propose an end-to-end multi-modal Transformer model that uses two encoders and a cross-modal decoder for grounding prediction. |
| Outcome: | The proposed model is 4.9% faster than existing models and is based on a set of encodings and decoders. |
Outdated Issue Aware Decoding for Factual Knowledge Editing (2024.findings-acl)
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| Challenge: | Existing knowledge editing methods retain outdated responses for reasoning questions . naively retraining LLMs can be computationally intensive and can lead to catastrophic forgetting . |
| Approach: | They propose a simple yet effective decoding strategy to enhance edited models on reasoning questions. |
| Outcome: | The proposed method outDates ISsue aware deCOding (DISCO) to improve models on reasoning questions. |
A structure-enhanced graph convolutional network for sentiment analysis (2020.findings-emnlp)
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| Challenge: | Recent work on sentiment analysis and aspect-based sentiment analysis does not exploit syntactic information from dependency parsing. |
| Approach: | They propose a weighted graph convolutional network which exploits syntactic information . they use BERT instead of Bi-LSTM to generate contextualized representations as inputs . |
| Outcome: | The proposed model can exploit rich syntactic information based on feature combination . it can improve on four ABSA tasks out of six and two SA tasks out . |
Generating Authentic Adversarial Examples beyond Meaning-preserving with Doubly Round-trip Translation (2022.naacl-main)
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| Challenge: | Existing approaches to generate adversarial examples for NMT use the meaning-preserving restriction. |
| Approach: | They propose a new definition for adversarial examples based on the Doubly Round-Trip Translation (DRTT) they introduce masked language models to construct bilingual adversarials based upon DRTT . |
| Outcome: | The proposed approach significantly improves the robustness of the NMT model on clean and noisy test sets. |
MR. Judge: Multimodal Reasoner as a Judge (2025.emnlp-main)
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| Challenge: | Effective reward modeling is especially valuable in reinforcement learning (RLHF) . |
| Approach: | They propose a paradigm for empowering general-purpose MLLMs judges with strong reasoning capabilities by using multiple-choice problem models instead of directly assigning scores. |
| Outcome: | The proposed model surpasses GPT-4o on VL-RewardBench and improves performance on MM-Vet by up to 7.7%. |
Language-Coupled Reinforcement Learning for Multilingual Retrieval-Augmented Generation (2026.findings-acl)
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Rui Qi, Fengran Mo, Yufeng Chen, Xue Zhang, Shuo Wang, Hongliang Li, Xu Jinan, Meng Jiang, Jian-Yun Nie, Kaiyu Huang
| Challenge: | Existing approaches to multilingual retrieval-augmented generation (MRAG) use a single-turn retrieval and subsequent optimization to acquire and integrate beneficial external knowledge from multilingual collections. |
| Approach: | They propose a multilingual search-augmented reinforcement learning framework that integrates a language-coupled Group Relative Policy Optimization into the policy and reward models. |
| Outcome: | The proposed framework achieves competitive performance and is appropriate for various practical scenarios such as constrained training data and retrieval over collections encompassing a large number of languages. |
Multilingual Knowledge Editing with Language-Agnostic Factual Neurons (2025.coling-main)
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| Challenge: | Existing methods to update factual knowledge overlook connections of same knowledge between different languages, resulting in knowledge conflicts and limited edit performance. |
| Approach: | They propose a method to edit multilingual knowledge simultaneously that avoids knowledge conflicts and improves edit performance. |
| Outcome: | The proposed method avoids knowledge conflicts and improves edit performance on bi-ZsRE and MzsRE benchmarks. |
DiffusEmp: A Diffusion Model-Based Framework with Multi-Grained Control for Empathetic Response Generation (2023.acl-long)
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| Challenge: | Existing methods to generate empathetic responses are monotonous and generic, resulting in shallow empathy and few connections to the context. |
| Approach: | They propose to use explicit control to guide the empathy expression and a framework DiffusEmp to unify the utilization of dialogue context and attribute-oriented control signals. |
| Outcome: | The proposed framework outperforms baselines on EmpatheticDialogue in terms of controllability, informativeness, diversity, and diversity without the loss of context-relatedness. |
LearnAct: Few-Shot Mobile GUI Agent with a Unified Demonstration Benchmark (2026.findings-acl)
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Guangyi Liu, Pengxiang Zhao, Liang Liu, Zhiming Chen, Yuxiang Chai, Yaozhen Liang, WenHao Wang, Siheng Chen, Zhengxi Lu, Shuai Ren, Hao Wang, Shibo He, Yong Liu, Wenchao Meng
| Challenge: | Mobile GUI agents show promise in automating tasks but face significant generalization challenges in long-tail scenarios. |
| Approach: | They propose a benchmark framework for mobile GUI agents that measures the performance of GUI agents by analyzing their performance. |
| Outcome: | The LearnGUI benchmark outperforms existing methods in offline and online evaluations and demonstrates consistent gains across model architectures. |
R.R.: Unveiling LLM Training Privacy through Recollection and Ranking (2025.findings-acl)
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| Challenge: | Existing privacy attacks focus on membership inference or data extraction, but reconstructing specific personally identifiable information (PII) in training data remains challenging. |
| Approach: | They propose a two-step privacy stealing attack that enables attackers to reconstruct PII entities from scrubbed training data where the PI I entities have been masked. |
| Outcome: | The proposed attack can reconstruct PII entities from scrubbed training data where the PI I entities have been masked. |
Unsupervised Knowledge Selection for Dialogue Generation (2021.findings-acl)
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| Challenge: | Existing knowledge selection tasks require the preidentified knowledge to generate informative dialogues. |
| Approach: | They propose a novel method to supervise knowledge selection when the gold knowledge label is unknown by obtaining an oracle knowledge label via distant supervision and leverage knowledge distillation to alleviate the noisy labeling problem of distant supervision. |
| Outcome: | The proposed method outperforms strong supervised baselines on two knowledge-grounded dialogue datasets and generates more informative responses. |
Perceiving the World: Question-guided Reinforcement Learning for Text-based Games (2022.acl-long)
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| Challenge: | Text-based games provide an interactive way to study natural language processing. |
| Approach: | They propose a two-phase training framework to decouple language learning from reinforcement learning and improve the sample efficiency. |
| Outcome: | The proposed method significantly improves performance and sample efficiency against compound error and limited pre-training data. |
Few-Shot Table Understanding: A Benchmark Dataset and Pre-Training Baseline (2022.coling-1)
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| Challenge: | Pre-trained language models have demonstrated their effectiveness for few-shot table understanding, but few-shoot table understanding is rarely explored due to the deficiency of public table pre-training corpus and well-defined downstream benchmark tasks. |
| Approach: | They establish a benchmark dataset and use it to explore few-shot table understanding in Chinese. |
| Outcome: | The proposed model improves the few-shot table understanding in Chinese. |
Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics (2022.naacl-main)
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| Challenge: | Recent work incorporates pre-trained word embeddings into Neural Topic Models (NTMs), generating highly coherent topics. |
| Approach: | They conduct thorough experiments to investigate whether embeddings directly with an appropriate word selection method can generate more coherent and diverse topics than NTMs. |
| Outcome: | The proposed model generates more coherent and diverse topics than traditional NTMs, achieving higher efficiency and simplicity. |
MTPChat: A Multimodal Time-Aware Persona Dataset for Conversational Agents (2025.findings-naacl)
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| Challenge: | Existing time-aware datasets that focus on persona-grounded conversations focus on temporal dynamics, which narrows their scope and diminishes their complexity. |
| Approach: | They propose a multimodal, time-aware persona dialogue dataset that integrates linguistic, visual, and temporal elements within dialogue and persona memory. |
| Outcome: | The proposed framework integrates linguistic, visual, and temporal elements within dialogue and persona memory to assess a model’s ability to understand implicit temporal cues and dynamic interactions. |
VaseVQA: Multimodal Agent and Benchmark for Ancient Greek Pottery (2026.findings-eacl)
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Jinchao Ge, Tengfei Cheng, Biao Wu, Zeyu Zhang, Shiya Huang, Judith Bishop, Gillian Shepherd, Meng Fang, Ling Chen, Yang Zhao
| Challenge: | MLLMs that use domain-specific data are limited in understanding cultural heritage artifacts such as ancient Greek pottery . supervised fine-tuning improves adaptation to domain knowledge, but it struggles with deeper reasoning tasks. |
| Approach: | They propose a visual question-answer tool that augments SFT with reinforcement learning using verifiable rewards. |
| Outcome: | The proposed model outperforms baseline models on reasoning-intensive questions on ancient Greek pottery. |
Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction (2021.acl-long)
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| Challenge: | Existing methods to extract event records from text decompose complex structure prediction task into multiple subtasks. |
| Approach: | They propose a sequence-to-structure generation paradigm that can extract events from text . they propose unified event extraction, constrained decoding algorithm and curriculum learning algorithm . |
| Outcome: | The proposed method can achieve competitive performance using record-level annotations in both supervised learning and transfer learning settings. |
MedDialog: Large-scale Medical Dialogue Datasets (2020.emnlp-main)
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Guangtao Zeng, Wenmian Yang, Zeqian Ju, Yue Yang, Sicheng Wang, Ruisi Zhang, Meng Zhou, Jiaqi Zeng, Xiangyu Dong, Ruoyu Zhang, Hongchao Fang, Penghui Zhu, Shu Chen, Pengtao Xie
| Challenge: | telemedicine is a medical practice that provides patient care remotely using video conferencing tools. |
| Approach: | They build large-scale medical dialogue datasets to facilitate research . they pretrain several models on the Chinese MedDialog dataset and compare their performance . |
| Outcome: | The proposed datasets show that models trained on MedDialog can generate doctor-like medical dialogues. |
Figure It Out: Improve the Frontier of Reasoning with Executable Visual States (2026.acl-long)
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| Challenge: | Recent reasoning models fail to capture structural constraints in complex settings. |
| Approach: | They propose a visual-based reasoning system that integrates executable visual construction into multi-turn reasoning via end-to-end reinforcement learning. |
| Outcome: | The proposed model outperforms strong text-only chain-of-thought models on seven mathematical benchmarks and improves by 13.12% on AIME 2025 and 11.00% on BeyondAIME. |
FinReporting: An Agentic Workflow for Localized Reporting of Cross-Jurisdiction Financial Disclosure (2026.acl-demo)
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Fan Zhang, Mingzi Song, Rania Elbadry, Yankai Chen, Shaobo Wang, Yixi Zhou, Xunwen Zheng, Yueru He, Yuyang Dai, Georgi Nenkov Georgiev, Ayesha Gull, Muhammad Usman Safder, Fan Wu, Liyuan Meng, Fengxian Ji, Junning Zhao, Xueqing Peng, Jimin Huang, YU Chen, Xue Liu, Preslav Nakov, Zhuohan Xie
| Challenge: | FinReporting is an agentic workflow for localized cross-jurisdiction financial reporting . existing approaches assume a single-market setting and overlook structural differences across jurisdictions . |
| Approach: | They propose a workflow that decomposes financial reporting into auditable stages . they use Large Language Models to extract and summarize corporate disclosures . |
| Outcome: | The proposed system decomposes reporting into auditable stages . it improves consistency and reliability under heterogeneous reporting regimes. |
Monotonic Paraphrasing Improves Generalization of Language Model Prompting (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable proficiency in zero-shot decision making and instruction following. |
| Approach: | They propose an end-to-end decoding strategy that paraphrases given prompts or instructions into their lower perplexity counterparts based on an ensemble of a paraphrase LM for prompt rewriting, and a target LM that constrains the generation for lower perxity. |
| Outcome: | The proposed method can efficiently paraphrase the original prompt without altering its semantic meaning while decreasing the perplexity of each generation as calculated by the target LM. |
Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning (2026.findings-acl)
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Fengran Mo, Yifan Gao, Sha Li, Hansi Zeng, Xin Liu, Zhaoxuan Tan, Xian Li, Jianshu Chen, Dakuo Wang, Meng Jiang
| Challenge: | Existing studies focus on single-turn scenarios, which might lack the ability to handle multi-turn interactions. |
| Approach: | They propose a conversational agent that interleaves search and reasoning across turns and provides tailored rewards towards evolving user goals. |
| Outcome: | The proposed agent interleaves search and reasoning across turns, enabling exploratory and adaptive behaviors learned through reinforcement learning (RL) training with tailored rewards towards evolving user goals. |
Collaborative Policy Learning for Open Knowledge Graph Reasoning (D19-1)
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| Challenge: | Existing models of knowledge graph reasoning suffer from limited performance when working on sparse and incomplete graphs due to the lack of evidential paths that can reach target entities. |
| Approach: | They propose a framework to train two collaborative agents to reason for missing facts over a graph augmented by a text corpus. |
| Outcome: | Experiments on two public datasets show the proposed approach is effective on a knowledge graph reasoning task. |
UniMoE-Audio: Unified Speech and Music Generation with Dynamic-Capacity Mixture-of-Experts (2026.acl-long)
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Zhenyu Liu, Yunxin li, Xuanyu Zhang, Qixun Teng, Shenyuan Jiang, Xinyu Chen, Haoyuan Shi, Haolan Chen, Fanbo Meng, Mingjun Zhao, Yu Xu, Yancheng He, Baotian Hu, Haizhou Li, Min Zhang
| Challenge: | Recent advances in unified multimodal models indicate a clear trend towards comprehensive content generation. |
| Approach: | They propose a unified speech and music generation model built upon a novel framework . they propose specialized MoE architectures and curated training strategies to tackle data imbalances . |
| Outcome: | The proposed model achieves state-of-the-art performance on major speech and music generation benchmarks. |
MASFactory: A Graph-centric Framework for Orchestrating LLM-Based Multi-Agent Systems with Vibe Graphing (2026.acl-demo)
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| Challenge: | Large language model-based multi-agent systems (MAS) are increasingly used to extend agentic problem solving via role specialization and collaboration. |
| Approach: | They propose a graph-centric framework for orchestrating large language model-based multi-agent systems . they compile a user's natural-language intent into an editable workflow specification and then into an executable graph . |
| Outcome: | The proposed framework compiles natural-language intent into an executable graph and then compile and executes it at runtime. |
Think Natively: Unlocking Multilingual Reasoning with Consistency-Enhanced Reinforcement Learning (2026.acl-long)
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Xue Zhang, Yunlong Liang, Fandong Meng, Songming Zhang, Kaiyu Huang, Yufeng Chen, Xu Jinan, Jie Zhou
| Challenge: | Current Large Reasoning Models exhibit two critical limitations when processing non-English languages: (1) They struggle to maintain input-output language consistency; (2) They generally perform poorly with wrong reasoning paths and lower answer accuracy compared to English. |
| Approach: | They propose a language-consistency reward and a cross-lingual thinking alignment reward to improve the model's interpretability and accuracy. |
| Outcome: | The proposed model achieves nearly 100% language consistency and superior performance on two multilingual benchmarks (MMATH and PolyMath). |
Fusion-Eval: Integrating Assistant Evaluators with LLMs (2024.emnlp-industry)
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| Challenge: | Recent studies have employed large language models (LLMs) as reference-free metrics for NLG evaluation, enhancing adaptability to new tasks tasks. |
| Approach: | They propose a method that leverages large language models to integrate insights from various assistant evaluators. |
| Outcome: | The proposed approach achieves a 0.962 system-level Kendall-Tau correlation with humans on SummEval and a 0.7444 turn-level Spearman correlation on TopicalChat, which is significantly higher than baseline methods. |
Benchmarking Web Agent Safety under E-commerce Deceptive Interfaces (2026.acl-long)
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| Challenge: | Existing web agents are highly susceptible to multiple classes of deceptive interfaces, but they are not designed to mitigate these failures. |
| Approach: | They propose a lightweight plugin framework that allows controlled injection of deceptive interface patterns into existing web environments. |
| Outcome: | The proposed framework enables controlled injection of deceptive interface patterns into web environments. |
Syntactically-Informed Unsupervised Paraphrasing with Non-Parallel Data (2021.emnlp-main)
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| Challenge: | Existing studies on syntactically controlled paraphrase generation rely on large-scale parallel data. |
| Approach: | They propose a syntactically-informed unsupervised paraphrasing model based on conditional variational auto-encoder which can generate texts in a specified syntastic structure. |
| Outcome: | The proposed model can generate diverse paraphrases with specified syntactic structure using non-parallel data. |
MuBench: Assessment of Multilingual Capabilities of Large Language Models Across 61 Languages (2026.findings-acl)
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| Challenge: | Existing evaluation datasets lack cross-lingual alignment, leaving assessments of multilingual capabilities fragmented in both language and skill coverage. |
| Approach: | They propose to use multilingual consistency as a complementary metric to assess performance bottlenecks and guide model improvement. |
| Outcome: | The proposed model lacks cross-lingual alignment and language coverage gaps between state-of-the-art models. |
ATLAS: Agent Tuning via Learning Critical Steps (2025.findings-acl)
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| Challenge: | Existing agent tuning approaches employ supervised finetuning on entire expert trajectories, but behavior-cloning of full traitories introduces expert bias and weakens generalization to states not covered by the expert data. |
| Approach: | They propose a method that finetunes LLMs on critical steps in expert trajectories and identifies and finetuns them on these steps with reduced costs. |
| Outcome: | The proposed method outperforms existing methods and open-source LLM agents on only 30% critical steps in extensive experiments. |
CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding (D19-1)
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| Challenge: | Existing models for slot filling and intent detection fail to fully utilize cooccurrence relations between slots and intents, which restricts their potential performance. |
| Approach: | They propose a novel Collaborative Memory Network (CM-Net) that captures slot-specific and intent-specific features in a collaborative manner. |
| Outcome: | The proposed network outperforms existing models on two benchmarks and a self-collected corpus. |
CITB: A Benchmark for Continual Instruction Tuning (2023.findings-emnlp)
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| Challenge: | Existing methods for instruction tuning do not leverage the rich natural language instructions. |
| Approach: | They propose to use a benchmark to study how instruction tuning works in CL tasks. |
| Outcome: | The proposed method can achieve similar or better results than existing CL methods. |
Hazards in Daily Life? Enabling Robots to Proactively Detect and Resolve Anomalies (2025.naacl-long)
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Zirui Song, Guangxian Ouyang, Meng Fang, Hongbin Na, Zijing Shi, Zhenhao Chen, Fu Yujie, Zeyu Zhang, Shiyu Jiang, Miao Fang, Ling Chen, Xiuying Chen
| Challenge: | Existing household robots are inability to recognize potential problems or dangers in home environments. |
| Approach: | They propose a task of creating anomaly scenarios using generative models instead of manually labeled data to build simulated environments. |
| Outcome: | The proposed framework outperforms existing models in terms of task description and scene diversity. |
On the Vulnerability of Text Sanitization (2025.naacl-long)
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| Challenge: | Existing reconstruction attacks on text sanitization are not able to accurately assess their effectiveness. |
| Approach: | They propose to use ASR to measure the effectiveness of reconstruction attacks to evaluate sanitization performance. |
| Outcome: | The proposed reconstruction attacks achieve a 46.4% improvement in ASR over the state-of-the-art baseline with a privacy budget of =4.0 on the SST-2 dataset. |
Who Can Withstand Chat-Audio Attacks? An Evaluation Benchmark for Large Audio-Language Models (2025.findings-acl)
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| Challenge: | Existing research focused on model-specific adversarial methods, but real-world applications demand a more generalizable approach to audio adversarials. |
| Approach: | They propose a Chat-Audio Attacks benchmark to evaluate LALMs' robustness . they propose standard evaluation, GPT-4o-based evaluation and human evaluation . |
| Outcome: | The proposed benchmark aims to explore the robustness of six state-of-the-art LALMs with voice interaction capabilities. |
Compressing LLM Knowledge into Graph Representations for Text-attributed Graphs Learning (2026.acl-long)
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| Challenge: | Existing GNN-LLM approaches use large language models at inference time for processing text attributes, resulting in costly deployment. |
| Approach: | They propose a framework that internalizes LLM knowledge within graph models and supports inference-efficient TAG learning. |
| Outcome: | The proposed framework internalizes LLM knowledge within graph models and supports inference-efficient TAG learning. |
Summary-Oriented Vision Modeling for Multimodal Abstractive Summarization (2023.acl-long)
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| Challenge: | Existing studies on multimodal abstractive summarization focus on how to use extracted visual features to produce a concise summary given the multimodal data. |
| Approach: | They propose to improve the visual quality of the multimodal abstractive summarization model by capturing summary-oriented visual features. |
| Outcome: | The proposed approach achieves state-of-the-art under 44 languages and is highly effective on high-resource English datasets. |
Less, but Better: Efficient Multilingual Expansion for LLMs via Layer-wise Mixture-of-Experts (2025.acl-long)
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| Challenge: | Existing large language models (LLMs) have remarkable ability in high-resource languages, but their performance in multilingual scenarios is still limited. |
| Approach: | They propose a layer-wise expert allocation algorithm to determine the appropriate number of new experts for each layer. |
| Outcome: | The proposed method outperforms the previous state-of-the-art baseline with 60% fewer experts in the single-expansion setting and 33.3% fewer in the lifelong-expanding setting. |
Scheduled Sampling Based on Decoding Steps for Neural Machine Translation (2021.emnlp-main)
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| Challenge: | Neural machine translation models are trained to maximize the likelihood of next token given previous golden tokens as inputs, but at the inference stage, golden token is unavailable. |
| Approach: | They propose to use scheduled sampling to replace ground-truth tokens with predicted tokens to bridge the gap between training and inference. |
| Outcome: | The proposed methods outperform the Transformer baseline and vanilla scheduled sampling on three large-scale WMT tasks. |
Self-imitation Learning for Action Generation in Text-based Games (2023.eacl-main)
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| Challenge: | Text-based games are situated systems where the game agents observe textual descriptions, and generate textual commands to interact with the environment. |
| Approach: | They propose a confidence-based self-imitation model to generate action candidates for the RL agent by exploiting past valuable trajectories to adapt a pre-trained language model towards a target game. |
| Outcome: | The proposed model performs well in multiple challenging games. |
A Survey for Efficient Open Domain Question Answering (2023.acl-long)
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| Challenge: | Open domain question answering (ODQA) is a longstanding task that can answer factoid questions without explicit evidence in natural language processing (NLP). |
| Approach: | They propose to use open domain question answering to answer factual questions from a large knowledge corpus without explicit evidence. |
| Outcome: | The proposed models can answer factoid questions from a large knowledge corpus without explicit evidence. |
Cross-Lingual Pitfalls: Automatic Probing Cross-Lingual Weakness of Multilingual Large Language Models (2025.acl-long)
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| Challenge: | Large language models have achieved remarkable success in Natural Language Processing, yet their cross-lingual consistency remains a significant challenge. |
| Approach: | They propose a method to identify cross-lingual weaknesses in Large Language Models . they construct bilingual question pairs that expose performance discrepancies between English and target languages . |
| Outcome: | The proposed method uncovers over 50% accuracy drops in target languages across models. |
CAP: Controllable Alignment Prompting for Unlearning in LLMs (2026.acl-long)
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Zhaokun Wang, Jinyu Guo, Jingwen Pu, Hongli Pu, Meng Yang, Xunlei Chen, Jie Ou, Wenyi Li, Guangchun Luo, Wenhong Tian
| Challenge: | Existing methods for modifying parameters are unsystematic and rely on empirical experience. |
| Approach: | They propose a controllable alignment prompting for unlearning framework that decouples unlearning into a learnable prompt optimization process via reinforcement learning. |
| Outcome: | The proposed framework achieves precise, controllable unlearning without updating model parameters. |
PCA-Bench: Evaluating Multimodal Large Language Models in Perception-Cognition-Action Chain (2024.findings-acl)
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Liang Chen, Yichi Zhang, Shuhuai Ren, Haozhe Zhao, Zefan Cai, Yuchi Wang, Peiyi Wang, Xiangdi Meng, Tianyu Liu, Baobao Chang
| Challenge: | a new multimodal decision-making benchmark evaluates the integrated capabilities of multimodal large language models. |
| Approach: | They propose a multimodal decision-making benchmark for evaluating MLLMs . they propose an automatic evaluation protocol to assess 10 prevalent ML models . |
| Outcome: | The proposed benchmark improves performance of multimodal large language models in three scenarios . the model is required to integrate multiple capabilities to make accurate decisions . |
On the Generation of Medical Dialogs for COVID-19 (2021.acl-short)
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Meng Zhou, Zechen Li, Bowen Tan, Guangtao Zeng, Wenmian Yang, Xuehai He, Zeqian Ju, Subrato Chakravorty, Shu Chen, Xingyi Yang, Yichen Zhang, Qingyang Wu, Zhou Yu, Kun Xu, Eric Xing, Pengtao Xie
| Challenge: | under the pandemic of COVID-19, people experiencing COVI D19-related symptoms have a pressing need to consult doctors. |
| Approach: | They develop a medical dialog system that can provide COVID19-related consultations . they use two dialog datasets containing conversations between doctors and patients . |
| Outcome: | The proposed system can provide COVID19-related consultations, but is too small compared with general-domain dialog datasets. |
Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization (2024.findings-emnlp)
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| Challenge: | Existing literature on leveraging persona in large language models is disorganized and lacks a systematic taxonomy . leveraging peopleas has resurfaced as an ideal lens for adapting LLMs for specific contexts . |
| Approach: | They propose to categorize current research on leveraging persona in large language models . they propose to use a comprehensive survey to categorize existing studies . |
| Outcome: | The proposed framework is a promising framework for tailoring large language models to specific contexts. |
Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning (2023.emnlp-main)
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| Challenge: | In-context learning (ICL) is a promising capability for large language models (LLMs) but its underlying mechanism remains unexplored. |
| Approach: | They propose a demonstration compression technique to expedite inference and an analysis framework for diagnosing ICL errors in GPT2-XL. |
| Outcome: | The proposed method improves ICL performance and expedites inference. |
HyperNetwork-based Decoupling to Improve Model Generalization for Few-Shot Relation Extraction (2023.emnlp-main)
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| Challenge: | Existing studies cannot generalize well to unseen relations using Prototypical Networks . current approaches are dependent on large amount of labeled data and cannot deal with unseense relations well. |
| Approach: | They propose a HyperNetwork-based Decoupling approach to improve FSRE generalization . they propose FSre models with an encoder, network generator and refined classifiers . |
| Outcome: | The proposed method improves the generalization of few-shot relation extraction models. |
Revisiting Parameter-Efficient Tuning: Are We Really There Yet? (2022.emnlp-main)
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| Challenge: | Pretrained language models (PLMs) are used as backbones to be combined with additional parameters and finetuned on downstream tasks in an end-to-end manner. |
| Approach: | They propose to use a fraction of parameters to tune pretrained language models (PLMs) this is the first comprehensive investigation into the training and evaluation of PETuning methods. |
| Outcome: | The proposed methods have been validated and tested with a rigorous evaluation protocol and have shown that they are unstable and inconsistent. |
BrowseComp-Plus: A Fair and Disentangled Evaluation Benchmark for Deep Search Agents (2026.acl-long)
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Zijian Chen, Xueguang Ma, Shengyao Zhuang, Ping Nie, Kai Zou, Sahel Sharifymoghaddam, Andrew Liu, Joshua Green, Kshama Patel, Ruoxi Meng, Mingyi Su, Yanxi Li, Haoran Hong, Xinyu Shi, Xuye Liu, Hosna Oyarhoseini, Nandan Thakur, Crystina Zhang, Luyu Gao, Wenhu Chen, Jimmy Lin
| Challenge: | Existing benchmarks for deep search agents rely on blackbox web search APIs . dynamic and opaque web APIs hinder reproducibility and fair comparisons - authors . |
| Approach: | They propose a benchmark that employs a fixed corpus for controlled retrieval for deep search agents. |
| Outcome: | The new benchmark shows that agents that combine large language models with retrieval tools excel at complex, reasoning-intensive queries. |
FARSS: Fisher-Optimized Adaptive Low-Rank and Singular-Vector Selection for Knowledge-Preserving Fine-Tuning (2026.findings-acl)
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Renxing Chen, Ziwei Xiang, Peisong Wang, Hongjian Fang, Meng Li, Fanhu Zeng, Yanan Zhu, Peipei Yang, Xu-Yao Zhang, Jian Cheng
| Challenge: | Low-rank adaptation methods for large language models have limitations in preserving world knowledge and limiting updates to preserve world knowledge. |
| Approach: | They propose a Fisher-optimized adaptive low Rank and Singular-VectorSelection framework for knowledge-preserving fine-tuning that allows efficient and task-sensitive updates. |
| Outcome: | The proposed framework outperforms existing methods for knowledge-preserving fine-tuning. |
Multimodal Incremental Transformer with Visual Grounding for Visual Dialogue Generation (2021.findings-acl)
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| Challenge: | Existing studies focus on implicit exploration of multimodal coreference but neglect the importance of locating the objects explicitly in the visual content, which is associated with textual entities. |
| Approach: | They propose a multimodal incremental transformer with visual grounding which aims to explicitly locate related objects in the image guided by textual entities. |
| Outcome: | The proposed model achieves comparable performance on the VisDial v0.9 and v1.0 datasets. |
Evaluating the Expressive Appropriateness of Speech in Rich Contexts (2026.acl-long)
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Tianrui Wang, Ziyang Ma, Yizhou Peng, Haoyu Wang, Zhikang Niu, Zikang Huang, Yihao Wu, Yi-Wen Chao, Yu Jiang, Yuheng Lu, Guanrou Yang, Xuanchen Li, Hexin Liu, Chunyu Qiang, Cheng Gong, Yifan Yang, Tianchi Liu, Junyu Wang, Nana Hou, Meng Ge, Fuming You, Yang Wei, Zhongqian Sun, Hu Haifeng, Xiaobao Wang, Eng Siong Chng, Xie Chen, Longbiao Wang, Jianwu Dang
| Challenge: | Existing methods for evaluating expressive speech focus on word accuracy, naturalness, signal quality, or emotional intensity at the utterance level. |
| Approach: | They propose a framework for Evaluating Expressive Appropriateness in speech that assesses whether a speech sample aligns with the underlying communicative intent implied by its discourse-level narrative context. |
| Outcome: | The proposed framework outperforms existing speech evaluation and analysis systems on a human-annotated test set. |
From Discrimination to Generation: Low-Resource Intent Detection with Language Model Instruction Tuning (2024.findings-acl)
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| Challenge: | Existing studies fine-tune discriminative models on specific defined intent classes, preventing them from being directly adopted to new intent domains. |
| Approach: | They propose to use a pre-trained generative intent model to detect new intents from different domains with no parameter updates. |
| Outcome: | The proposed model outperforms baselines that need further fine-tuning or domain-specific samples. |
Be Cautious When Merging Unfamiliar LLMs: A Phishing Model Capable of Stealing Privacy (2025.findings-acl)
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| Challenge: | Model merging is a widespread technology in large language models that integrates multiple task-specific LLMs into a unified one. |
| Approach: | They propose a model merging approach that trains a phishing model capable of stealing privacy using a privacy phish instruction dataset. |
| Outcome: | The proposed model cloaking method mimics a specialized capability to conceal attack intent, luring users into merging the phishing model. |
Seg2Act: Global Context-aware Action Generation for Document Logical Structuring (2024.emnlp-main)
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Zichao Li, Shaojie He, Meng Liao, Xuanang Chen, Yaojie Lu, Hongyu Lin, Yanxiong Lu, Xianpei Han, Le Sun
| Challenge: | Document logical structuring is crucial for document intelligence due to the complexity of text segment dependencies in the document. |
| Approach: | They propose an end-to-end, generation-based method for document logical structuring that generates the action sequence via a global context-aware generative model and updates its global context and current logical structure based on the generated actions. |
| Outcome: | Experiments on ChCatExt and HierDoc datasets show that Seg2Act performs better than previous methods in both supervised and transfer learning settings. |
Speech Discrete Tokens or Continuous Features? A Comparative Analysis for Spoken Language Understanding in SpeechLLMs (2025.emnlp-main)
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| Challenge: | Speech Large Language Models (SpeechLLMs) have emerged as dominant speech processing approaches. |
| Approach: | They compare self-supervised learning-based discrete and continuous features . they compare performance across six spoken language understanding-related tasks . |
| Outcome: | The proposed models outperform discrete tokens and continuous features in six spoken language understanding-related tasks. |
From Short Video to Clickable Search: RLVR-Enabled Listwise Query Suggestion with Retrieval-Augmented Context (2026.acl-industry)
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| Challenge: | Existing methods to generate short-video bottom-bar queries are largely retrieval-based. |
| Approach: | They propose to reformulate the task as one-shot list generation, producing multiple queries per video . they also build multi-query ground truth from exposure and CTR logs, and redesign offline evaluation . |
| Outcome: | The proposed system yields strong offline and online improvements . it is deployed on Kuaishou to serve hundreds of millions of users daily . |
MEFT: Memory-Efficient Fine-Tuning through Sparse Adapter (2024.acl-long)
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| Challenge: | Parameter-Efficient Fine-tuning (PEFT) methods are limited on knowledge-intensive tasks due to the limited number of trainable parameters. |
| Approach: | They propose a mechanism that fine-tunes Large Language Models with larger adapters . they store and update the parameters of larger adapter adapters on the CPU . |
| Outcome: | The proposed method achieves comparable results to those obtained with larger memory capacities over the limited bandwidth of PCI Express (PCIe). |
MathAgent: Adversarial Evolution of Constraint Graphs for Mathematical Reasoning Data Synthesis (2026.findings-acl)
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| Challenge: | Current approaches to synthesising high-quality mathematical reasoning data without human priors suffer from mode collapse and limited logical complexity. |
| Approach: | They propose a hierarchical synthesis framework that formulates data synthesis as an unsupervised optimization problem over a constraint graph followed by semantic instantiation rather than a direct text generation task. |
| Outcome: | The proposed framework outperforms widely-used datasets on eight mathematical benchmarks. |
An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment Analysis (2021.findings-emnlp)
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| Challenge: | Existing approaches to Aspect-based sentiment analysis do not exploit the interactive relations among subtasks and do not utilize document-level labeled domain/sentiment knowledge, which restricts their performance. |
| Approach: | They propose an iterative multi-knowledge transfer network for end-to-end ABSA that leverages the inter-task interaction between subtasks. |
| Outcome: | The proposed approach improves on three benchmark datasets. |
Unlocking the Effectiveness of LoRA-FP for Seamless Transfer Implantation of Fingerprints in Downstream Models (2025.findings-emnlp)
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| Challenge: | lightweight plug-and-play framework that encodes backdoor fingerprints into LoRA adapters . |
| Approach: | proposed framework encodes backdoor fingerprints into LoRA adapters via constrained fine-tuning . enables seamless fingerprint transplantation through parameter fusion, eliminating full-parameter updates while maintaining integrity. |
| Outcome: | The proposed framework achieves superior robustness against various scenarios while reducing computational overhead compared to traditional approaches. |
Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering (2024.findings-emnlp)
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| Challenge: | Existing language models have limited sensitivity to temporal information and inadequate temporal reasoning capabilities. |
| Approach: | They propose a framework that enhances temporal awareness and reasoning . they propose to use Temporal Information-Aware Embedding and Granular Contrastive Reinforcement Learning . |
| Outcome: | The proposed framework outperforms existing LLMs on time-sensitive question answering tasks. |
The GaoYao Benchmark: A Comprehensive Framework for Evaluating Multilingual and Multicultural Abilities of Large Language Models (2026.acl-long)
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Yilun Liu, Chunguang Zhao, Mengyao Piao, Lingqi Miao, Shimin Tao, Minggui HE, Chenxin Liu, Zhang Li, null Mahongxia, Jiaxin Guo, Chen Liu, Liqun Deng, Jiansheng Wei, Xiaojun Meng, Fanyi Du, Daimeng Wei, Yanghua Xiao
| Challenge: | Existing multilingual evaluation benchmarks neglect cultural nuances and lack language coverage in subjective tasks. |
| Approach: | They propose a framework that categorizes evaluation tasks into three cultural layers and nine cognitive sub-layers. |
| Outcome: | The proposed framework surpasses prior coverage by up to 111% on 20+ LLMs. |
MedINST: Meta Dataset of Biomedical Instructions (2024.findings-emnlp)
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| Challenge: | Medical data and tasks require extensive preprocessing and standardization for effective use in training LLMs. |
| Approach: | They propose to use MedINST as a meta-dataset to evaluate LLMs' generalization ability. |
| Outcome: | The meta-dataset of biomedical instruction measures the generalization ability of LLMs across multiple open-domain tasks. |
Scheduled Multi-task Learning for Neural Chat Translation (2022.acl-long)
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| Challenge: | Existing methods to improve chat translation focus on modeling bilingual dialogue characteristics . however, there is insufficient data and simple joint training methods . |
| Approach: | They propose a scheduled multi-task learning framework to incorporate large-scale chat translation data into training by adding a second pre-training stage between the original pre- training and fine-tuning stages. |
| Outcome: | The proposed framework improves translation tasks on four language directions. |
Constructing coherent spatial memory in LLM agents through graph rectification (2026.acl-long)
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| Challenge: | Existing approaches to map construction and map repair are limited by context constraints and inconsistencies in text processing environments. |
| Approach: | They propose a framework for LLM-driven construction and map repair . it detects, localizes, and corrects structural inconsistencies in incremental navigation graphs . |
| Outcome: | The proposed method significantly improves map correctness and robustness in entangled or chained inconsistencies. |
Clear Up Confusion: Iterative Differential Generation for Fine-grained Intent Detection with Contrastive Feedback (2025.coling-main)
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| Challenge: | Recent studies on fine-grained intent detection have focused on collecting large-scale and high-quality samples via crowdsourcing resulting in data scarcity. |
| Approach: | They propose an iterative differential generation framework with contrastive feedback to generate high-quality pseudo samples and accurately capture the crucial nuances in target class distribution. |
| Outcome: | The proposed framework generates high-quality pseudo samples and captures crucial nuances in target class distribution. |
Turn-Level Active Learning for Dialogue State Tracking (2023.emnlp-main)
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| Challenge: | Existing approaches to annotate dialogues require supervised training, which requires human workers to manually annotates dialogues. |
| Approach: | They propose a turn-level active learning framework to actively select dialogue turns to annotate . their approach can achieve comparable performance to traditional training approaches . |
| Outcome: | The proposed model achieves comparable performance to existing training approaches with significantly less annotated data. |
UniS-MMC: Multimodal Classification via Unimodality-supervised Multimodal Contrastive Learning (2023.findings-acl)
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| Challenge: | Existing multimodal fusion methods ignore inter-modality relationship, treat each modality equally, suffer sensor noise, and thus reduce multimodal learning performance. |
| Approach: | They propose a multimodal contrastive method to explore more reliable multimodal representations under the weak supervision of unimodal predicting. |
| Outcome: | The proposed method outperforms current state-of-the-art multimodal learning methods on image-text classification benchmarks UPMC-Food-101 and N24News. |
CompTab: A Comprehensive Benchmark for Real-World TableQA with Complex Reasoning and Irregular Tables (2026.acl-long)
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Zhen Yang, Wei Du, Jie Wang, Wenze Zhou, Xiangfeng Meng, Zhengyang Wang, Suping Sun, Ziwei Du, Haodong Zou, Jie Chen, Yongbin Liu, Shicheng Tan, Jiahao Ying, Shu Zhao
| Challenge: | Existing benchmarks focus on well-structured tables and fail to reflect irregular structures and complex reasoning commonly encountered in real-world scenarios. |
| Approach: | They propose a benchmark to evaluate TableQA under complex reasoning and irregular table conditions. |
| Outcome: | The proposed framework improves generalization and realism of large language models under complex and irregular table conditions. |
Mastering the Craft of Data Synthesis for CodeLLMs (2025.naacl-long)
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Meng Chen, Philip Arthur, Qianyu Feng, Cong Duy Vu Hoang, Yu-Heng Hong, Mahdi Kazemi Moghaddam, Omid Nezami, Duc Thien Nguyen, Gioacchino Tangari, Duy Vu, Thanh Vu, Mark Johnson, Krishnaram Kenthapadi, Don Dharmasiri, Long Duong, Yuan-Fang Li
| Challenge: | Large language models (LLMs) have shown impressive performance in code understanding and generation. |
| Approach: | They propose a systematic review of large language models and their taxonomy and propose specialized LLMs for code-related tasks. |
| Outcome: | The proposed models have shown to be highly effective in coding tasks. |
GoG: Relation-aware Graph-over-Graph Network for Visual Dialog (2021.findings-acl)
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| Challenge: | Experimental results show that our model outperforms the strong baseline in both generative and discriminative settings by a significant margin. |
| Approach: | They propose a relation-aware graph-over-graph network (GoG) for visual dialog . their model outperforms the strong baseline in both generative and discriminative settings . |
| Outcome: | The proposed model outperforms baseline models in both generative and discriminative settings by a significant margin. |
Bridging the Gap between Prior and Posterior Knowledge Selection for Knowledge-Grounded Dialogue Generation (2020.emnlp-main)
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| Challenge: | Existing knowledge-grounded dialogue models lack prior and posterior knowledge selection . prior selection module may not learn to select knowledge properly because of lack of posterior information . |
| Approach: | They propose a knowledge distillation-based training strategy to remove the exposure bias of knowledge selection. |
| Outcome: | The proposed model improves on two knowledge-grounded dialogue datasets. |
Modeling Uncertainty in Composed Image Retrieval via Probabilistic Embeddings (2025.acl-long)
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Haomiao Tang, Jinpeng Wang, Yuang Peng, GuangHao Meng, Ruisheng Luo, Bin Chen, Long Chen, Yaowei Wang, Shu-Tao Xia
| Challenge: | Composed Image Retrieval (CIR) combines text and reference images to search for images . metric learning methods that focus on point embeddings fail to capture uncertainty in input data . |
| Approach: | They propose a framework that captures uncertainty in images and queries by Gaussian distributions in latent space rather than fixed points. |
| Outcome: | Experiments show that the proposed framework quantifies quality and semantic uncertainties . it can handle polysemy and ambiguity in search intentions, authors say . |
GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling (P19-1)
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| Challenge: | Existing systems for sequence labeling are limited by shallow connections between consecutive hidden states and insufficient modeling of global information. |
| Approach: | They propose a global context enhanced deep transition architecture for sequence labeling . they deepen the state transition path at each position in a sentence and assign tokens with global representations . |
| Outcome: | The proposed architecture outperforms the best reported results on two standard sequence labeling tasks. |
What Factors Influence LLMs’ Judgments? A Case Study on Question Answering (2024.lrec-main)
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Lei Chen, Bobo Li, Li Zheng, Haining Wang, Zixiang Meng, Runfeng Shi, Hao Fei, Jun Zhou, Fei Li, Chong Teng, Donghong Ji
| Challenge: | Existing studies indicate that Large Language Models perform at a level comparable to humans with advantages of speed and cost-effectiveness in different fields. |
| Approach: | They propose to introduce four unexplored factors and a new dimension of question difficulty to provide a more comprehensive understanding of LLMs’ judgments across varying question intricacies. |
| Outcome: | The proposed dimensions of question difficulty and answer quantity provide valuable insights into optimizing LLMs’ performance as judges. |
Separate the Wheat from the Chaff: Winnowing Down Divergent Views in Retrieval Augmented Generation (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) lack robustness in knowledge-intensive tasks due to noisy or irrelevant retrieved data. |
| Approach: | They propose a multi-agent debate-based RAG framework that integrates external knowledge sources into large language models to improve their accuracy. |
| Outcome: | The proposed framework is unsupervised and leverages pretrained LLMs without fine-tuning, making it easily adaptable to various tasks. |
Target-to-Source Augmentation for Aspect Sentiment Triplet Extraction (2023.emnlp-main)
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| Challenge: | Aspect Sentiment Triplet Extraction (ASTE) is an important task in sentiment analysis, but data scarcity limits performance of existing methods. |
| Approach: | They propose a target-to-source augmentation approach to alleviate the issue of data scarcity in Aspect Sentiment Triplet Extraction (ASTE) they use fluency and alignment discriminators to provide feedback and use this feedback to optimize the generator. |
| Outcome: | The proposed approach significantly improves the performance of existing methods. |
RAP: Efficient Text-Video Retrieval with Sparse-and-Correlated Adapter (2024.findings-acl)
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Meng Cao, Haoran Tang, Jinfa Huang, Peng Jin, Can Zhang, Ruyang Liu, Long Chen, Xiaodan Liang, Li Yuan, Ge Li
| Challenge: | Text-Video Retrieval (TVR) aims to align relevant video content with natural language queries. |
| Approach: | They propose to conduct efficient text-video Retrieval with a salient-and-correlated AdaPter . they propose a low-rank modulation module to refine per-image features from frozen CLIP backbone . |
| Outcome: | Experiments on four TVR datasets show that the proposed method performs better than other methods. |
TSAM: A Two-Stream Attention Model for Causal Emotion Entailment (2022.coling-1)
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| Challenge: | Existing studies on EAC focus on Emotion Recognition in Conversations (ERC), i.e., recognizing emotion labels of utterances. |
| Approach: | They propose a two-stream attention model to capture correlations between utterances in a global view and classify multiple utterrances synchronously to capture emotion and speaker information in parallel. |
| Outcome: | The proposed model outperforms baselines and achieves new State-Of-The-Art (SOTA) performance. |
More than Minorities and Majorities: Understanding Multilateral Bias in Language Generation (2024.findings-acl)
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| Challenge: | Existing studies on bias dataset construction and mitigation focus on one demographic group . in real-world applications, there are more than two demographic groups at risk of the same bias. |
| Approach: | They propose to analyze and reduce biases across multiple demographic groups using a multi-demographic bias dataset. |
| Outcome: | The proposed method can mitigate biases among multiple demographic groups effectively, the authors show . |
Pathway to Relevance: How Cross-Encoders Implement a Semantic Variant of BM25 (2025.emnlp-main)
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| Challenge: | Interpretability in information retrieval (IR) models is coarse-grained and poorly understood . a cross-encoder model extracts traditional relevance signals, such as term frequency and inverse document frequency . |
| Approach: | They analyze how a common IR model extracts traditional relevance signals . this is similar to the probabilistic ranking function BM25 . |
| Outcome: | The proposed model extracts traditional relevance signals in early-to-middle layers, similar to BM25 . the model then combine these concepts in later layers, laying the groundwork for future interventions . |
Learning Structural Information for Syntax-Controlled Paraphrase Generation (2022.findings-naacl)
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| Challenge: | Syntax-controlled paraphrase generation aims to produce paraphrase conform to given syntactic patterns. |
| Approach: | They propose a model that captures parent-child and sibling relations and a syntax encoder to capture alignment relations. |
| Outcome: | The proposed model achieves state-of-the-art in terms of semantic and syntactic quality on two popular benchmark datasets. |