Papers by Yao Chen
RiOT: Efficient Prompt Refinement with Residual Optimization Tree (2025.acl-long)
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| Challenge: | Existing methods for automatic prompt optimization face two challenges: lack of diversity and semantic drift. |
| Approach: | They propose a framework for automatic prompt optimization that iteratively refines prompts through text gradients and selects the best prompt using perplexity. |
| Outcome: | The proposed framework outperforms existing prompt optimization methods and manual prompting on commonsense, mathematical, logical, temporal, and semantic reasoning benchmarks. |
Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories (2021.emnlp-main)
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| Challenge: | Existing supervised models struggle to make correct predictions on rare word senses due to limited training data. |
| Approach: | They propose a gloss alignment algorithm that can align definition sentences with the same meaning from different sense inventories to collect rich lexical knowledge. |
| Outcome: | The proposed method outperforms previous methods on both frequent and rare word senses. |
Enhancing Reinforcement Learning for Radiology Report Generation with Evidence-aware Rewards and Self-correcting Preference Learning (2026.acl-long)
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| Challenge: | Recent reinforcement learning approaches have advanced radiology report generation (RRG) however, there are two limitations: report-level rewards offer limited evidence-grounded guidance for clinical faithfulness . |
| Approach: | They propose a method that uses group-wise evidence-aware alignment rewards and self-correcting preference learning to build a reliable, disease-agnostic preference dataset without human supervision. |
| Outcome: | ESC-RL promotes clinically faithful, disease-aligned reward and supports continual self-improvement during training. |
Enhancing Speech-to-Speech Dialogue Modeling with End-to-End Retrieval-Augmented Generation (2025.findings-emnlp)
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| Challenge: | End-to-end speech-to speech (S2S) dialogue systems face key challenges in incorporating external knowledge into their models. |
| Approach: | They propose a framework that directly retrieves relevant textual knowledge from speech queries. |
| Outcome: | The proposed framework improves the performance of end-to-end speech-tospeech dialogue systems while achieving higher retrieval efficiency. |
HASH-RAG: Bridging Deep Hashing with Retriever for Efficient, Fine Retrieval and Augmented Generation (2025.findings-acl)
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| Challenge: | Experimental evaluations on NQ, TriviaQA, and HotpotQA datasets demonstrate that our approach achieves a 90% reduction in retrieval time compared to conventional methods while maintaining considerate recall performance. |
| Approach: | They propose a framework that integrates deep hashing techniques with systematic optimizations to address these limitations. |
| Outcome: | The proposed framework outperforms retrieval/non-retrieval baselines by 1.4-4.3% in EM scores on NQ, TriviaQA, and HotpotQA datasets. |
RSMeM: Knowledge-Enhanced Memory Evolution for Remote Sensing Agents with Systematic Evaluation (2026.acl-long)
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Bingxian Wu, Yu Zhang, Zonghao Guo, Tang Liu, Chen Qian, Yuxiang Lu, Xingbo Du, Yanghao Li, Yidan Zhang, Chi Chen, Ling Yao, Chenghu Zhou, Maosong Sun
| Challenge: | Existing RS agents built on general-purpose LLMs are domain-agnostic, resulting in brittle and error-prone workflows. |
| Approach: | They propose a knowledge-enhanced memory evolution mechanism that bootstraps RS agents with pre-distilled domain knowledge and iteratively integrates online experience for robust multi-step tool execution. |
| Outcome: | Experiments show that the new model improves tool-use performance and accuracy . iteratively, iteration of the model integrates online experience for robust multi-step tool execution . |
The Elephant in the Room: Exploring the Role of Neutral Words in Language Model Group-Agnostic Debiasing (2025.findings-acl)
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Xinwei Guo, Jiashi Gao, Junlei Zhou, Jiaxin Zhang, Guanhua Chen, Xiangyu Zhao, Quanying Liu, Haiyan Wu, Xin Yao, Xuetao Wei
| Challenge: | Large Language Models (LLMs) are increasingly integrated into our daily lives, raising ethical concerns, especially about perpetuating stereotypes. |
| Approach: | They propose a method that incorporates a neutral word semantics-based loss function to alleviate the deterioration of the LMS during debiasing. |
| Outcome: | The proposed method alleviates the deterioration of the Language Modeling Score (LMS) by incorporating a neutral word semantics-based loss function. |
Compressing Large-Scale Transformer-Based Models: A Case Study on BERT (2021.tacl-1)
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Prakhar Ganesh, Yao Chen, Xin Lou, Mohammad Ali Khan, Yin Yang, Hassan Sajjad, Preslav Nakov, Deming Chen, Marianne Winslett
| Challenge: | Popular pre-trained Transformers have improved performance for various NLP tasks by sizable margins, but are too resource-hungry and computation-intensive to suit low-capacity devices or applications with strict latency requirements. |
| Approach: | They present a literature review of the compression of Transformers, focusing on the popular BERT model, which has attracted considerable research attention. |
| Outcome: | The proposed models improve Sentiment analysis, paraphrase detection, machine reading comprehension, question answering, text summarization, and other tasks by sizable margins. |
EmoMM: Benchmarking and Steering MLLM for Multimodal Emotion Recognition under Conflict and Missingness (2026.findings-acl)
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| Challenge: | Multimodal Large Language Models (MLLMs) have shown promise in MER, but their internal decision-making mechanisms under modality conflict and missingness remain underexplored. |
| Approach: | They propose a multimodal large language model that can detect and control modality conflicts and missing subsets by a lightweight mechanism that detects and controls modality conflict. |
| Outcome: | The proposed framework improves performance across settings, showing it can handle conflict and missing behaviors. |
Rethinking Diverse Human Preference Learning through Principal Component Analysis (2025.findings-acl)
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| Challenge: | Decomposed Reward Models extract diverse human preferences from binary comparisons without fine-grained annotations. |
| Approach: | They propose a decomposed reward model that extracts diverse human preferences from binary comparisons without fine-grained annotations. |
| Outcome: | The proposed approach extracts diverse human preferences from binary comparisons without fine-grained annotations. |
ChessArena: A Chess Testbed for Evaluating Strategic Reasoning Capabilities of Large Language Models (2026.acl-long)
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| Challenge: | Existing evaluation frameworks focus on isolated question-answering tasks that may not capture the essential aspects of strategic reasoning. |
| Approach: | They evaluate 13 large language models across over 800 games in chess . they use a chessian-based framework to test strategic reasoning and pattern recognition . |
| Outcome: | The proposed framework improves performance and basic understanding of large language models. |
OpenWebAgent: An Open Toolkit to Enable Web Agents on Large Language Models (2024.acl-demos)
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Iat Long Iong, Xiao Liu, Yuxuan Chen, Hanyu Lai, Shuntian Yao, Pengbo Shen, Hao Yu, Yuxiao Dong, Jie Tang
| Challenge: | OpenWebAgent integrates large language models and large multimodal models to improve web automation. |
| Approach: | They propose to integrate large language models and large multimodal models into an open toolkit to optimize web automation. |
| Outcome: | The open toolkit integrates both large language models (LLMs) and large multimodal models (LMMs) it enables the development of powerful, task-oriented web agents, significantly enhancing user experience and operational efficiency on the web. |
Generate-on-Graph: Treat LLM as both Agent and KG for Incomplete Knowledge Graph Question Answering (2024.emnlp-main)
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Yao Xu, Shizhu He, Jiabei Chen, Zihao Wang, Yangqiu Song, Hanghang Tong, Guang Liu, Jun Zhao, Kang Liu
| Challenge: | Existing methods to integrate LLMs with Knowledge Graphs (KGs) however, these methods are often incomplete to cover all the knowledge required to answer questions. |
| Approach: | They propose to integrate LLMs with Knowledge Graphs (KGs) to address insufficient knowledge and hallucination issues in Large Language Models. |
| Outcome: | The proposed method outperforms existing methods on two datasets. |
From Synthesis to Clinical Assistance: A Strategy-Aware Agent Framework for Autism Intervention based on Real Clinical Dataset (2026.acl-long)
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| Challenge: | Applied Behavior Analysis (ABA) is the gold standard for clinical intervention, but large language models struggle to adhere to its standardized procedures. |
| Approach: | They propose a strategy-aware framework to unify high-fidelity intervention dialogue synthesis and clinical decision support. |
| Outcome: | Experiments show that ASDAgent achieves nearly 80% strategic consistency with human experts. |
ActionStudio: A Lightweight Framework for Data and Training of Large Action Models (2025.emnlp-main)
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Jianguo Zhang, Thai Quoc Hoang, Ming Zhu, Zuxin Liu, Shiyu Wang, Tulika Manoj Awalgaonkar, Akshara Prabhakar, Haolin Chen, Weiran Yao, Zhiwei Liu, Juntao Tan, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong
| Challenge: | Existing infrastructure for efficient agentic data processing and model training remains underdeveloped. |
| Approach: | They propose a lightweight and extensible data and training framework for large action models . they propose to unify diverse agent trajectories using Unified Format 2.0 . |
| Outcome: | The proposed framework shows 9 higher throughput than existing frameworks and performs well across public and realistic agent benchmarks. |
Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models (2024.findings-acl)
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| Challenge: | Existing studies on the confidence calibration of LLMs have not explored the effects of different prompting strategies on LLM performance. |
| Approach: | They propose Fact-and-Reflection prompting which improves LLM confidence calibration . they propose to use human cognition to elicit known "facts" and ask model to "reflect" over them . |
| Outcome: | The proposed method lowers the expected calibration error by 23.5% on multi-purpose QA tasks. |
SelfRACG: Enabling LLMs to Self-Express and Retrieve for Code Generation (2025.emnlp-main)
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Qian Dong, Jia Chen, Qingyao Ai, Hongning Wang, Haitao Li, null Yiwu, Yao Hu, Yiqun Liu, Shaoping Ma
| Challenge: | Existing retrieval-augmented code generation methods fail to accurately fetch the knowledge required for code generation for consecutive code fragments. |
| Approach: | They propose a paradigm that enables large language models to Self-express their information needs to enhance retrieval-augmented code generation methods. |
| Outcome: | Experiments show that SelfRACG can retrieve external knowledge that better aligns with the LLM’s own information needs, resulting in superior generation performance compared to vanilla RACG. |
Towards the Law of Capacity Gap in Distilling Language Models (2025.acl-long)
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| Challenge: | Language model (LM) distillation aims at distilling knowledge in a large teacher LM to a small student one. |
| Approach: | They propose to use the law of capacity gap to distill knowledge from a large teacher to a small student model. |
| Outcome: | The proposed model outperforms other language models on a larger scale by using the law of capacity gap inducted from a preliminary study on small-scale (3B) LMs. |
Good Visual Guidance Make A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction (2022.findings-naacl)
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Xiang Chen, Ningyu Zhang, Lei Li, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Luo Si, Huajun Chen
| Challenge: | Existing approaches for named entity recognition and relation extraction suffer from error sensitivity when irrelevant object images are incorporated in texts. |
| Approach: | They propose a hierarchical visual prefix fusion NeTwork for visual-enhanced entity and relation extraction using pluggable visual prefixed visual features. |
| Outcome: | The proposed method achieves state-of-the-art on three benchmark datasets. |
EventRAG: Enhancing LLM Generation with Event Knowledge Graphs (2025.acl-long)
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Zairun Yang, Yilin Wang, Zhengyan Shi, Yuan Yao, Lei Liang, Keyan Ding, Emine Yilmaz, Huajun Chen, Qiang Zhang
| Challenge: | Existing approaches to text generation often neglect event structures that shape real-world narratives. |
| Approach: | They propose a framework that integrates structured event semantics with iterative retrieval and inference to enhance text generation. |
| Outcome: | Experiments on UltraDomain and MultiHopRAG show that the proposed framework outperforms baseline RAG systems in generation effectiveness, logical consistency, and multi-hop reasoning accuracy. |
SPARD: Self-Paced Curriculum for RL Alignment via Integrating Reward Dynamics and Data Utility (2026.acl-long)
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Xuyang Zhi, Peilun Zhou, Chengqiang Lu, Hang Lv, Yiwei Liang, Rongyang Zhang, Yan Gao, null Yiwu, Yao Hu, Hongchao Gu, Defu Lian, Hao Wang, Enhong Chen
| Challenge: | Large Language Models (LLMs) are shifting the focus from single verifiable tasks toward complex, open-ended real-world scenarios. |
| Approach: | They propose a framework that automatically adjusts reward weights and data importance to synchronize learning intent with data utility for optimal performance. |
| Outcome: | The proposed framework improves model capabilities across all domains and scales. |
How do Words Contribute to Sentence Semantics? Revisiting Sentence Embeddings with a Perturbation Method (2023.eacl-main)
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| Challenge: | Existing studies on sentence representation learning focus on human annotation, but they neglect the critical property that essential contents should contribute to sentence semantics more than non-essential contents when encoding a sentence. |
| Approach: | They propose a perturbation method for unsupervised semantic analysis that uses a sentence compression metric to adapt sentence compression datasets for automatic evaluation. |
| Outcome: | The proposed method can capture the main semantics of sentences better than several SOTA unsupervised sentence embedding models. |
C-MORE: Pretraining to Answer Open-Domain Questions by Consulting Millions of References (2022.acl-short)
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| Challenge: | Existing approaches to pretrain open-domain question answering systems lack task-specific annotations. |
| Approach: | They propose to pretrain a two-stage open-domain question answering system with strong transfer capabilities by using a dictionary and a large-scale corpus. |
| Outcome: | The proposed approach leads to 2%-10% gains in top-20 accuracy and improves with reader. |
Can Persona-Prompted LLMs Emulate Subgroup Values? An Empirical Analysis of Generalisability and Fairness in Cultural Alignment (2026.acl-long)
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Bryan Chen Zhengyu Tan, Zhengyuan Liu, Xiaoyuan Yi, Jing Yao, Xing Xie, Nancy F. Chen, Roy Ka-Wei Lee
| Challenge: | Current alignment paradigms treat "human values" as a monolithic entity, ignoring the fact that many societies are a mosaic of diverse subgroups with distinct and sometimes conflicting values, preferences, and norms. |
| Approach: | They examine whether Large Language Models can emulate distinct cultural values of subgroups . they use a global value survey to examine the value landscape of a multicultural society . |
| Outcome: | The proposed model improves on unseen, out-of-distribution subgroups by 17.4% . the model widens the disparity between subgroup groups when measured by distance-aware metrics. |
CKnowEdit: A New Chinese Knowledge Editing Dataset for Linguistics, Facts, and Logic Error Correction in LLMs (2025.acl-long)
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| Challenge: | CKnowEdit is the first-ever knowledge editing dataset designed to correct linguistic, factual, and logical errors in Large Language Models. |
| Approach: | They propose a Chinese knowledge editing dataset to correct linguistic, factual, and logical errors in Large Language Models. |
| Outcome: | The proposed dataset highlights the challenges that LLMs face in mastering Chinese . CKnowEdit can correct linguistic, factual, and logical errors in Chinese, the authors show . |
StructMem: Structured Memory for Long-Horizon Behavior in LLMs (2026.acl-short)
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| Challenge: | Existing memory systems lack structure and efficiency in capturing relationships between events. |
| Approach: | They propose a structure-enriched hierarchical memory framework that preserves event-level bindings and induces cross-event connections. |
| Outcome: | The proposed framework preserves event-level bindings and induces cross-event connections while reducing token usage, API calls, and runtime compared to prior memory systems. |
Exploring Model Kinship for Merging Large Language Models (2025.findings-emnlp)
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| Challenge: | Model merging has become one of the key technologies for enhancing the capabilities and efficiency of Large Language Models. |
| Approach: | They propose a model merging strategy that incorporates model kinship to improve model performance. |
| Outcome: | The proposed model merging strategy can yield better performance on benchmark datasets. |
LLMs Trust Humans More, That’s a Problem! Unveiling and Mitigating the Authority Bias in Retrieval-Augmented Generation (2025.acl-long)
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Yuxuan Li, Xinwei Guo, Jiashi Gao, Guanhua Chen, Xiangyu Zhao, Jiaxin Zhang, Quanying Liu, Haiyan Wu, Xin Yao, Xuetao Wei
| Challenge: | Large language models (LLMs) generate outputs that stray from user input or contravene established knowledge. |
| Approach: | They propose a new phenomenon, Authority Bias, where LLMs favor one knowledge source over the other . they propose atomic information that generates conflicts and a Conflict Detection Enhanced Query framework . |
| Outcome: | The proposed framework reduces Authority bias in large language models . it detects conflicts, performs credibility assessment on conflicting paragraphs, and detects perturbed text . |
QBridge: Bridging Natural Language and SQL via Gold Query Rewriting with Agentic Refinement (2026.acl-long)
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| Challenge: | Natural language to SQL (NL2SQL) is an intuitive interface for querying structured data . but real user questions are noisy, ambiguous, and weakly grounded to database semantics. |
| Approach: | They propose an agentic feedback-driven NL2SQL framework that bridges natural language and SQL via Gold Query. |
| Outcome: | The proposed framework outperforms strong prompting and agentic baselines on spider, BIRD, and three robustness variants on NL2SQL. |
CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents (2025.findings-acl)
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Tianqi Xu, Linyao Chen, Dai-Jie Wu, Yanjun Chen, Zecheng Zhang, Xiang Yao, Zhiqiang Xie, Yongchao Chen, Shilong Liu, Bochen Qian, Anjie Yang, Zhaoxuan Jin, Jianbo Deng, Philip Torr, Bernard Ghanem, Guohao Li
| Challenge: | Existing benchmarks for MLM agents in interactive environments are limited by their focus on a single environment, lack of detailed and generalized evaluation methods, and the complexity of constructing tasks and evaluators. |
| Approach: | They propose a cross-environment agent benchmark framework that integrates graph-based evaluation and task generation methods. |
| Outcome: | The proposed framework supports multiple devices and can be easily extended to any environment with a Python interface. |
Z-LaVI: Zero-Shot Language Solver Fueled by Visual Imagination (2022.emnlp-main)
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| Challenge: | Large-scale pretrained language models suffer from reporting bias, describing the lack of explicit commonsense knowledge in written text. |
| Approach: | They propose to endow language models with visual imagination capabilities by recalling existing images and synthesizing nonexistent images via text-to-image generation. |
| Outcome: | The proposed model improves the performance of existing language models across a diverse set of language tasks. |
SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis (2025.findings-naacl)
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Hengxing Cai, Xiaochen Cai, Junhan Chang, Sihang Li, Lin Yao, Wang Changxin, Zhifeng Gao, Hongshuai Wang, Li Yongge, Mujie Lin, Shuwen Yang, Jiankun Wang, Mingjun Xu, Jin Huang, Xi Fang, Jiaxi Zhuang, Yuqi Yin, Yaqi Li, Changhong Chen, Zheng Cheng, Zifeng Zhao, Linfeng Zhang, Guolin Ke
| Challenge: | Existing benchmarks fail to adequately evaluate the proficiency of Large Language Models (LLMs) Existing standards do not cover the skills needed to evaluate LLMs in scientific literature analysis. |
| Approach: | They propose a benchmark to evaluate the proficiency of large language models in scientific literature analysis. |
| Outcome: | SciAssess evaluates 11 LLMs on multiple tasks across scientific fields. |
i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data (2024.findings-naacl)
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Ziyi Yang, Mahmoud Khademi, Yichong Xu, Reid Pryzant, Yuwei Fang, Chenguang Zhu, Dongdong Chen, Yao Qian, Xuemei Gao, Yi-Ling Chen, Robert Gmyr, Naoyuki Kanda, Noel Codella, Bin Xiao, Yu Shi, Lu Yuan, Takuya Yoshioka, Michael Zeng, Xuedong Huang
| Challenge: | i-Code V2 is one of the first models capable of generating natural language from any combination of Vision, Language, and Speech data. |
| Approach: | They propose to create a model that can generate natural language from any combination of Vision, Language, and Speech data. |
| Outcome: | i-Code V2 matches or outperforms state-of-the-art single- and dual-modality baselines on 7 multimodal tasks. |
Towards a Mechanistic Understanding of Large Reasoning Models: A Survey of Training, Inference, and Failures (2026.acl-long)
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Yi Hu, Jiaqi Gu, Ruxin Wang, Zijun Yao, Hao Peng, Xiaobao Wu, Jianhui Chen, Muhan Zhang, Liangming Pan
| Challenge: | Recent research has shown that reinforcement learning can elicit intriguing emergent reasoning behaviors. |
| Approach: | They propose a comprehensive survey of the mechanistic understanding of large reasoning models . they organize findings into three core dimensions: 1) training dynamics, 2) reasoning mechanisms, and 3) unintended behaviors. |
| Outcome: | This paper synthesizes the mechanistic understanding of large reasoning models into three dimensions . authors outline a roadmap for future studies including improved interpretability and methodologies . |
Hierarchical Attention Generates Better Proofs (2025.acl-long)
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| Challenge: | Large language models (LLMs) have shown promise in formal theorem proving, but their token-level processing often fails to capture the inherent hierarchical nature of mathematical proofs. |
| Approach: | They propose a regularization method that aligns LLMs’ attention mechanisms with mathematical reasoning structures and establishes a five-level hierarchy from foundational elements to high-level concepts. |
| Outcome: | The proposed method improves proof success rates by 2.05% on miniF2F and 1.69% on ProofNet while reducing proof complexity by 23.81% and 16.50% respectively. |
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. |
MOBA-E2C: Generating MOBA Game Commentaries via Capturing Highlight Events from the Meta-Data (2022.findings-emnlp)
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| Challenge: | e-sports game competitions lack commentators because of the shortage of professional human commentators. |
| Approach: | They propose a data-driven MOBA commentary generation framework for MOBA games . they use a rule-based generator and a generative GPT generator to generate commentaries . |
| Outcome: | The proposed model generates commentaries based on the game meta-data and a rule-based generator and generative GPT generator. |
StorySparkQA: Expert-Annotated QA Pairs with Real-World Knowledge for Children’s Story-Based Learning (2024.emnlp-main)
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Jiaju Chen, Yuxuan Lu, Shao Zhang, Bingsheng Yao, Yuanzhe Dong, Ying Xu, Yunyao Li, Qianwen Wang, Dakuo Wang, Yuling Sun
| Challenge: | Existing story reading systems fail to capture the nuances of how education experts think when conducting interactive story reading activities. |
| Approach: | They propose to use existing question-answering (QA) datasets to capture experts' annotations and thinking process to construct a story-based annotation framework. |
| Outcome: | The proposed framework captures experts’ annotations and thinking process and can be used to generate 5, 868 expert-annotated QA pairs with real-world knowledge. |
Listen, Watch, and Learn to Feel: Retrieval-Augmented Emotion Reasoning for Compound Emotion Generation (2025.findings-acl)
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| Challenge: | Existing methods to assess human emotion are limited by the subjective nature of emotion perception, limiting the robustness of existing models. |
| Approach: | They propose a plug-and-play module that enhances MLLMs’ ability to tackle compound and context-rich emotion tasks. |
| Outcome: | The proposed framework improves MLLMs' ability to tackle compound and context-rich emotion tasks and the Compound Emotion QA dataset shows it performs well across both benchmarks and evaluation frameworks. |
AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning (2025.emnlp-demos)
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Zhong Zhang, Yaxi Lu, Yikun Fu, Yupeng Huo, Shenzhi Yang, Yesai Wu, Han Si, Xin Cong, Haotian Chen, Yankai Lin, Xie Xie, Wei Zhou, Wang Xu, Zhou Su, Zhongwu Zhai, Xiaoming Liu, null Meiyudong, Jianming Xu, Hongyan Tian, Chongyi Wang, Chi Chen, Yuan Yao, Zhiyuan Liu, Maosong Sun
| Challenge: | Large language model agents have enabled GUI-based automation, but their deployment is limited by noisy data, poor generalization, and lack of support for non-English GUIs. |
| Approach: | They propose an 8B-parameter GUI agent built for robust and efficient on-device GUI interaction. |
| Outcome: | The proposed GUI agent achieves promising performance on five public benchmarks and proposed Chinese benchmark CAGUI. |
Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution Reconstruction (2026.findings-acl)
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Xu Shen, Qi Zhang, Song Wang, Zhen Tan, Xinyu Zhao, Laura Yao, Vaishnav Tadiparthi, Hossein Nourkhiz Mahjoub, Ehsan Moradi Pari, Kwonjoon Lee, Tianlong Chen
| Challenge: | Large Language Model based multi-agent systems (MAS) excel at collaborative problem solving but remain brittle to cascading errors. |
| Approach: | They propose a metacognitive framework that enables step-level error detection and self-correction in Large Language Model based multi-agent systems (MAS) . |
| Outcome: | The proposed framework outperforms baselines on the Who When benchmark and delivers consistent gains on AgentErrorBench. |
UMSE: Unified Multi-scenario Summarization Evaluation (2023.findings-acl)
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| Challenge: | Summarization quality evaluation is a non-trivial task in text summarization. |
| Approach: | They propose a unified multi-scenario summarization evaluation model that shares cross-sceenario knowledge and uses a self-supervised training paradigm to optimize the model without extra human labeling. |
| Outcome: | The proposed model can achieve comparable performance with existing methods for three evaluation scenarios. |
T2DR: A Two-Tier Deficiency-Resistant Framework for Incomplete Multimodal Learning (2025.findings-acl)
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| Challenge: | Existing incomplete multimodal learning frameworks are inadequate for integrating multimodal data. |
| Approach: | They propose a framework for incomplete multimodal learning that is deficiency-resistant and provides two modules to address fine-grained deficiencies. |
| Outcome: | The proposed framework outperforms the SOTA models on two well-known multimodal benchmarks. |
Listening Like Humans: Semantics-Guided Noise-Robust Multimodal Speech Recognition (2026.acl-long)
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| Challenge: | Severe acoustic degradation results in unreliable ASR outputs . et al., 2024b): critical concerns regarding reliability and fairness of ASR . |
| Approach: | They propose a multimodal framework that reframes ASR as semantics-guided speech reconstruction. |
| Outcome: | The proposed framework achieves an average reduction in WER while also attaining 98.71% BERTScore and 96.7% USE over advanced baselines. |
A Learning Rate Path Switching Training Paradigm for Version Updates of Large Language Models (2024.emnlp-main)
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Zhihao Wang, Shiyu Liu, Jianheng Huang, Wang Zheng, YiXuan Liao, Xiaoxin Chen, Junfeng Yao, Jinsong Su
| Challenge: | Version updates are an indispensable requirement for Large Language Models . a large learning rate in the first stage and a complete learning decay process are crucial for version updates of LLMs. |
| Approach: | They propose a learning rate path switching training paradigm for version updates of Large Language Models. |
| Outcome: | The proposed paradigm reduces training cost to 58% when training four versions of LLMs compared to PTFS and CPT . |
Domain Transfer based Data Augmentation for Neural Query Translation (2020.coling-main)
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| Challenge: | Query translation (QT) is a critical factor in successful cross-lingual information retrieval (CLIR). |
| Approach: | They propose to extend query translation (QT) with a domain transfer procedure to revise synthetic candidates to search-aware examples. |
| Outcome: | The proposed method outperforms baselines and domain transfer methods on translation quality and retrieval accuracy. |
Knowledge Rumination for Pre-trained Language Models (2023.emnlp-main)
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| Challenge: | Existing studies have shown that pre-trained language models lack the capacity to handle knowledge-intensive tasks alone. |
| Approach: | They propose a new paradigm to help pre-trained language models utilize latent knowledge without retrieving it from external corpus. |
| Outcome: | The proposed paradigm can be applied to pre-trained language models without retrieving external knowledge from the corpus. |
DIDS: Domain Impact-aware Data Sampling for Large Language Model Training (2025.emnlp-main)
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Weijie Shi, Jipeng Zhang, Yaguang Wu, Jingzhi Fang, Shibo Zhang, Yao Zhao, Hao Chen, Ruiyuan Zhang, Yue Cui, Jia Zhu, Sirui Han, Jiajie Xu, Xiaofang Zhou
| Challenge: | Existing approaches for optimizing domain-level sampling strategies struggle with maintaining intra-domain consistency and accurately measuring domain impact. |
| Approach: | They propose to use a Fisher-Information Matrix-guided metric to measure domain impact to ensure intra-domain consistency and accuracy. |
| Outcome: | The proposed model achieves 3.4% higher average performance while maintaining comparable training efficiency. |
Cognitive-Level Adaptive Generation via Capability-Aware Retrieval and Style Adaptation (2025.findings-emnlp)
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| Challenge: | Large Language Models struggle to adapt content to users with differing cognitive capacities, leading to cognitive misalignment. |
| Approach: | They propose a cognitive-level alignment framework that aligns both knowledge complexity and presentation style with user cognition. |
| Outcome: | The proposed framework aligns knowledge complexity and presentation style with user cognition. |
From Scenes to Elements: Multi-Granularity Evidence Retrieval for Verifiable Multimodal RAG (2026.findings-acl)
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| Challenge: | Existing multimodal Retrieval-Augmented Generation (RAG) systems retrieve evidence at coarse granularities, making failures unverifiable. |
| Approach: | They propose a multimodal benchmark that features real-world landmarks with annotations across multiple viewpoints and a framework that treats visual elements as first-class retrieval units through three stages: element-level detection and classification, multi-granularity cross-modal alignment for evidence retrieval, and attribution-constrained generation. |
| Outcome: | The proposed framework achieves up to 29.2% improvement over six strong baselines for this task. |
code-transformed: The Influence of Large Language Models on Code (2026.findings-eacl)
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| Challenge: | Using Large Language Models, code generation capabilities have transformed programming practices. |
| Approach: | They analyze 20,000 GitHub repositories linked to arXiv papers published between 2020 and 2025 . they identify measurable trends in the evolution of coding style that align with LLM-generated code . |
| Outcome: | The proposed study examines 20,000 GitHub repositories linked to arXiv papers . it finds that LLMs influence code style, and that they can be observed in real-world code . |
Ameli: Enhancing Multimodal Entity Linking with Fine-Grained Attributes (2024.eacl-long)
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| Challenge: | Experimental results show that understanding attributes of mentions from text descriptions and visual images plays a vital role in multimodal entity linking. |
| Approach: | They propose to integrate attributes into multimodal entity linking using a text-image-based knowledge base. |
| Outcome: | The proposed approach integrates attributes into disambiguation. |
Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations (2022.acl-long)
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Wei Chen, Yeyun Gong, Can Xu, Huang Hu, Bolun Yao, Zhongyu Wei, Zhihao Fan, Xiaowu Hu, Bartuer Zhou, Biao Cheng, Daxin Jiang, Nan Duan
| Challenge: | Existing studies focus on coarse-grained response selection in retrieval-based dialogue systems. |
| Approach: | They propose a Contextual Fine-to-Coarse (CFC) distilled model for coarse-grained response selection in open-domain conversations. |
| Outcome: | The proposed model improves over baseline methods on two datasets based on the Reddit comments dump and Twitter corpus compared with baseline methods. |
Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code (2025.coling-industry)
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Taishi Nakamura, Mayank Mishra, Simone Tedeschi, Yekun Chai, Jason T. Stillerman, Felix Friedrich, Prateek Yadav, Tanmay Laud, Vu Minh Chien, Terry Yue Zhuo, Diganta Misra, Ben Bogin, Xuan-Son Vu, Marzena Karpinska, Arnav Varma Dantuluri, Wojciech Kusa, Tommaso Furlanello, Rio Yokota, Niklas Muennighoff, Suhas Pai, Tosin Adewumi, Veronika Laippala, Xiaozhe Yao, Adalberto Barbosa Junior, Aleksandr Drozd, Jordan Clive, Kshitij Gupta, Liangyu Chen, Qi Sun, Ken Tsui, Nour Moustafa-Fahmy, Nicolo Monti, Tai Dang, Ziyang Luo, Tien-Tung Bui, Roberto Navigli, Virendra Mehta, Matthew Blumberg, Victor May, Hiep Nguyen, Sampo Pyysalo
| Challenge: | Pretrained language models are integral part of AI applications, but their high computational cost limits accessibility. |
| Approach: | They evaluate Aurora-M, a 15B parameter multilingual open-source model trained on English, Finnish, Hindi, Japanese, Vietnamese, and code. |
| Outcome: | The proposed model outperforms existing models on English, Finnish, Hindi, Japanese, Vietnamese, and code. |
MMAC: A Multilingual, Multimodal Alignment Framework for Cultural Grounding Evaluation (2026.acl-long)
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Weihua Zheng, Zhengyuan Liu, Tanmoy Chakraborty, Weiwen Xu, Xiaoxue Gao, Bryan Chen Zhengyu Tan, Bowei Zou, Chang Liu, Yujia Hu, Xing Xie, Xiaoyuan Yi, Jing Yao, Chaojun Wang, Long Li, Rui Liu, Huiyao Liu, Koji Inoue, Ryuichi Sumida, Tatsuya Kawahara, Fan Xu, Lingyu Ye, Wei Tian, Dongjun Kim, Jimin Jung, Jaehyung Seo, Nadya Yuki Wangsajaya, Pham Minh Duc, Ojasva Saxena, Palash Nandi, Xiyan Tao, Wiwik Karlina, Tuan Luong, Keertana Arun Vasan, Roy Ka-Wei Lee, Nancy F. Chen
| Challenge: | Existing models lack cultural alignment across modalities and languages . a new framework to assess cultural awareness across linguistics and languages is needed . |
| Approach: | They propose a framework that integrates tri-modally aligned cultural benchmarks and a five-dimensional evaluation protocol to assess cross-country awareness disparities. |
| Outcome: | The proposed framework assesses cultural awareness disparities across modalities and languages . it is the first dataset aligned at the input level across text, image, and speech . |
Every Response Counts: Quantifying Uncertainty of LLM-based Multi-Agent Systems through Tensor Decomposition (2026.acl-long)
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| Challenge: | Existing methods for MAS fail to address the unique complexities of multi-step reasoning . Existing uncertainty quantification methods struggle with cascading uncertainty . |
| Approach: | They propose a framework that quantifies uncertainty through tensor decomposition . they show that MATU effectively estimates holistic and robust uncertainty . |
| Outcome: | The proposed framework disentangles and quantifies distinct sources of uncertainty . it is generalizable across different agent structures and can be used for scientific discovery, education, healthcare and transportation. |
PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language Models (2022.emnlp-main)
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| Challenge: | Recent advances on self-supervised learning have led to powerful vision-language pre-training models that achieve state-of-the-art performance on a wide range of cross-modal tasks. |
| Approach: | They propose a vision-language pre-training framework that reformulates discretized object positions and language in a unified language modeling framework. |
| Outcome: | The proposed model improves performance on position-sensitive vision-language (VL) tasks and also improves on position insensitive tasks. |
MED-COPILOT: A Medical Assistant Powered by GraphRAG and Similar Patient Case Retrieval (2026.acl-demo)
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| Challenge: | MED-COPILOT is an interactive research prototype for evidence-aware clinical reasoning . large language models (LLMs) are prone to hallucinations and lack verifiable evidence grounding . |
| Approach: | They propose a system that integrates GraphRAG and semantic-keyword similar-patient retrieval to support transparent clinical reasoning. |
| Outcome: | The proposed system outperforms baseline and standard RAGs on clinical note completion and medical question answering. |
CrowdSelect: SyntheticInstruction Data Selection with Multi-LLM Wisdom (2026.findings-eacl)
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| Challenge: | Existing methods for capturing instruction-following complexity rely on single-dimensional signals, but they fail to capture complexity across diverse fields. |
| Approach: | They propose three foundational metrics that leverage Multi-LLMs wisdom to capture instruction-response pair characteristics and propose CrowdSelect, an integrated metric incorporating a clustering-based approach to maintain response diversity. |
| Outcome: | The proposed metrics outperform existing models on MT-bench and Arena-hard and show improvements of 4.81% on full and LoRA fine-tuning. |
SceneLM: 3D-Aware Language Models for Editable 3D Scene Synthesis (2026.findings-acl)
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| Challenge: | Existing methods for synthesising 3D scenes from a single image are text-driven and lack precise metric understanding from images. |
| Approach: | They propose a language-model-based framework that grounds 3D scene synthesis in visual evidence by recovering an executable metric 3D layout directly from a single image. |
| Outcome: | The proposed framework recovers an executable metric 3D layout directly from an RGB image and instantiates, places, and edits objects for iterative refinement. |
Detoxifying Large Language Models via Knowledge Editing (2024.acl-long)
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Mengru Wang, Ningyu Zhang, Ziwen Xu, Zekun Xi, Shumin Deng, Yunzhi Yao, Qishen Zhang, Linyi Yang, Jindong Wang, Huajun Chen
| Challenge: | Existing methods to detoxify Large Language Models (LLMs) are limiting, but knowledge editing can be effective. |
| Approach: | They propose a baseline method to detoxify Large Language Models (LLMs) they propose supervised fine-tuning and reinforcement learning from human feedback (RLHF) |
| Outcome: | The proposed method reduces toxicity of large language models with one instance of tuning . it reduces the toxicity, while minimizing the toxins, the authors show . |
CaKE: Circuit-aware Editing Enables Generalizable Knowledge Learners (2025.emnlp-main)
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| Challenge: | Existing knowledge editing methods fail to generalize updates to multi-hop reasoning tasks . Existing methods only edit single or a few model layers, inadequately integrate updated knowledge into reasoning pathways. |
| Approach: | They propose a circuit-aware method that enhances the effective integration of updated knowledge in large language models by leveraging curated data samples guided by their analysis. |
| Outcome: | The proposed method improves accuracy and accuracy of 20% on the MQuAKE dataset while requiring less memory. |
Anti-Length Shift: Dynamic Outlier Truncation for Training Efficient Reasoning Models (2026.acl-long)
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Wei Wu, Liyi Chen, Congxi Xiao, Tianfu Wang, Qimeng Wang, Chengqiang Lu, Yan Gao, null Yiwu, Yao Hu, Hui Xiong
| Challenge: | Existing efficient reasoning methods rely on explicit length penalties for excessive verbosity on simple queries. |
| Approach: | They propose a training-time intervention that selectively suppresses redundant tokens . they find length shift occurs when models generate unnecessary reasoning on trivial inputs - a phenomenon that is often unexplored . |
| Outcome: | The proposed method reduces inference token usage by 78% while increasing accuracy compared to the initial policy and surpasses state-of-the-art efficient reasoning methods. |
AttnPO: Attention-Guided Process Supervision for Efficient Reasoning (2026.acl-long)
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Shuaiyi Nie, null Dingsiyu, Wenyuan Zhang, Linhao Yu, Tianmeng Yang, Yao Chen, Weichong Yin, Yu Sun, Hua Wu, Tingwen Liu
| Challenge: | Existing trajectory-level length penalties fail to effectively shorten reasoning length and degrade accuracy, as they treat all reasoning steps uniformly and lack fine-grained signals to distinguish redundancy from necessity. |
| Approach: | They propose a low-overhead process-supervised RL framework that leverages the model’s intrinsic attention signals for step-level credit assignment. |
| Outcome: | The proposed framework reduces reasoning length while improving performance across 9 benchmarks. |
LLaSA: Large Language and Structured Data Assistant (2025.naacl-long)
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Yao Xu, Shizhu He, Jiabei Chen, ZengXiangrong ZengXiangrong, Bingning Wang, Guang Liu, Jun Zhao, Kang Liu
| Challenge: | Structured knowledge grounding (SKG) tasks are a key part of many NLP applications. |
| Approach: | They propose a framework for enhancing LLMs' ability to handle structured data . they represent various types of structured data in a unified hypergraph format . |
| Outcome: | The proposed framework outperforms existing methods on SKG tasks using LoRA finetuning. |
D2PCM:A Multi-Turn Dialogue Dataset with Personalized Contextual Memory (2026.findings-acl)
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| Challenge: | Conventional interactive algorithms have predominantly treated memory as a contextual element, neglecting the nuanced cognitive processes involved in individualized memory encoding and retrieval. |
| Approach: | They propose a multi-turn dialogue dataset with Personalized Contextual Memory to facilitate advanced research on personalized memory processing. |
| Outcome: | The proposed datasets provide a comprehensive benchmark to facilitate advanced research on personalized memory processing. |
Towards Tracing Trustworthiness Dynamics: Revisiting Pre-training Period of Large Language Models (2024.findings-acl)
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| Challenge: | Existing studies focus on pre-trained LLMs to better understand and improve their trustworthiness. |
| Approach: | They apply linear probing to LLMs to explore five key dimensions of trustworthiness: reliability, privacy, toxicity, fairness, and robustness. |
| Outcome: | The proposed model can distinguish concepts in each trustworthiness dimension, suggesting that it can be trained in early pre-training. |
ONION: A Simple and Effective Defense Against Textual Backdoor Attacks (2021.emnlp-main)
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| Challenge: | Backdoor attacks can manipulate the output of deep neural networks and possess high insidiousness. |
| Approach: | They propose a textual backdoor defense based on outlier word detection that can handle all the textual attacks. |
| Outcome: | The proposed method can handle all the textual backdoor attack situations. |
EscapeBench: Towards Advancing Creative Intelligence of Language Model Agents (2025.acl-long)
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Cheng Qian, Peixuan Han, Qinyu Luo, Bingxiang He, Xiusi Chen, Yuji Zhang, Hongyi Du, Jiarui Yao, Xiaocheng Yang, Denghui Zhang, Yunzhu Li, Heng Ji
| Challenge: | Existing language model agents excel in planning and reasoning, but lack creativity in unfamiliar environments. |
| Approach: | They propose a benchmark suite of room escape game environments to challenge agents with creative reasoning, unconventional tool use and iterative problem-solving to uncover implicit goals. |
| Outcome: | The proposed framework can perform with 40% fewer steps and hints and performs robustly across difficulty levels. |
Invisible Entropy: Towards Safe and Efficient Low-Entropy LLM Watermarking (2025.emnlp-main)
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| Challenge: | Existing methods to watermark low-entropy content are expensive and risky . IE reduces parameter size by 99% while achieving performance on par with state-of-the-art methods . |
| Approach: | They propose a logit-based watermarking paradigm that uses entropy-based features to predict whether the next token is high or low. |
| Outcome: | The proposed method reduces parameter size by 99% while achieving performance on par with state-of-the-art methods. |
Reasoning with Language Model Prompting: A Survey (2023.acl-long)
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Shuofei Qiao, Yixin Ou, Ningyu Zhang, Xiang Chen, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Huajun Chen
| Challenge: | Reasoning is an essential ability for complex problem-solving and can provide back-end support for various real-world applications. |
| Approach: | They present cutting-edge research on reasoning with language model prompting and provide systematic resources to help beginners. |
| Outcome: | The proposed approaches have not been systematically reviewed and analyzed. |
Can LLMs Hear the Dogwhistle? (2026.findings-acl)
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Yifan Liu, Yi Lin, Xinwei Guo, Ziwei Wang, Jiaxin Zhang, Guanhua Chen, Haiyan Wu, Xiangyu Zhao, Xin Yao, Xuetao Wei
| Challenge: | Existing safety benchmarks focus on explicitly harmful content, but ignore context-dependent expressions such as dogwhistles. |
| Approach: | They propose a benchmark for evaluating LLM safety under dogwhistle-driven prompts . their findings expose a blind spot in current safety evaluation practices . |
| Outcome: | The proposed benchmark compared safety performance with toxic terms using dogwhistle-driven prompts. |
SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignment (2025.findings-emnlp)
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Yuqing Huang, Rongyang Zhang, Qimeng Wang, Chengqiang Lu, Yan Gao, null Yiwu, Yao Hu, Xuyang Zhi, Guiquan Liu, Xin Li, Hao Wang, Enhong Chen
| Challenge: | Existing solutions for supervised fine-tuning often lead to catastrophic forgetting, where models lose their previously acquired knowledge and general capabilities. |
| Approach: | They propose a self-distribution alignment method that aligns input sequence logits to preserve the model’s semantic distribution, thereby mitigating catastrophic forgetting and improving downstream performance. |
| Outcome: | The proposed method achieves a superior balance between downstream learning and general capability retention. |
Editing Conceptual Knowledge for Large Language Models (2024.findings-emnlp)
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Xiaohan Wang, Shengyu Mao, Shumin Deng, Yunzhi Yao, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen, Ningyu Zhang
| Challenge: | Existing knowledge editing methods can modify concept-level definitions, but they can distort instantial knowledge in LLMs, leading to poor performance. |
| Approach: | They construct a benchmark dataset ConceptEdit and establish new metrics for evaluation to investigate the editing capability of LLMs. |
| Outcome: | The proposed methods can modify concept definitions but can distort instantial knowledge in LLMs, leading to poor performance. |
EcoLANG: Efficient and Effective Agent Communication Language Induction for Social Simulation (2025.findings-emnlp)
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| Challenge: | Large language models (LLMs) have demonstrated impressive ability to role-play humans and replicate complex social dynamics. |
| Approach: | They propose an efficient agent communication language induction for social simulations that reduces token consumption by over 20%. |
| Outcome: | The proposed model reduces token consumption by over 20% while preserving human language. |
SwarmAgentic: Towards Fully Automated Agentic System Generation via Swarm Intelligence (2025.emnlp-main)
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| Challenge: | Existing agentic system generation frameworks lack autonomy, autonomy, and functionality . current frameworks are too rigid, limiting adaptability and scalability. |
| Approach: | They propose a framework that fully automates agentic system generation, optimization, and collaboration . they construct agents from scratch and jointly refine functionality and coordination . |
| Outcome: | The proposed framework outperforms ADAS on six real-world, open-ended, and exploratory tasks on the TravelPlanner benchmark. |
Language Constrained Multimodal Hyper Adapter For Many-to-Many Multimodal Summarization (2025.acl-long)
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| Challenge: | Existing models that share parameters neglect the language-specific knowledge learning. |
| Approach: | They propose a language-constrained multimodal hyper adapter for multimodal summarization that integrates language-specific adapters into multilingual pre-trained backbones. |
| Outcome: | The proposed model can generate summaries based on multimodal documents such as text and visuals, allowing people to quickly locate key information from the vast multimedia con. |
From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning (2024.naacl-long)
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| Challenge: | Large Language Models (LLMs) have achieved remarkable success in aligning with user intentions. |
| Approach: | They develop local and global explanation methods and a feed-forward-based method for input-output attribution to investigate the impact of instruction tuning on user intentions. |
| Outcome: | The proposed method compares explanations from pre-trained and instruction-tuned models . it empowers LLMs to recognize the instruction parts of user prompts, it encourages response generation . |
GCPG: A General Framework for Controllable Paraphrase Generation (2022.findings-acl)
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Kexin Yang, Dayiheng Liu, Wenqiang Lei, Baosong Yang, Haibo Zhang, Xue Zhao, Wenqing Yao, Boxing Chen
| Challenge: | Existing studies highlight a special condition under two indispensable aspects of controllable paraphrase generation (CPG) individually, lacking a unified circumstance to explore and analyze their effectiveness. |
| Approach: | They propose a general controllable paraphrase generation framework that integrates lexical and syntactical conditions into a text sequence and uniformly processes them in an encoder-decoder paradigm. |
| Outcome: | The proposed framework can combine lexical and syntactical conditions and improve paraphrase generation. |
Editing Large Language Models: Problems, Methods, and Opportunities (2023.emnlp-main)
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Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, Ningyu Zhang
| Challenge: | Recent advances in model editing for LLMs have created challenges and opportunities for the community. |
| Approach: | They propose to alter the behavior of LLMs efficiently within a specific domain without negatively impacting performance across other inputs. |
| Outcome: | The proposed method alters behavior of LLMs efficiently within a specific domain without negatively impacting performance across other inputs. |
Worldwide LiveVQA: Real-Time Visual Knowledge Seeking and Updating Across Languages (2026.findings-acl)
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| Challenge: | Existing resources for visual knowledge remain confined to English, creating a "Worldwide Knowledge Gap" eric liu: production and dissemination of knowledge exhibit a distinct trend toward decentralization and linguistic fragmentation. |
| Approach: | They propose a dataset for multilingual visual knowledge seeking and updating across ten major languages. |
| Outcome: | The proposed dataset is the first dynamic-updating dataset for multilingual visual knowledge seeking and updating across ten major languages. |
Smart “Chef”: Verifying the Effect of Role-based Paraphrasing for Aspect Term Extraction (2023.findings-emnlp)
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| Challenge: | Aspect Term Extraction (ATE) is a task of automatically extracting aspect terms from sentences. |
| Approach: | They propose to automatically rewrite sentences from virtual experts with different roles . they leverage ChatGPT to determine virtual experts in the considered domains . |
| Outcome: | The proposed method can be used to expand the predictions obtained on the original sentences without retraining or fine-tuning the baseline extractors. |
Paper2Web: Let’s Make Your Paper Alive! (2026.acl-demo)
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| Challenge: | Current approaches to creating layout-aware, interactive academic webpages are limited. |
| Approach: | They propose a benchmark dataset and evaluation framework for assessing academic webpage generation that incorporates rule-based metrics like Connectivity, Completeness and PaperQuiz. |
| Outcome: | The proposed framework outperforms baselines like template-based webpages and arXiv/alphaXivo versions while maintaining low cost. |
AutoPRM: Automating Procedural Supervision for Multi-Step Reasoning via Controllable Question Decomposition (2024.naacl-long)
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| Challenge: | Recent advances in large language models (LLMs) have shown promise in multi-step reasoning tasks, yet relying on extensive manual labeling to provide procedural feedback remains a significant impediment. |
| Approach: | They propose a self-supervised framework that decomposes complex problems into manageable subquestions with a controllable granularity switch and sequentially applies reinforcement learning to iteratively improve the subquest solver. |
| Outcome: | The proposed framework improves performance on mathematical and commonsense reasoning tasks over SOTA. |
SDAR: A Synergistic Diffusion-AutoRegression Paradigm for Scalable Sequence Generation (2026.findings-acl)
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Shuang Cheng, Yihan Bian, Dawei Liu, Yuhua Jiang, Yihao Liu, Linfeng Zhang, Qian Yao, Zhongbo Tian, Wenhai Wang, Qipeng Guo, Kai Chen, Biqing Qi, Bowen Zhou
| Challenge: | Autoregressive (AR) language models are a dominant paradigm in the field of parallelism and non-causal modeling. |
| Approach: | They propose a blockwise discrete diffusion model that preserves AR-compatible serving while enabling parallel intra-block generation. |
| Outcome: | The proposed model achieves theoretical speedups over 5 and wall-clock speedup of 2.3 on H200 GPUs in latency-critical regimes. |
CoLA: Collaborative Low-Rank Adaptation (2025.findings-acl)
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| Challenge: | The scaling law of Large Language Models (LLMs) reveals diminishing return on performance as model scale increases. |
| Approach: | They propose a more flexible LoRA architecture with an efficient initialization scheme . they propose combining three collaborative strategies to enhance performance . |
| Outcome: | The proposed model outperforms existing methods in low-sample scenarios. |
A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU (2024.lrec-main)
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| Challenge: | Multi-intent natural language understanding (NLU) models lack the rich information between the shared intents, especially in low-data scenarios. |
| Approach: | They propose a two-stage framework for multi-intent natural language understanding to harness shared intent information by word-level pre-training and prediction-aware contrastive fine-tuning. |
| Outcome: | The proposed framework surpasses baselines on low-data and full-data scenarios. |
How Do LLMs Acquire New Knowledge? A Knowledge Circuits Perspective on Continual Pre-Training (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have exceptional capabilities in knowledge-intensive tasks . however, they struggle with knowledge updates due to dynamic nature of world knowledge . |
| Approach: | They propose to identify computational subgraphs that facilitate knowledge storage and processing . they also identify a phase shift from formation to optimization in LLMs . |
| Outcome: | The proposed model can capture factual knowledge from pre-training corpus and encapsulate it as extensive parametric knowledge. |
Yet Another Format of Universal Dependencies for Korean (2022.coling-1)
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Yige Chen, Eunkyul Leah Jo, Yundong Yao, KyungTae Lim, Miikka Silfverberg, Francis M. Tyers, Jungyeul Park
| Challenge: | Existing dependency parsers for Korean do not perform as well as their English counterparts due to the complexity of Korean's linguistic features. |
| Approach: | They propose a morpheme-based Korean dependency parsing format and propose to adopt it to Universal Dependencies. |
| Outcome: | The proposed format outperforms parsing results for Korean UD treebanks and detailed error analysis. |
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation (2020.emnlp-main)
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| Challenge: | Existing adversarial text generation approaches can lead to generation lacking diversity or fluency, whereas perturbing in the intermediate representation space can lead a model to generate generations that are not related to the input. |
| Approach: | They propose to generate adversarial texts through controllable attributes that are known to be invariant to task labels. |
| Outcome: | The proposed model generates more diverse and fluent adversarial examples, compared to existing approaches, and is more robust against model re-training and different model architectures. |
ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation (2021.acl-demo)
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Weizhen Qi, Yeyun Gong, Yu Yan, Can Xu, Bolun Yao, Bartuer Zhou, Biao Cheng, Daxin Jiang, Jiusheng Chen, Ruofei Zhang, Houqiang Li, Nan Duan
| Challenge: | Existing models for pre-training are not convenient for users to find and set them up. |
| Approach: | They propose to extend ProphetNet into other domains and languages by pre-training models . they pre-train a cross-lingual generation model ProphetNet-Multi and a Chinese generation model . |
| Outcome: | The proposed models achieve new state-of-the-art on 10 benchmarks. |
From Evasion to Concealment: Stealthy Knowledge Unlearning for LLMs (2025.findings-acl)
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| Challenge: | Existing approaches to unlearning often treat nonsensical responses or template-based refusals as the unlearning target, making the process even more vulnerable to attacks and jailbreaks. |
| Approach: | They propose a method that uses inverted facts to remove the need for auxiliary models or retaining data while avoiding leakage. |
| Outcome: | Evaluated on the ToFU Knowledge Unlearning dataset using Llama2-7B-Chat and Phi-1.5, MEOW outperforms baselines in forgetting quality while preserving model utility. |
Chain-of-Talkers (CoTalk): Fast Human Annotation of Dense Image Captions (2025.emnlp-main)
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| Challenge: | Existing approaches for optimizing human annotation efforts are limited . et al., 2015) suggest that densely annotated image captions improve vision-language alignment . |
| Approach: | They propose an AI-in-the-loop methodology to maximize the number of annotated samples and improve their comprehensiveness under fixed budget constraints. |
| Outcome: | The proposed method improves annotation speed and retrieval performance over the parallel method. |
KEEP CHATTING! An Attractive Dataset for Continuous Conversation Agents (2024.findings-acl)
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| Challenge: | Existing works about persona dialogue such as PersonaChat have greatly facilitated the chatbot with configurable and persistent personalities. |
| Approach: | They propose to collect a dataset called ContinuousChat and rewrite it in style-specific ways to increase users' willingness to continue chatting. |
| Outcome: | The proposed model increases users' willingness to continue talking to the chatbot by increasing their personas to detailed-personas through experiences, daily life, future plans, or interesting stories. |
Generative Frame Sampler for Long Video Understanding (2025.findings-acl)
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| Challenge: | Existing video large language models (LMMs) employ an impedance of thousands of frames to understand long videos. |
| Approach: | They propose a plug-and-play module integrated with VideoLLMs to facilitate efficient lengthy video perception. |
| Outcome: | The proposed module boosts the performance of open-source VideoLLMs and proprietary assistants on long-form video benchmarks. |
AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora (2026.acl-long)
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Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong, Chunyang Li, Hong Ting Tsang, Hongyu Luo, Yauwai Yim, Haoyu Huang, Xiao Zhou, Feng Qin, Tianshi Zheng, Xi Peng, Xin Yao, Huiwen Yang, Leijie Wu, JI Yi, Gong Zhang, Renhai Chen, Yangqiu Song
| Challenge: | Existing knowledge graph construction frameworks require predefined schemas, limiting their scalability and domain coverage. |
| Approach: | They propose a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas. |
| Outcome: | The proposed framework outperforms state-of-the-art models on multi-hop QA tasks and enhances LLM factuality. |
AEQ-Bench: Measuring Empathy of Omni-Modal Large Models (2026.findings-acl)
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| Challenge: | Existing benchmarks focus on cognitive abilities, such as knowledge retrieval, complex reasoning, and instruction following, largely overlooking empathy evaluation. |
| Approach: | They propose to benchmark two core empathetic capabilities of omnimodal large models (OLMs) generating empatries by comprehending affective cues from multi-modal inputs and judging empathy of audio responses without relying on text transcription. |
| Outcome: | The proposed benchmark outperforms existing models with audio output capabilities but is unreliable for evaluating fine-grained paralinguistic expressiveness. |
MoVa: Towards Generalizable Classification of Human Morals and Values (2025.emnlp-main)
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Ziyu Chen, Junfei Sun, Chenxi Li, Tuan Dung Nguyen, Jing Yao, Xiaoyuan Yi, Xing Xie, Chenhao Tan, Lexing Xie
| Challenge: | Identifying human morals and values embedded in language is essential to empirical studies of communication. |
| Approach: | They propose a framework for generalizable classification of human morals and values . they recommend a classification strategy that scores all related concepts simultaneously . |
| Outcome: | The proposed method outperforms fine-tuned models across domains and frameworks. |
MoDification: Mixture of Depths Made Easy (2025.naacl-long)
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Chen Zhang, Meizhi Zhong, Qimeng Wang, Xuantao Lu, Zheyu Ye, Chengqiang Lu, Yan Gao, Yao Hu, Kehai Chen, Min Zhang, Dawei Song
| Challenge: | Long-context efficiency is a trending topic in large language model (LLM) serving. |
| Approach: | They propose a method to combine long-context efficiency and mixture of depths to bring down both latency and memory. |
| Outcome: | The proposed method achieves 1.2 speedup in latency and 1.8 reduction in memory compared to original LLMs especially in long-context applications. |
TAG : Type Auxiliary Guiding for Code Comment Generation (2020.acl-main)
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| Challenge: | Existing code comment generation approaches ignore type information of interpretation of the code, e.g., operator, string, etc. Existing approaches ignore the type information due to the hierarchical dependence among the type. |
| Approach: | They propose an encoder-decoder framework which considers the source code as an N-ary tree with type information associated with each node. |
| Outcome: | The proposed framework is based on a Type Auxiliary Guiding encoder-decoder framework and a type-restricted Decoder to resolve training difficulties. |
MoleculeQA: A Dataset to Evaluate Factual Accuracy in Molecular Comprehension (2024.findings-emnlp)
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| Challenge: | Existing models generate erroneous information and evaluations fail to assess factual correctness of models. |
| Approach: | They propose to use MoleculeQA to evaluate molecular factual correctness in large language models by organizing molecules into a taxonomy and building QA pairs through human and LLM efforts. |
| Outcome: | The proposed model improves the factual correctness of generated information and enables the development of new models. |
Understanding the RoPE Extensions of Long-Context LLMs: An Attention Perspective (2025.coling-main)
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| Challenge: | Enabling LLMs to handle lengthy context is currently a research hotspot . a notable challenge limiting further customization is the inability of LLM to utilize context beyond pretrained length due to the inherent flaw of rotary position embedding (RoPE). |
| Approach: | They propose to extend the RoPE from an attention perspective and on two benchmarking tasks. |
| Outcome: | The proposed extension of the RoPE improves extrapolation and retrieval errors. |
Investigating Ensemble Methods for Model Robustness Improvement of Text Classifiers (2022.findings-emnlp)
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| Challenge: | Existing methods to reduce model's reliance on bias features ignore the learnability of these features. |
| Approach: | They propose to reduce models' reliance on bias features by first training models with fixed low-capacity models which ignore the learnability of the bias features. |
| Outcome: | The proposed models can perform better on out-of-distribution datasets than baseline models with a more sophisticated model design. |
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. |
Multi-Lingual Question Generation with Language Agnostic Language Model (2021.findings-acl)
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| Challenge: | Existing training data for question generation in English and Chinese is limited . a language-agnostic model is developed to learn the shared representation from several languages in a single architecture. |
| Approach: | They propose a language-agnostic language model which learns the shared representation from several languages in a single architecture. |
| Outcome: | The proposed model improves multi-lingual question generation over five languages. |
Illusions of Confidence? Diagnosing LLM Truthfulness via Neighborhood Consistency (2026.acl-long)
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Haoming Xu, Ningyuan Zhao, Yunzhi Yao, Weihong Xu, Hongru Wang, Xinle Deng, Shumin Deng, Jeff Z. Pan, Huajun Chen, Ningyu Zhang
| Challenge: | Existing evaluations rely on point-wise confidence, which can mask brittle belief. |
| Approach: | They propose a measure of belief robustness that evaluates coherence across a conceptual neighborhood. |
| Outcome: | The proposed model is more resistant to interference than existing models. |
Why Do Emotions Change? Appraisal-Guided Reasoning for Emotion–Cause Triplet Extraction in Conversations (2026.acl-long)
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| Challenge: | Existing methods for multi-turn, multi-speaker multimodal affect understanding are difficult to maintain conversation-level consistency under within-speaks' emotion shifts. |
| Approach: | They propose a framework that combines appraisal-guided structured generation with graph-structured reinforcement learning to extract triplets from multi-turn multimodal conversations. |
| Outcome: | The proposed framework outperforms baselines on public MECTEC benchmarks and improves structure-aware metrics on emotion shift coherence and core events. |
Learning-by-Narrating: Narrative Pre-Training for Zero-Shot Dialogue Comprehension (2022.acl-short)
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| Challenge: | Existing models for dialogue comprehension are not available for the pre-training of such a model. |
| Approach: | They propose a narrative-guided pre-training strategy that learns by narrating key information from a dialogue input. |
| Outcome: | The proposed model performs better on four dialogue-based tasks and is comparable to existing models. |
Does Memory Need Graphs? A Unified Framework and Empirical Analysis for Long-Term Dialog Memory (2026.acl-long)
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| Challenge: | Existing literature on dialog memory systems is inconsistent on their effectiveness . empirical findings on graph structures are difficult to attribute to specific design choices . |
| Approach: | They propose a framework that decomposes dialog memory systems into core components . they conduct stage-wise experiments on LongMemEval and HaluMeM, and compare implementation details . |
| Outcome: | The proposed framework compares graph-based and non-graph memory architectures on long-term dialog memory systems. |
GUICourse: From General Vision Language Model to Versatile GUI Agent (2025.acl-long)
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Wentong Chen, Junbo Cui, Jinyi Hu, Yujia Qin, Junjie Fang, Yue Zhao, Chongyi Wang, Jun Liu, Guirong Chen, Yupeng Huo, Yuan Yao, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Graphical User Interfaces (GUIs) are a pivotal medium for human-computer interaction. |
| Approach: | They propose a series of datasets for training visual-based GUI agents using general VLMs. |
| Outcome: | The proposed GUICourse datasets show that even a small-sized GUI agent performs better on GUI tasks. |
EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models (2024.acl-demos)
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Peng Wang, Ningyu Zhang, Bozhong Tian, Zekun Xi, Yunzhi Yao, Ziwen Xu, Mengru Wang, Shengyu Mao, Xiaohan Wang, Siyuan Cheng, Kangwei Liu, Yuansheng Ni, Guozhou Zheng, Huajun Chen
| Challenge: | Large Language Models (LLMs) suffer from knowledge cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to outdated/noisy data. |
| Approach: | They propose an easy-to-use knowledge editing framework for Large Language Models that allows users to easily edit updated knowledge and adjust undesired behavior while minimizing the impact on unrelated inputs. |
| Outcome: | The proposed framework surpasses traditional fine-tuning in terms of reliability and generalization. |
SGIC: A Self-Guided Iterative Calibration Framework for RAG (2025.acl-long)
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| Challenge: | Existing studies on retrieval-augmented generation (RAG) focus on extracting relevant documents or refinement of specialized instructions. |
| Approach: | They propose a framework that provides LLMs with specific cues to improve their calibration efficacy . they propose an iterative self-calibration training set that harnesses uncertainty scores . |
| Outcome: | The proposed framework significantly improves performance on both closed-source and open-source LLMs. |
Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study (2025.coling-main)
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Bowen Li, Wenhan Wu, Ziwei Tang, Lin Shi, John Yang, Jinyang Li, Shunyu Yao, Chen Qian, Binyuan Hui, Qicheng Zhang, Zhiyin Yu, He Du, Ping Yang, Dahua Lin, Chao Peng, Kai Chen
| Challenge: | Existing benchmarks focused on simplified or isolated aspects of coding, ignoring the full spectrum of programming challenges. |
| Approach: | They propose a case study that examines the performance of large language models across the entire software development lifecycle with four programming languages, multiple domains, and carefully designed and verified metrics for each task. |
| Outcome: | The proposed model performs across the entire software development lifecycle, including design, environment setup, implementation, acceptance testing, and unit testing. |
MedVerse: Efficient and Reliable Medical Reasoning via DAG-Structured Parallel Execution (2026.acl-long)
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Jianwen Chen, Xinyu Yang, Peng Xia, Arian Azarang, Yueh Z Lee, Gang Li, Hongtu Zhu, Yun Li, Beidi Chen, Huaxiu Yao
| Challenge: | Recent advances in large reasoning models have broadened the capabilities of medical artificial intelligence. |
| Approach: | They propose a reasoning framework for complex medical inference that reformulates medical reasoning as a parallelizable directed acyclic graph process based on Petri Net theory. |
| Outcome: | The proposed reasoning framework improves strong general-purpose LLMs by up to 8.9%. |
Implicit Relation Linking for Question Answering over Knowledge Graph (2022.findings-acl)
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| Challenge: | Existing methods rely on textual similarities between NL and KG to build relation links. |
| Approach: | They propose an implicit relation linking method called ImRL which links relation phrases in NL to relation paths in KG. |
| Outcome: | The proposed method significantly outperforms state-of-the-art methods on two benchmarks and a newly-created datasets. |
Text2World: Benchmarking Large Language Models for Symbolic World Model Generation (2025.findings-acl)
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Mengkang Hu, Tianxing Chen, Yude Zou, Yuheng Lei, Qiguang Chen, Ming Li, Yao Mu, Hongyuan Zhang, Wenqi Shao, Ping Luo
| Challenge: | Recent studies have encountered limitations in leveraging large language models to generate symbolic world models. |
| Approach: | They propose a benchmarking framework based on planning domain definition language (PDDL) that employs multi-criteria, execution-based metrics for a more robust evaluation. |
| Outcome: | The proposed model outperforms models trained with large-scale reinforcement learning, but lacks the robustness needed to perform in world modeling. |
More Samples or More Prompts? Exploring Effective Few-Shot In-Context Learning for LLMs with In-Context Sampling (2024.findings-naacl)
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Bingsheng Yao, Guiming Chen, Ruishi Zou, Yuxuan Lu, Jiachen Li, Shao Zhang, Yisi Sang, Sijia Liu, James Hendler, Dakuo Wang
| Challenge: | Existing studies on LLM prompting focus on selecting a better set of data samples inside one single prompt input, but why not design and leverage multiple ICL prompts together to further improve the LLM’s performance? |
| Approach: | They propose a low-resource LLM prompting technique to optimize the construction of multiple ICL prompt inputs to produce confident predictions. |
| Outcome: | The proposed technique can produce confident predictions by optimizing the construction of multiple ICL prompt inputs on four NLI datasets and one QA dataset. |
Multilingual Language Model Pretraining using Machine-translated Data (2025.emnlp-main)
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Jiayi Wang, Yao Lu, Maurice Weber, Max Ryabinin, David Ifeoluwa Adelani, Yihong Chen, Raphael Tang, Pontus Stenetorp
| Challenge: | Existing methods for collecting and filtering multilingual web data lead to most languages lagging behind English performance due to the Internet's English-centric nature. |
| Approach: | They propose to translate a high-quality English web corpus into nine languages and pretrain a 1.3B-parameter model on it. |
| Outcome: | The proposed model matches or outperforms multilingual LLMs of similar size across Non-English understanding and reasoning tasks despite being trained on an order of magnitude less data. |
MCPEval: Automatic MCP-based Deep Evaluation for AI Agent Models (2025.emnlp-demos)
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Zhiwei Liu, Jielin Qiu, Shiyu Wang, Jianguo Zhang, Zuxin Liu, Roshan Ram, Haolin Chen, Weiran Yao, Shelby Heinecke, Silvio Savarese, Huan Wang, Caiming Xiong
| Challenge: | Existing evaluation frameworks suffer from limitations such as static task benchmarks, limited scope, and inadequate integration with practical applications. |
| Approach: | They propose an open-source, Model Context Protocol-based evaluation framework specifically tailored for comprehensive and systematic assessment of LLM-powered agents. |
| Outcome: | The proposed framework uncovers nuanced performance patterns and identify domain-specific strengths and weaknesses, providing valuable insights beyond traditional binary success metrics. |
LLM-as-a-Coauthor: Can Mixed Human-Written and Machine-Generated Text Be Detected? (2024.findings-naacl)
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Qihui Zhang, Chujie Gao, Dongping Chen, Yue Huang, Yixin Huang, Zhenyang Sun, Shilin Zhang, Weiye Li, Zhengyan Fu, Yao Wan, Lichao Sun
| Challenge: | Current research focuses on purely MGT detection without adequately addressing mixed scenarios including AI-revised Human-Written Text (HWT) and human-revealed MGT. |
| Approach: | They define mixtext, a form of mixed text involving both AI and human-generated content, and then use a MixSet dataset to assess their effectiveness. |
| Outcome: | The proposed detectors struggle to identify mixtext, particularly in dealing with subtle modifications and style adaptability. |
Think Both Ways: Teacher-Student Bidirectional Reasoning Enhances MCQ Generation and Distractor Quality (2025.findings-acl)
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| Challenge: | Existing methods for generating high-quality MCQs struggle with contextual relevance and plausible distractors. |
| Approach: | They propose a framework that integrates bidirectional reasoning perspectives to generate contextually relevant questions and plausible distractors while student reasoning evaluates question clarity and the misleading nature of distractors. |
| Outcome: | The proposed framework outperforms existing methods in generating text-grounded questions and high-quality distractors for narrative contexts. |
Noisy-Labeled NER with Confidence Estimation (2021.naacl-main)
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| Challenge: | Recent studies in deep learning have shown significant progress in named entity recognition (NER) . however, most existing works assume clean data annotation, while real-world data typically involve a large amount of noises. |
| Approach: | They propose a confidence estimation approach for named entity recognition using noisy labels using local and global independence assumptions. |
| Outcome: | The proposed method marginalizes out labels of low confidence with a CRF model and integrates it into a self-training framework for boosting performance. |
Bridging Continuous and Discrete Spaces: Interpretable Sentence Representation Learning via Compositional Operations (2023.emnlp-main)
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| Challenge: | Existing approaches to learn sentence embeddings do not capture the semantic similarity of sentences. |
| Approach: | They propose a framework that integrates compositional sentence operations into the embedding space and optimizes operator networks and a bottleneck encoder-decoder model to produce meaningful and interpretable sentence embeddables. |
| Outcome: | The proposed framework improves the interpretability of sentence embeddings on four textual generation tasks while maintaining strong performance on traditional semantic similarity tasks. |
Enhancing Code Generation Performance of Smaller Models by Distilling the Reasoning Ability of LLMs (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) have made significant advances in code generation through the ‘Chain-of-Thought’ prompting technique. |
| Approach: | They propose a framework which aims to transfer LLMs’ reasoning capabilities to smaller models through distillation. |
| Outcome: | The proposed framework improves the smaller model's code generation performance by over 130% on the APPS benchmark. |
Chain-of-Procedure: Hierarchical Visual-Language Reasoning for Procedural QA (2026.findings-acl)
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Guanhua Chen, Yutong Yao, Shenghe Sun, Ci-jun Gao, Shudong Liu, Lidia S. Chao, Feng Wan, Derek F. Wong
| Challenge: | Recent advances in vision-language models (VLMs) have achieved impressive results on standard image-text tasks, yet their capability in visual procedure question answering (VP-QA) remains largely unexplored. |
| Approach: | They propose a multimodal benchmark specifically designed for visual procedural reasoning that synergizes cross-modal procedure retrieval, context-aware step decomposition, and the next step prediction. |
| Outcome: | The proposed framework significantly outperforms baselines on visual procedure question answering (VP-QA) Experiments on six VLMs show that it performs better than baselines. |
Beyond the Context Window: Scaling Agentic RL via End-to-end Optimized Context Compression (2026.acl-long)
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| Challenge: | Existing reinforcement learning pipelines suffer from degraded instruction following, excessive rollout costs, and strict context limits. |
| Approach: | They propose a reinforcement learning (RL) fine-tuning of large language model (LLM) agents for long-horizon multi-turn tool use where context length quickly becomes a bottleneck. |
| Outcome: | The proposed framework improves the success rate while maintaining the same or even lower working context length compared to baselines. |
Taming Text-to-Image Synthesis for Novices: User-centric Prompt Generation via Multi-turn Guidance (2025.emnlp-main)
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Yilun Liu, Minggui He, Feiyu Yao, Yuhe Ji, Shimin Tao, Jingzhou Du, Justin Li, Jian Gao, Zhang Li, Hao Yang, Boxing Chen, Osamu Yoshie
| Challenge: | Existing solutions for text-to-image synthesis are sensitive on textual prompts, posing a challenge for novice users. |
| Approach: | They propose a dialogue-based TIS prompt generation model that emphasizes user experience for novice users. |
| Outcome: | The proposed model emphasizes user experience for novice users . it improves user-centricity score while maintaining a competitive quality of synthesized images. |
Finding the Sweet Spot: Preference Data Construction for Scaling Preference Optimization (2025.acl-long)
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| Challenge: | Large language models generate unintended outputs due to their unsupervised nature. |
| Approach: | They propose a method to construct preference pairs of selected and rejected LLMs by repeated random sampling to improve alignment performance. |
| Outcome: | The proposed method improves performance as the sample size increases. |
Salience Allocation as Guidance for Abstractive Summarization (2022.emnlp-main)
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Fei Wang, Kaiqiang Song, Hongming Zhang, Lifeng Jin, Sangwoo Cho, Wenlin Yao, Xiaoyang Wang, Muhao Chen, Dong Yu
| Challenge: | Abstractive summarization models implicitly learn to capture the salient information from scratch. |
| Approach: | They propose a method that uses salience expectation to guide abstractive summarization by averaging salient content to a fixed threshold. |
| Outcome: | The proposed method can be easily adapted to documents with various abstractiveness and achieves high performance. |
CoMMIT: Coordinated Multimodal Instruction Tuning (2025.emnlp-main)
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Xintong Li, Junda Wu, Tong Yu, Rui Wang, Yu Wang, Xiang Chen, Jiuxiang Gu, Lina Yao, Julian McAuley, Jingbo Shang
| Challenge: | et al., 2024) show that multimodal instruction tuning is more effective than baselines. |
| Approach: | They propose a multimodal balance coefficient that enables quantitative measurement of the balance of learning . they propose auxiliary regularization on the gradient to promote updating with larger step sizes . |
| Outcome: | The proposed method is more effective than baselines in MLLM instruction tuning. |
Evaluating Small Language Models for News Summarization: Implications and Factors Influencing Performance (2025.naacl-long)
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| Challenge: | Large language models (LLMs) provide superior summarization quality, but their high computational resource requirements limit practical use applications. |
| Approach: | They evaluate 19 small language models for news summarization across 2,000 news samples . they find that top-performing models achieve comparable results to those of 70B LLMs . |
| Outcome: | The proposed models achieve comparable results to 70B LLMs while generating more concise summaries. |
The Impact of Large Language Models in Academia: from Writing to Speaking (2025.findings-acl)
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| Challenge: | Large language models (LLMs) are impacting human society, especially in textual information. |
| Approach: | They propose to build an automated monitoring platform to track the impact of large language models on human expression. |
| Outcome: | The results show that LLM-style words such as significant are used more frequently in abstracts and oral presentations. |
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. |
R2I-Bench: Benchmarking Reasoning-Driven Text-to-Image Generation (2025.emnlp-main)
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| Challenge: | Reasoning is a fundamental capability underpinning text-to-image (T2I) generation. |
| Approach: | They propose a benchmark to rigorously assess reasoning-driven T2I generation. |
| Outcome: | Experiments with 16 representative T2I models show limited reasoning performance . a strong pipeline-based framework decouples reasoning and generation . |
HiAgent: Hierarchical Working Memory Management for Solving Long-Horizon Agent Tasks with Large Language Model (2025.acl-long)
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| Challenge: | Existing approaches to optimize agent performance by incorporating entire historical action-observation pairs into LLMs are redundant in long-horizon tasks. |
| Approach: | They propose a framework that leverages subgoals as memory chunks to manage working memory of LLM-based agents hierarchically. |
| Outcome: | The proposed framework achieves a twofold increase in success rate and reduces the average number of steps required by 3.8. |
Uncertainty Propagation on LLM Agent (2025.acl-long)
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Qiwei Zhao, Dong Li, Yanchi Liu, Wei Cheng, Yiyou Sun, Mika Oishi, Takao Osaki, Katsushi Matsuda, Huaxiu Yao, Chen Zhao, Haifeng Chen, Xujiang Zhao
| Challenge: | Existing methods for estimating uncertainty in large language models (LLMs) focus on final-step outputs, which fail to account for cumulative uncertainty over multi-step decision-making process and dynamic interactions between agents and their environments. |
| Approach: | They propose a framework that propagates uncertainty through each step of an LLM-based agent’s reasoning process. |
| Outcome: | Extensive experiments on benchmark datasets show that the proposed framework outperforms state-of-the-art methods by 20%. |
MPCoder: Multi-user Personalized Code Generator with Explicit and Implicit Style Representation Learning (2024.acl-long)
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| Challenge: | Existing Large Language Models are usually generalized with large programming corpus, therefore the generated code is difficult to adapt to personalized and/or customized requests. |
| Approach: | They propose a method to use Large Language Models to generate personalized code for multiple users. |
| Outcome: | The proposed model can generate personalized code for multiple users . it can be used to improve code generation and reduce maintenance costs. |
D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat (2022.emnlp-main)
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| Challenge: | Existing human-machine dialogue systems are not able to provide diagnostic information for depression diagnosis due to stigma associated with mental illness. |
| Approach: | They propose to construct a Chinese Dialogue Dataset for depression-diagnosis-oriented chat based on clinical depression diagnostic criteria. |
| Outcome: | The proposed system can be used to diagnose depression using a Chinese Dialogue Dataset. |
Autonomous Workflow for Multimodal Fine-Grained Training Assistants Towards Mixed Reality (2024.findings-acl)
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Jiahuan Pei, Irene Viola, Haochen Huang, Junxiao Wang, Moonisa Ahsan, Fanghua Ye, Jiang Yiming, Yao Sai, Di Wang, Zhumin Chen, Pengjie Ren, Pablo Cesar
| Challenge: | a fine-grained, comprehensive understanding of multimodal environments remains under-explored. |
| Approach: | They propose an automated workflow for integrating AI agents into extended reality (XR) they propose a cerebral language agent that integrates LLM with memory, planning, and interaction with XR tools and a vision-language agent . |
| Outcome: | The proposed workflow integrates AI agents seamlessly into extended reality (XR) applications for fine-grained training. |
Multi-Agent-as-Judge: Aligning LLM-Agent-Based Automated Evaluation with Multi-Dimensional Human Evaluation (2026.acl-long)
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Jiaju Chen, Yuxuan Lu, Xiaojie Wang, Huimin Zeng, Jing Huang, Jiri Gesi, Ying Xu, Bingsheng Yao, Dakuo Wang
| Challenge: | Existing "LLM-as-a-judge" evaluation frameworks are limited by persona descriptions and are not generalizable to other tasks. |
| Approach: | They propose a framework that can automatically construct multiple evaluator personas with distinct dimensions from relevant text documents and instantiate LLM agents with the persona. |
| Outcome: | The proposed framework can believably simulate human evaluators . it extracts stakeholders' diverse perspectives from the provided research papers and constructs personas for the agents . |
GUI Agents: A Survey (2025.findings-acl)
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Dang Nguyen, Jian Chen, Yu Wang, Gang Wu, Namyong Park, Zhengmian Hu, Hanjia Lyu, Junda Wu, Ryan Aponte, Yu Xia, Xintong Li, Jing Shi, Hongjie Chen, Viet Dac Lai, Zhouhang Xie, Sungchul Kim, Ruiyi Zhang, Tong Yu, Mehrab Tanjim, Nesreen K. Ahmed, Puneet Mathur, Seunghyun Yoon, Lina Yao, Branislav Kveton, Jihyung Kil, Thien Huu Nguyen, Trung Bui, Tianyi Zhou, Ryan A. Rossi, Franck Dernoncourt
| Challenge: | Large Foundation Models (LFMs) have transformed the landscape of AI research and day-to-day life. |
| Approach: | They propose a framework that delineates GUI agents' perception, reasoning, planning, and acting capabilities. |
| Outcome: | The proposed framework delineates their perception, reasoning, planning, and acting capabilities. |
CodeRetriever: A Large Scale Contrastive Pre-Training Method for Code Search (2022.emnlp-main)
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Xiaonan Li, Yeyun Gong, Yelong Shen, Xipeng Qiu, Hang Zhang, Bolun Yao, Weizhen Qi, Daxin Jiang, Weizhu Chen, Nan Duan
| Challenge: | Existing code pre-training approaches often adopt (masked) language modeling as the training objective which targets on learning to predict (macked) tokens in a given code context. |
| Approach: | They propose a code-text contrastive learning model which learns function-level code semantic representations through large-scale code corpus. |
| Outcome: | The proposed model achieves new state-of-the-art with significant improvement over existing pre-trained models on eleven domain/language-specific code search tasks with six programming languages in different code granularity. |
ZigZagKV: Dynamic KV Cache Compression for Long-context Modeling based on Layer Uncertainty (2025.coling-main)
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| Challenge: | Existing methods to accelerate inference of Large Language models (LLMs) are limited in their ability to retain key tokens as input length increases. |
| Approach: | They propose a method that leverages layer uncertainty to allocate budget size for each layer to reduce memory usage. |
| Outcome: | The proposed method reduces memory usage of the KV caches to only 20% when compared to full KV inference while achieving nearly lossless performance. |
MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning (2024.naacl-long)
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Fuxiao Liu, Xiaoyang Wang, Wenlin Yao, Jianshu Chen, Kaiqiang Song, Sangwoo Cho, Yaser Yacoob, Dong Yu
| Challenge: | Existing large language models have limited ability to perform tasks effectively. |
| Approach: | They propose a large-scale multimodal chart instruction dataset with 600k instances supporting diverse tasks and chart types. |
| Outcome: | The proposed LMM achieves state-of-the-art performance on existing chart QA benchmarks. |
SLIM: Subtrajectory-Level Elimination for More Effective Reasoning (2025.findings-emnlp)
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Xifeng Yao, Chengyuan Ma, Dongyu Lang, Yinhao Ni, Zhiwei Xu, Huarui Xie, Zihao Chen, Guang Shen, Dandan Tu, Yi Bai, Changzheng Zhang
| Challenge: | Notable examples include OpenAI’s o1/o3/o4 series and DeepSeek-R1 . |
| Approach: | They develop a framework to identify suboptimal subtrajectories based on human-established criteria . they also use a sampling algorithm to select data whose reasoning process is free from suboptimally subtravertories to the highest degree . |
| Outcome: | The proposed method reduces the number of suboptimal subtrajectories by 25.9% during the inference process. |
VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering (2023.acl-demo)
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Zijun Yao, Yuanyong Chen, Xin Lv, Shulin Cao, Amy Xin, Jifan Yu, Hailong Jin, Jianjun Xu, Peng Zhang, Lei Hou, Juanzi Li
| Challenge: | Existing knowledge base question answering systems that parse natural language questions into knowledge oriented program language (KoPL) . |
| Approach: | They propose a knowledge base question answering system that integrates human into the loop to edit and debug queries. |
| Outcome: | The proposed system can debug and edit knowledge base questions on a million-entity-level . it provides auto-completion for its knowledge base schema and user interaction can fix a large portion of wrong KoPL programs to acquire the correct answer. |
RealMem: Benchmarking LLMs in Real-World Memory-Driven Interaction (2026.findings-acl)
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Haonan Bian, Zhiyuan Yao, Sen Hu, Zishan Xu, Shaolei Zhang, Yifu Guo, Ziliang Yang, Xueran Han, Huacan Wang, Ronghao Chen
| Challenge: | Existing benchmarks focus on casual conversation or task-oriented dialogue, failing to capture “long-term project-oriented” interactions where agents must track evolving goals. |
| Approach: | They propose a benchmark that simulates the dynamic evolution of memory in real-world projects. |
| Outcome: | The proposed benchmarks simulate the dynamic evolution of memory in real-world projects. |
LESA: Learnable LLM Layer Scaling-Up (2025.acl-long)
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| Challenge: | Existing methods for depth scaling-up rely on empirical heuristic rules for layer duplication, resulting in poor initialization and slower convergence during continual pre-training. |
| Approach: | They propose a method for learning latent parameters between layers by concatenating parameters from each layer and applying Singular Value Decomposition. |
| Outcome: | Experiments show that LESA outperforms baseline models with less than half the cost of existing methods. |
Apertus: Democratizing Open and Compliant LLMs for Global Language Environments (2026.acl-long)
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Alejandro Hernández-Cano, Alexander Hägele, Allen Hao Huang, Angelika Romanou, Antoni-Joan Solergibert, Barna Pásztor, Bettina Messmer, Dhia Garbaya, Eduard Frank Ďurech, Ido Hakimi, Juan Garcia Giraldo, Mete Ismayilzada, Negar Foroutan, Skander Moalla, Tiancheng Chen, Vinko Sabolčec, Yixuan Xu, Michael Aerni, Badr AlKhamissi, Inés Altemir Marinas, Mohammad Hossein Amani, Matin Ansaripour, Ilia Badanin, Harold Benoit, Emanuela Boros, Nicholas John Browning, Fabian Bösch, Maximilian Böther, Niklas Canova, Camille Challier, Clément Charmillot, Jonathan Coles, Jan Milan Deriu, Arnout Devos, Lukas Drescher, Daniil Dzenhaliou, Maud Ehrmann, Dongyang Fan, Simin Fan, Silin Gao, Miguel Gila, María Grandury, Diba Hashemi, Alexander Miserlis Hoyle, Jiaming Jiang, Mark Klein, Andrei Kucharavy, Anastasiia Kucherenko, Frederike Lübeck, Roman Machacek, Theofilos Ioannis Manitaras, Andreas Marfurt, Kyle Matoba, Simon Matrenok, Henrique Mendonça, Fawzi Roberto Mohamed, Syrielle Montariol, Luca Mouchel, Sven Najem-Meyer, Jingwei Ni, Gennaro Oliva, Matteo Pagliardini, Elia Palme, Andrei Panferov, Léo Paoletti, Marco Passerini, Ivan Pavlov, Auguste Poiroux, Kaustubh Ponkshe, Nathan Ranchin, Javier Rando, Mathieu Sauser, Jakhongir Saydaliev, Mukhammadali Sayfiddinov, Marian Schneider, Stefano Schuppli, Marco Scialanga, Andrei Semenov, Kumar Shridhar, Raghav Singhal, Anna Sotnikova, Alexander Sternfeld, Ayush Kumar Tarun, Paul Teiletche, Jannis Vamvas, Xiaozhe Yao, Hao Zhao, Alexander Ilic, Ana Klimovic, Andreas Krause, Caglar Gulcehre, David Rosenthal, Elliott Ash, Florian Tramèr, Joost VandeVondele, Livio Veraldi, Martin Rajman, Thomas C. Schulthess, Torsten Hoefler, Antoine Bosselut, Martin Jaggi, Imanol Schlag
| Challenge: | Apertus is a fully open suite of large language models (LLMs) designed to address responsibility shortcomings in today’s open model ecosystem, namely data responsibility and global representation. |
| Approach: | They propose to release a fully open suite of large language models (LLMs) that address data responsibility and global representation shortcomings in today’s open model ecosystem. |
| Outcome: | The proposed model is pretrained on openly available data and suppresses verbatim recall of data while retaining task performance. |
MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendation (2025.acl-long)
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Ching-Wen Yang, Zhi-Quan Feng, Ying-Jia Lin, Che Wei Chen, Kun-da Wu, Hao Xu, Yao Jui-Feng, Hung-Yu Kao
| Challenge: | Existing models that generate generic aspects do not provide personalized informative recommendations. |
| Approach: | They propose a model that integrates aspect category as another input dimension to facilitate memorizing fine-grained aspect terms. |
| Outcome: | The proposed model outperforms baseline model on restaurant review datasets in the restaurant domain. |
Sparse Growing Transformer: Training-Time Sparse Depth Allocation via Progressive Attention Looping (2026.findings-acl)
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Yao Chen, Yilong Chen, Yinqi Yang, Junyuan Shang, Zhenyu Zhang, Zefeng Zhang, Shuaiyi Nie, Shuohuan Wang, Yu Sun, Hua Wu, Haifeng Wang, Tingwen Liu
| Challenge: | Existing approaches to increasing effective depth of LLMs rely on parameter reuse, extending computation through recursive execution. |
| Approach: | They propose a training-time sparse depth allocation framework that progressively increases depth for a small subset of parameters as training evolves. |
| Outcome: | The proposed model outperforms existing approaches to increasing the effective depth of language models while reducing training FLOPs overhead from approximately 16–20% to only 1–3% relative to a standard Transformer backbone. |
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. |
Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments (2026.findings-acl)
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Junjie Ye, Changhao Jiang, Zhengyin Du, Yufei Xu, Xuesong Yao, Zhiheng Xi, Xiaoran Fan, Qi Zhang, Tao Gui, Xuanjing Huang, Jiecao Chen
| Challenge: | Currently, there are no efficient reinforcement learning (RL) frameworks specifically designed for tool use. |
| Approach: | They propose an automated environment construction pipeline that incorporates scenario decomposition, document generation, function integration, complexity scaling, and localized deployment to enable high-quality training environments without external tools. |
| Outcome: | The proposed framework significantly improves the models’ tool-use performance without degrading their general capabilities. |
Making RALM Robust to Irrelevant Contexts via Layer Knowledge Guided Attention (2025.findings-acl)
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Weijie Shi, Hao Chen, Jiaming Li, Yao Zhao, Yazhong Zhang, Qijin Chen, Jipeng Zhang, Ruiyuan Zhang, Jia Zhu, Jiajie Xu, Xiaofang Zhou
| Challenge: | Large language models (LLMs) face factual hallucination and knowledge obsolescence when tackling knowledge-intensive tasks. |
| Approach: | They propose a layer-knowledge guided attention method which harnesses the layer-wise knowledge of large language models to optimize per-layer attention on useful passages. |
| Outcome: | The proposed method outperforms existing methods on RALM benchmarks. |
Vocabulary Hijacking in LVLMs: Unveiling Critical Attention Heads by Excluding Inert Tokens to Mitigate Hallucination (2026.acl-long)
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| Challenge: | Large Vision-Language Models (LVLMs) are capable of processing visual inputs, but are susceptible to hallucinations. |
| Approach: | They propose a method to localize and localize specific visual tokens, which are defined as **Inert Tokens**, across layers, revealing a rigid semantic collapse. |
| Outcome: | The proposed approach reduces the likelihood of LVLMs being hijacked by visual inputs while maintaining general capabilities. |
Improving Classifier Robustness through Active Generative Counterfactual Data Augmentation (2023.findings-emnlp)
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| Challenge: | Existing methods for finding meaningful counterfactuals rely on human annotation or implicit label invariance . a small amount of human-annotated counterf actual data can generate a robust dataset with learned labels. |
| Approach: | They propose a framework that generates counterfactuals by actively sampling from regions of uncertainty and automatically labeling them with a learned auxiliary classifier. |
| Outcome: | The proposed framework generates a large number of diverse counterfactuals and labels them with a learned classifier. |
xLAM: A Family of Large Action Models to Empower AI Agent Systems (2025.naacl-long)
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Jianguo Zhang, Tian Lan, Ming Zhu, Zuxin Liu, Thai Quoc Hoang, Shirley Kokane, Weiran Yao, Juntao Tan, Akshara Prabhakar, Haolin Chen, Zhiwei Liu, Yihao Feng, Tulika Manoj Awalgaonkar, Rithesh R N, Zeyuan Chen, Ran Xu, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong
| Challenge: | Autonomous agents powered by large language models (LLMs) have attracted significant research interest, but there are few standards for developing specialized models for agent tasks. |
| Approach: | They propose a series of large action models with dense and mixture-of-expert architectures that unifies, augments, and synthesizes diverse datasets to enhance agent generalizability and performance. |
| Outcome: | The proposed models outperform GPT-4, Claude-3, and many other models in terms of tool use and outperformed GPT-based models on multiple agent ability benchmarks. |
AICA-Bench: Holistically Examining the Capabilities of VLMs in Affective Image Content Analysis (2026.findings-acl)
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| Challenge: | Recent studies have focused on factual correctness, semantic grounding, visual reasoning, or multimodal large language models. |
| Approach: | They propose a benchmark to assess AICA, which integrates perception, reasoning, and generation into a unified framework. |
| Outcome: | The proposed framework corrects intensity errors and significantly enhances descriptive depth. |
Knowledge Mechanisms in Large Language Models: A Survey and Perspective (2024.findings-emnlp)
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Mengru Wang, Yunzhi Yao, Ziwen Xu, Shuofei Qiao, Shumin Deng, Peng Wang, Xiang Chen, Jia-Chen Gu, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang
| Challenge: | Using large language models, we can understand knowledge mechanisms in LLMs for learning, storage, utilization, and evolution. |
| Approach: | They propose to analyze knowledge mechanisms in Large Language Models (LLMs) they examine utilization, evolution, and the potential dark knowledge (hypothesis) they hope to help understand knowledge in LLMs and provide insights for future research . |
| Outcome: | The proposed model can be used to analyze the evolution of parametric knowledge in LLMs. |
VLMInferSlow: Evaluating the Efficiency Robustness of Large Vision-Language Models as a Service (2025.acl-long)
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| Challenge: | Existing studies evaluate efficiency robustness of vision-language models under unrealistic assumptions, requiring access to model architecture and parameters. |
| Approach: | They propose a novel approach to evaluate VLM efficiency robustness in a realistic black-box setting. |
| Outcome: | The proposed approach generates adversarial images with imperceptible perturbations, increasing the computational cost by up to 128.47%. |
EasyEdit2: An Easy-to-use Steering Framework for Editing Large Language Models (2025.emnlp-demos)
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Ziwen Xu, Shuxun Wang, Kewei Xu, Haoming Xu, Mengru Wang, Xinle Deng, Yunzhi Yao, Guozhou Zheng, Huajun Chen, Ningyu Zhang
| Challenge: | Large Language Models (LLMs) have demonstrated extraordinary capabilities, however, they may still generate unreliable or unsafe outputs. |
| Approach: | They propose a framework that allows plug-and-play adjustability for controlling Large Language Model (LLM) behaviors. |
| Outcome: | The framework is designed to enable plug-and-play adjustability for controlling Large Language Model (LLM) behaviors. |
iPET: An Interactive Emotional Companion Dialogue System with LLM-Powered Virtual Pet World Simulation (2025.acl-demo)
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Zheyong Xie, Shaosheng Cao, Zuozhu Liu, Zheyu Ye, Zihan Niu, Chonggang Lu, Tong Xu, Enhong Chen, Zhe Xu, Yao Hu, Wei Lu
| Challenge: | Existing approaches to role-playing emotional companion products lack sustained personalization and contextual adaptability, limiting their effectiveness in real-world settings. |
| Approach: | They propose a virtual pet agent that can enhance user engagement through rich, dynamic pet behaviors and interactions tailored to individual preferences. |
| Outcome: | The proposed system has been deployed in a real-world, non-commercial product for 200 days and has demonstrated its effectiveness in practical applications. |
From Verbatim to Gist: Distilling Pyramidal Multimodal Memory via Semantic Information Bottleneck for Long-Horizon Video Agents (2026.acl-long)
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| Challenge: | Existing multimodal large language models struggle with long-horizon video understanding due to limited context windows and static memory mechanisms that fail to mirror human cognitive efficiency. |
| Approach: | They propose a pyramidal multimodal memory architecture grounded in Fuzzy-Trace Theory that structures memory hierarchically into a *Sensory Buffer*, *Episodic Stream*, and *Symbolic Schema*. |
| Outcome: | The proposed architecture achieves state-of-the-art on both offline and streaming tasks, demonstrating robust generalization and validating the effectiveness of cognition-inspired memory organization. |
ToolHop: A Query-Driven Benchmark for Evaluating Large Language Models in Multi-Hop Tool Use (2025.acl-long)
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Junjie Ye, Zhengyin Du, Xuesong Yao, Weijian Lin, Yufei Xu, Zehui Chen, Zaiyuan Wang, Sining Zhu, Zhiheng Xi, Siyu Yuan, Tao Gui, Qi Zhang, Xuanjing Huang, Jiecao Chen
| Challenge: | Effective evaluation of multi-hop tool use is critical for analyzing the understanding, reasoning, and function-calling capabilities of large language models. |
| Approach: | They propose a dataset that provides rigorous evaluation of multi-hop tool use. |
| Outcome: | The proposed model achieves 49.04% accuracy across five model families. |
CAIR: Causal Adaptive Information-based Reinforcement Learning for Multimodal Emotion Reasoning (2026.findings-acl)
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| Challenge: | Existing methods for multimodal emotion reasoning produce fluent but superficial explanations that lack authentic logical derivation. |
| Approach: | They propose a framework that treats rationales as causal mediators between raw perceptual signals and emotional semantics and an adaptive optimization mechanism to balance perception and reasoning across varying cognitive loads. |
| Outcome: | The proposed framework outperforms specialized SFT models by 14.4% while enhancing rationale faithfulness. |
Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement (2025.findings-naacl)
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Xiyao Wang, Jiuhai Chen, Zhaoyang Wang, Yuhang Zhou, Yiyang Zhou, Huaxiu Yao, Tianyi Zhou, Tom Goldstein, Parminder Bhatia, Taha Kass-Hout, Furong Huang, Cao Xiao
| Challenge: | Existing methods for visual and language alignment depend on external models or data, leading to uncontrollable and unstable results. |
| Approach: | They propose a framework that enhances visual and language alignment without external dependencies by incorporating an in-context self-critic mechanism that constructs preference pairs for tuning. |
| Outcome: | The proposed framework outperforms existing methods and improves performance on 14 hallucination and comprehensive benchmarks. |
nvAgent: Automated Data Visualization from Natural Language via Collaborative Agent Workflow (2025.acl-long)
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| Challenge: | *Natural Language to Visualization (NL2Vis) seeks to transform natural-language descriptions into visual representations of given tables. |
| Approach: | They propose a collaborative agent workflow for NL2Vis that incorporates three agents . the model is called **nvAgent** and comprises a processor agent for database processing and context filtering, a composer agent for planning visualization generation and a validator agent for code translation and output verification. |
| Outcome: | The proposed workflow surpasses state-of-the-art models on the VisEval benchmark. |
SimCSE: Simple Contrastive Learning of Sentence Embeddings (2021.emnlp-main)
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| Challenge: | Existing methods for learning universal sentence embeddings are based on unsupervised approaches with only dropout as noise. |
| Approach: | They propose an unsupervised approach that takes an input sentence and predicts itself in a contrastive objective with only standard dropout used as noise. |
| Outcome: | The proposed framework performs on par with previous supervised approaches and can produce superior sentence embeddings from unlabeled or labeled data. |
BatonVoice: An Operationalist Framework for Enhancing Controllable Speech Synthesis with Linguistic Intelligence from LLMs (2026.acl-long)
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Yue Wang, Ruotian Ma, Xingyu Chen, Zhengliang Shi, Morunliu Yang, Wanshun Chen, Huang Liu, Jiadi Yao, Xin He, Qu Yang, Qingxuan Jiang, Fanghua Ye, Juntao Li, Zhaopeng Tu, Xiaolong Li, Liefeng Bo, Min Zhang
| Challenge: | Existing approaches often fail to leverage the linguistic intelligence of Large Language Models (LLMs) Existing models lack the ability to follow text instructions for controllable Text-to-Speech (TTS). |
| Approach: | They propose a framework where an LLM acts as a conductor, understanding user instructions and generating a textual plan - explicit vocal features. |
| Outcome: | The proposed model outperforms open- and closed-source models in speech synthesis and achieves zero-shot cross-lingual generalization. |
Why Steering Works: Toward a Unified View of Language Model Parameter Dynamics (2026.acl-long)
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Ziwen Xu, Chenyan WU, Hengyu Sun, Haiwen Hong, Mengru Wang, Yunzhi Yao, Longtao Huang, Hui Xue, Shumin Deng, Zhixuan Chu, Huajun Chen, Ningyu Zhang
| Challenge: | Methods for controlling large language models (LLMs) are often studied in isolation, obscuring connections and making comparison difficult. |
| Approach: | They propose a preference-utility analysis that separates control effects into preference and utility, and measures both on a shared log-odds scale using polarity-paired contrastive examples. |
| Outcome: | The proposed approach improves preference while preserving utility. |
Improving Reasoning Capabilities in Small Models through Mixture-of-layers Distillation with Stepwise Attention on Key Information (2025.emnlp-main)
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| Challenge: | Existing methods focus on transferring teacher-generated rationales to student models, but do not explore teachers’ dynamic attention towards critical information during reasoning. |
| Approach: | They propose a method that transfers the teacher’s stepwise attention on key information to the student model and a Mixture of Layers module that allows dynamic alignment between the teacher and student. |
| Outcome: | The proposed framework achieves consistent performance improvements across multiple mathematical and commonsense reasoning datasets. |
Towards a Design Guideline for RPA Evaluation: A Survey of Large Language Model-Based Role-Playing Agents (2025.findings-acl)
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| Challenge: | Role-Playing Agents (RPAs) are increasingly popular due to diverse task requirements and agent designs. |
| Approach: | They propose an evidence-based evaluation design guideline for LLM-based RPAs based on agent attributes, task attributes, and evaluation metrics. |
| Outcome: | The proposed evaluation design guideline is based on a systematic review of 1,676 papers published between Jan. 2021 and Dec. 2024. |
CGBridge: Bridging Code Graphs and Large Language Models for Better Structure-Aware Code Understanding (2026.findings-acl)
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| Challenge: | Existing structure-aware approaches treat structure as serialized text prompts or auxiliary training objectives, failing to provide explicit guidance during inference. |
| Approach: | They propose a plug-and-play method that enhances Large Language Models with Code Graph information through an external, trainable Bridge module. |
| Outcome: | The proposed method decouples structural reasoning from textual generation without updating the backbone. |
Search-in-Context: Efficient Multi-Hop QA over Long Contexts via Monte Carlo Tree Search with Dynamic KV Retrieval (2025.findings-acl)
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| Challenge: | Existing approaches to multihop question answering (MHQA) over long contexts are often neglecting explicit reasoning or incurring expensive computational costs due to full-attention mechanisms over long contextuals. |
| Approach: | They propose a framework that integrates Monte Carlo Tree Search (MCTS) with dynamic key-value retrieval to enable iterative, context-aware reasoning. |
| Outcome: | The proposed framework integrates Monte Carlo Tree Search (MCTS) with dynamic key-value (KV) retrieval to enable iterative, context-aware reasoning. |
Position Paper: Data-Centric AI in the Age of Large Language Models (2024.findings-emnlp)
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Xinyi Xu, Zhaoxuan Wu, Rui Qiao, Arun Verma, Yao Shu, Jingtan Wang, Xinyuan Niu, Zhenfeng He, Jiangwei Chen, Zijian Zhou, Gregory Kang Ruey Lau, Hieu Dao, Lucas Agussurja, Rachael Hwee Ling Sim, Xiaoqiang Lin, Wenyang Hu, Zhongxiang Dai, Pang Wei Koh, Bryan Kian Hsiang Low
| Challenge: | a paper proposes a data-centric perspective of AI research, focusing on large language models. |
| Approach: | They propose a data-centric viewpoint of AI research, focusing on large language models . they propose four scenarios centered around data, including data curation, attribution, knowledge transfer . |
| Outcome: | The proposed research focuses on large language models with data centric benchmarks . the proposed benchmarks can be used to develop new data curation methods . |
DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation (2022.acl-long)
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Wei Chen, Yeyun Gong, Song Wang, Bolun Yao, Weizhen Qi, Zhongyu Wei, Xiaowu Hu, Bartuer Zhou, Yi Mao, Weizhu Chen, Biao Cheng, Nan Duan
| Challenge: | Existing pre-trained dialog models shed light on various downstream tasks in natural language processing (NLP). |
| Approach: | They propose a dialog pre-training framework that introduces latent variables into the enhanced encoder-decoder pre-train framework to increase relevance and diversity of responses. |
| Outcome: | The proposed model achieves state-of-the-art on personaChat, DailyDialog, and DSTC7-AVSD datasets. |
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. |
Text-Attributed Knowledge Graph Enrichment with Large Language Models for Medical Concept Representation (2026.acl-long)
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| Challenge: | eHRs encode a patient's medical history as a high-dimensional and sparse sequence of diagnosis, medication, and procedure concepts . robust concept representation learning is hindered by key challenges, authors say . clinically important cross-type dependencies are often missing or incomplete in existing ontology resources . |
| Approach: | They propose a graph learning framework that integrates semantics with medical concepts to improve prediction performance. |
| Outcome: | The proposed framework improves prediction performance and integrates semantics with graph structure. |