Papers by Chen Wei
RETAIL: Towards Real-world Travel Planning for Large Language Models (2025.emnlp-main)
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| Challenge: | Existing travel planning systems assume users provide explicit queries, limiting their practical utility. |
| Approach: | They propose a dataset RETAIL which supports decision-making for implicit queries while covering explicit queries. |
| Outcome: | The proposed model achieves a 1.0% pass rate, suggesting real-world travel planning remains challenging. |
Say What You Mean! Large Language Models Speak Too Positively about Negative Commonsense Knowledge (2023.acl-long)
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| Challenge: | Large language models (LLMs) have been studied for their ability to store and utilize positive knowledge. |
| Approach: | They propose to use a constrained keywords-to-sentence generation task and a Boolean question answering task to probe large language models on negative commonsense knowledge. |
| Outcome: | The proposed tasks show that LLMs fail to generate valid sentences grounded in negative commonsense knowledge, yet they can correctly answer yes-or-no questions. |
We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning? (2025.acl-long)
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Runqi Qiao, Qiuna Tan, Guanting Dong, MinhuiWu MinhuiWu, Chong Sun, Xiaoshuai Song, Jiapeng Wang, Zhuoma GongQue, Shanglin Lei, YiFan Zhang, Zhe Wei, Miaoxuan Zhang, Runfeng Qiao, Xiao Zong, Yida Xu, Peiqing Yang, Zhimin Bao, Muxi Diao, Chen Li, Honggang Zhang
| Challenge: | Existing benchmarks focus more on end-to-end performance, but neglect the underlying principles of knowledge acquisition and generalization. |
| Approach: | They propose a benchmark specifically designed to explore the problem-solving principles by decomposing 6.5K visual math problems into 10.9K step-level questions for evaluation. |
| Outcome: | The proposed benchmark covers 6.5K visual math problems and 10.9K step-level questions spanning 5 layers of knowledge granularity and 67 hierarchical knowledge concepts. |
Set Generation Networks for End-to-End Knowledge Base Population (2021.emnlp-main)
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| Challenge: | Existing knowledge base population systems require a machine translation task to generate multiple facts, but the fact order is not considered. |
| Approach: | They propose a knowledge base population task that aims to discover facts about entities from texts and expand a KB with these facts. |
| Outcome: | The proposed networks achieve state-of-the-art (SoTA) performance on two benchmark datasets. |
WavLLM: Towards Robust and Adaptive Speech Large Language Model (2024.findings-emnlp)
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Shujie Hu, Long Zhou, Shujie Liu, Sanyuan Chen, Lingwei Meng, Hongkun Hao, Jing Pan, Xunying Liu, Jinyu Li, Sunit Sivasankaran, Linquan Liu, Furu Wei
| Challenge: | Recent advances in large language models (LLMs) have expanded their scope to encompass multimodal functions. |
| Approach: | They propose a robust and adaptive speech large language model with dual encoders . they validate the model on universal speech benchmarks and apply it to specialized speech-question-answer datasets based on a CoT approach . |
| Outcome: | The proposed model achieves state-of-the-art performance across a range of speech tasks on the same model size. |
Attention as Selector: Unlocking VLM Attention for Long Document Page Retrieval (2026.acl-long)
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| Challenge: | Existing page-level retrieval methods lack query–page interaction before similarity scoring . Existing methods require large-scale datasets to align visual and textual embeddings . |
| Approach: | They propose a retrieval framework that utilizes attention mechanisms inside VLMs for page selection. |
| Outcome: | The proposed retrieval framework outperforms embedding-based retrieval methods on four long-document benchmarks. |
A Survey of Large Language Models in Psychotherapy: Current Landscape and Future Directions (2025.findings-acl)
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Hongbin Na, Yining Hua, Zimu Wang, Tao Shen, Beibei Yu, Lilin Wang, Wei Wang, John Torous, Ling Chen
| Challenge: | Large language models (LLMs) can handle extensive context and multi-turn reasoning. |
| Approach: | They propose a taxonomy dividing psychotherapy into stages of assessment, diagnosis, and treatment to examine LLM advancements and challenges. |
| Outcome: | The proposed taxonomy reveals imbalances in current research, such as a focus on common disorders, linguistic biases, fragmented methods, and limited theoretical integration. |
Multi-Source Cross-Lingual Model Transfer: Learning What to Share (P19-1)
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| Challenge: | Cross-lingual transfer learning (CLTL) is a viable method for building NLP models for a low-resource target language . however, many languages lack the labeled training data necessary for training deep neural nets for varying NLP tasks. |
| Approach: | They propose a cross-lingual transfer learning method that leverages annotated data from other languages to build NLP models for a target language. |
| Outcome: | The proposed model achieves significant performance gains over prior art over multiple text classification and sequence tagging tasks including a large-scale industry dataset. |
Full-Step-DPO: Self-Supervised Preference Optimization with Step-wise Rewards for Mathematical Reasoning (2025.findings-acl)
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| Challenge: | Existing approaches to improve long-chain mathematical reasoning focus on the first erroneous step, but ignore all other steps and rely heavily on external signals. |
| Approach: | They propose a DPO framework that leverages step-wise rewards from the entire reasoning chain instead of optimizing only the first erroneous step. |
| Outcome: | The proposed framework improves on in-domain and out-of-domain mathematical reasoning benchmarks. |
Unsupervised Neural Machine Translation with Weight Sharing (P18-1)
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| Challenge: | Unsupervised neural machine translation (NMT) is a new approach for machine translation . the model uses only one shared encoder to map pairs of sentences from different languages to a shared-latent space . |
| Approach: | They propose an unsupervised approach which trains the model without labeling data . they propose two independent encoders but share some partial weights to extract high-level representations of input sentences. |
| Outcome: | The proposed approach achieves significant improvements on English-German, English-French and Chinese-to-English translation tasks. |
Penetrating Linguistic Disguises: A Slang-aware Label-Aligned Framework for Fine-Grained Toxicity Extraction in Chinese Hate Speech Detection (2026.findings-acl)
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| Challenge: | Flexible word boundaries and linguistic obfuscation, particularly slang, challenge precise span-level hate speech detection in Chinese. |
| Approach: | They propose a Slang-aware Label-Aligned Framework that maps slang to explicit hate semantics and uses task-specific branches to mitigate feature interference. |
| Outcome: | The proposed framework reduces ambiguity by mapping obscure slang to explicit hate semantics. |
CollabKG: A Learnable Human-Machine-Cooperative Information Extraction Toolkit for (Event) Knowledge Graph Construction (2024.lrec-main)
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| Challenge: | Existing IE tools lack multi-task support and automatic updates for KG and EKG construction. |
| Approach: | They propose a human-machine-cooperative IE toolkit for KG and EKG construction that unifies different IE subtasks and integrates LLMs as the assistant machine. |
| Outcome: | The proposed tool improves annotation quality, efficiency, and stability simultaneously. |
Detecting Conversational Mental Manipulation with Intent-Aware Prompting (2025.coling-main)
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| Challenge: | Existing approaches to detect mental manipulations are limited due to complexity of detecting subtle, covert tactics in conversations. |
| Approach: | They propose an approach to detect mental manipulations using large language models using intent-aware prompting by capturing the intents of participants. |
| Outcome: | The proposed approach significantly reduces false negatives, helping detect more instances of mental manipulation with minimal misjudgment of positive cases. |
Autoregressive Speech Synthesis without Vector Quantization (2025.acl-long)
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Lingwei Meng, Long Zhou, Shujie Liu, Sanyuan Chen, Bing Han, Shujie Hu, Yanqing Liu, Jinyu Li, Sheng Zhao, Xixin Wu, Helen M. Meng, Furu Wei
| Challenge: | MELLE is a novel language modeling approach for text-to-speech synthesis that generates continuous tokens from text . authors demonstrate that it reduces the need for vector quantization and improves model robustness . |
| Approach: | They propose to autoregressively generate continuous mel-spectrogram frames directly from text condition, bypassing vector quantization. |
| Outcome: | The proposed model achieves superior performance across multiple metrics and is more streamlined. |
Better Process Supervision with Bi-directional Rewarding Signals (2025.findings-acl)
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Wenxiang Chen, Wei He, Zhiheng Xi, Honglin Guo, Boyang Hong, Jiazheng Zhang, Nijun Li, Tao Gui, Yun Li, Qi Zhang, Xuanjing Huang
| Challenge: | Existing processes that reward for each step are one-directional and lack a mechanism to model the distance to the final target. |
| Approach: | They propose a process supervision model that evaluates the correctness of previous steps and the probability of future success. |
| Outcome: | The proposed model outperforms existing supervision models like ORM and PRM on reasoning tasks and improves solution re-design. |
When Evolution Strategy Meets Language Models Tuning (2025.coling-main)
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| Challenge: | Autoregressive language models with pretraining often display limited capability in effectively following instructions. |
| Approach: | They propose an on-policy approach to optimize models by harnessing the principle of biological evolution, namely survival of the fittest. |
| Outcome: | The proposed method can achieve superior performance in various tasks and comparable performance in the human alignment task. |
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. |
PricingLogic: Evaluating LLMs Reasoning on Complex Tourism Pricing Tasks (2025.emnlp-main)
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Yunuo Liu, Dawei Zhu, Zena Al-Khalili, Dai Cheng, Yanjun Chen, Dietrich Klakow, Wei Zhang, Xiaoyu Shen
| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains, such as code generation, mathematical problem-solving, and general-purpose human instruction following. |
| Approach: | They propose to use large language models to process questions expressed in natural language to automate tourism-booking prices when multiple, overlapping farerules apply. |
| Outcome: | The proposed model can automate tourism-booking prices when multiple, overlapping farerules apply. |
USB: A COMPREHENSIVE AND UNIFIED SAFETY EVALUATION BENCHMARK FOR MULTIMODAL LARGE LANGUAGE MODELS (2026.acl-long)
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Baolin Zheng, Guanlin Chen, Qingyang Teng, Hongqiong Zhong, Yingshui Tan, Zhendong Liu, Weixun Wang, Jiaheng Liu, Jian Yang, Huiyun Jing, Jincheng Wei, Wenbo Su, Xiaoyong Zhu, Bo Zheng, Kaifu Zhang
| Challenge: | Existing safety benchmarks fail to provide reliable assessments due to limited risk coverage, insufficient scale and the oversight of complex modality combinations. |
| Approach: | They propose a framework that covers 61 risk categories across four modality interactions to address this gap. |
| Outcome: | The proposed framework covers 61 risk categories across four distinct modality interactions. |
Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation (2026.acl-long)
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| Challenge: | Large Language Models lack specific task alignment and large-scale simulations are challenging due to their ambiguity, noise and massive volume. |
| Approach: | They propose a framework that leverages user feedback in RSs with advanced LLM capabilities to generate high-quality simulation data. |
| Outcome: | The proposed framework boosts the alignment with human preferences and in-domain reasoning capabilities of the fine-tuned LLMs. |
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)
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Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin
| Challenge: | a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities . |
| Approach: | They present a comparative analysis to identify and distinguish LLM activities from human activities. |
| Outcome: | The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities. |
Counterfactual Supporting Facts Extraction for Explainable Medical Record Based Diagnosis with Graph Network (2021.naacl-main)
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| Challenge: | Existing methods provide explanations based on a precise medical knowledge base, which is disease-specific and difficult to obtain for experts in reality. |
| Approach: | They propose a method to extract supporting facts from irregular EMR without external knowledge bases by constructing a hierarchical graph network and using it to obtain causal relationship between multi-granularity features and diagnosis results. |
| Outcome: | The proposed method diagnoses four types of EMR correctly and provides accurate supporting facts for the results. |
Symbol tuning improves in-context learning in language models (2023.emnlp-main)
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Jerry Wei, Le Hou, Andrew Lampinen, Xiangning Chen, Da Huang, Yi Tay, Xinyun Chen, Yifeng Lu, Denny Zhou, Tengyu Ma, Quoc Le
| Challenge: | Language models are sensitive to the way that prompts are given, indicating that they are not reasoning in a robust manner. |
| Approach: | They propose to fine tune language models on in-context input-label pairs where natural language labels are replaced with arbitrary symbols. |
| Outcome: | The proposed model is much stronger at reasoning tasks and more robust to underspecified prompts than the standard model. |
Learning In-context Learning for Named Entity Recognition (2023.acl-long)
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Jiawei Chen, Yaojie Lu, Hongyu Lin, Jie Lou, Wei Jia, Dai Dai, Hua Wu, Boxi Cao, Xianpei Han, Le Sun
| Challenge: | Existing methods to recognize entities in text are limited by the diversity of entity types and the lack of high-quality annotations. |
| Approach: | They propose an in-context learning-based NER approach that can inject in-const NER ability into PLMs and recognize entities of novel types on-the-fly using only a few demonstrative instances. |
| Outcome: | The proposed method outperforms the PLMs+fine-tuning counterparts on 4 few-shot NER datasets and significantly outperformed the Plms+initialized extractors. |
CPT-Agent: A Cognitive Process Theory-driven Framework for Student Simulation in Writing Development (2026.acl-long)
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| Challenge: | Existing LLMs model overly capable learners who over-apply feedback, resulting in pedagogically implausible behavior. |
| Approach: | They propose a framework that decouples cognitive ability from writing proficiency and models their interaction during writing and revision. |
| Outcome: | The proposed model produces distinguishable proficiency levels and is consistent with instructional theories. |
To Diff or Not to Diff? Structure-Aware and Adaptive Output Formats for Efficient LLM-based Code Editing (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are increasingly used for code editing, yet the full-code generation paradigm suffers from severe efficiency bottlenecks. |
| Approach: | They propose to use a structure-aware diff format to train LLMs to choose the most token-efficient format between a given diff format and full code. |
| Outcome: | The proposed approach matches the most token-efficient format with full-code generation while reducing latency and cost by over 30% on long-code editing tasks. |
What Makes AI Research Replicable? Executable Knowledge Graphs as Scientific Knowledge Representations (2026.acl-short)
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Yujie Luo, Zhuoyun Yu, Xuehai Wang, Yuqi Zhu, Ningyu Zhang, Lanning Wei, Lun Du, Da Zheng, Huajun Chen
| Challenge: | Existing approaches to replicate AI research are limited by insufficient background knowledge and the limitations of retrieval-augmented generation methods. |
| Approach: | They propose a pluggable, paper-centric knowledge base that integrates code snippets and technical insights extracted from scientific literature into a verifiable, executable representation. |
| Outcome: | The proposed knowledge base shows significant performance gains on paperBench when integrated into three agent frameworks with two different LLMs. |
EdgeFormer: A Parameter-Efficient Transformer for On-Device Seq2seq Generation (2022.emnlp-main)
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| Challenge: | Extensive experiments show EdgeFormer can effectively outperform previous parameter-efficient Transformer baselines and achieve competitive results under both the computation and memory constraints. |
| Approach: | They propose a parameter-efficient Transformer for on-device seq2seq generation that uses two novel principles for cost-effective parameterization. |
| Outcome: | Extensive experiments show that EdgeFormer outperforms the previous parameter-efficient Transformers and achieves competitive results under both the computation and memory constraints. |
Aligning Large Language Models via Fully Self-Synthetic Data (2026.acl-long)
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| Challenge: | Existing approaches to reinforcement learning from human feedback (RLHF) require expensive human-annotated datasets and proprietary models like GPT-4 to annotate preference pairs. |
| Approach: | They propose a self-synthetic framework for LLM alignment where all training data, including prompts (i.e., user queries), responses, and preferences, are generated by the model itself. |
| Outcome: | The proposed framework enhances the model’s chat capabilities on standard benchmarks like AlpacaEval 2.0 while maintaining strong performance on downstream objective tasks. |
SA-DETR:Span Aware Detection Transformer for Moment Retrieval (2025.coling-main)
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| Challenge: | Moment Retrieval aims to locate video segments related to text. |
| Approach: | They propose a method that leverages the importance of instance related span anchors . they initialize span anchor using instance related fuse token and supervise them with GT labels . |
| Outcome: | The proposed method achieves competitive results on QVHighlights, Charades-STA and TACoS. |
Emergent Relational Order in LLM Agent Societies: From Collective Affect to Authority Stratification (2026.findings-acl)
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| Challenge: | Fei Xiaotong’s Differential Order Pattern characterizes rural society as egocentric and relationally graded, with cooperation attenuating over social distance. |
| Approach: | They propose a multi-agent framework grounded in Affect Control Theory, Social Identity Theory, and Durkheimian collective affect. |
| Outcome: | Extensive simulations support interpreting Differential Order as a structure-sensitive emergent outcome of general social mechanisms. |
I-MCTS: Enhancing Agentic AutoML via Introspective Monte Carlo Tree Search (2026.findings-eacl)
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| Challenge: | Existing LLM-based agents struggle with low diversity and suboptimal code generation. |
| Approach: | They propose an approach that iteratively expands tree nodes through an introspective process that meticulously analyzes solutions and results from parent and sibling nodes. |
| Outcome: | The proposed approach shows a 4% improvement in performance compared to the strong open-source AutoML agents. |
HalluAudio: A Comprehensive Benchmark for Hallucination Detection in Large Audio-Language Models (2026.acl-long)
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| Challenge: | Existing studies on hallucination focus on text or vision, while few audio-oriented studies are limited in scale, modality coverage, and diagnostic depth. |
| Approach: | They propose a large-scale benchmark for evaluating hallucinations across speech, sound, and music. |
| Outcome: | The proposed model improves hallucination rate, yes/no bias, error-type analysis, and refusal rate. |
Knowing the No-match: Entity Alignment with Dangling Cases (2021.acl-long)
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| Challenge: | Existing approaches to find entities that cannot find alignment across knowledge graphs (KGs) despite their importance, knowledge graph is expensive and suffers from incompleteness. |
| Approach: | They propose a framework for entity alignment and dangling entity detection that can be used to abstain from predicting alignment for detected dangle entities. |
| Outcome: | The proposed framework can abstain from predicting alignment for detected dangling entities. |
Zero-Shot Cross-Lingual Transfer of Neural Machine Translation with Multilingual Pretrained Encoders (2021.emnlp-main)
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| Challenge: | Existing work on improving cross-lingual transferability of NMT model is under-explored. |
| Approach: | They propose a model that leverages a multilingual pretrained encoder to improve cross-lingual transferability. |
| Outcome: | The proposed model outperforms mBART and m2m-100 on a zero-shot cross-lingual transfer task. |
Towards Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning (2020.coling-main)
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| Challenge: | Chinese word segmentation datasets have ambiguous annotation criteria resulting in multi-grained compounds. |
| Approach: | They propose a domain adaptive segmenter to exploit diverse annotation criteria of datasets . they use bidirectional encoder representations from transformers to introduce open-domain knowledge . |
| Outcome: | The proposed model outperforms the state-of-the-art models on 10 Chinese word datasets with superior efficiency. |
Don’t Reinvent the Wheel: Efficient Instruction-Following Text Embedding based on Guided Space Transformation (2025.acl-long)
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| Challenge: | Existing methods for text embedding require re-encoding the entire corpus for each instruction. |
| Approach: | They propose a framework that generates dynamic text embeddings that adapt to user instructions, highlighting specific attributes of text. |
| Outcome: | The proposed framework improves instruction-following text embedding quality over state-of-the-art methods while speeding up processing on large datasets. |
Expectation Confirmation Preference Optimization for Multi-Turn Conversational Recommendation Agent (2025.findings-acl)
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Xueyang Feng, Jingsen Zhang, Jiakai Tang, Wei Li, Guohao Cai, Xu Chen, Quanyu Dai, Yue Zhu, Zhenhua Dong
| Challenge: | Recent advances in Large Language Models (LLMs) have propelled the development of Conversational Recommendation Agents (CRAs). |
| Approach: | They propose a multi-turn preference optimization paradigm that leverages Expectation Confirmation Theory to explicitly model the evolution of user satisfaction throughout multi-turned dialogues. |
| Outcome: | The proposed paradigm eliminates the significant sampling overhead of existing MTPO methods while ensuring the optimization process drives meaningful improvements. |
Learning from Contrasts: Synthesizing Reasoning Paths from Diverse Search Trajectories (2026.acl-long)
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| Challenge: | MCTS methods retain only the single highest-reward trajectory, discarding comparative signals present in the many explored paths. |
| Approach: | They propose a framework that transforms supervision extraction into a synthesis procedure. |
| Outcome: | The proposed framework matches or exceeds baselines on 60K CRPS-synthesized examples on out-of-domain benchmarks. |
From Lists to Emojis: How Format Bias Affects Model Alignment (2025.acl-long)
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| Challenge: | Format biases in reinforcement learning from human feedback are underexplored . despite its effectiveness, RLHF faces challenges, including policy and regulatory constraints . |
| Approach: | They extend the study of preference biases beyond verbosity bias to a wider range of format biase . they show that with a small amount of biased data, they can inject significant bias into the reward model . |
| Outcome: | The proposed approach can be easily exploited by large language models to achieve higher rankings on popular benchmarks like AlpacaEval and LMSYS Chatbot Arena. |
SEGMENT+: Long Text Processing with Short-Context Language Models (2024.emnlp-main)
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Wei Shi, Shuang Li, Kerun Yu, Jinglei Chen, Zujie Liang, Xinhui Wu, Yuxi Qian, Feng Wei, Bo Zheng, Jiaqing Liang, Jiangjie Chen, Yanghua Xiao
| Challenge: | Existing frameworks that increase context window do not guarantee robust performance across long input tasks. |
| Approach: | They propose a framework that enables language models to handle extended inputs within limited context windows efficiently. |
| Outcome: | The framework improves performance on long-document question-answering and Needle-in-a-Haystack tasks. |
Guided Knowledge Generation with Language Models for Commonsense Reasoning (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have achieved notable success in commonsense reasoning tasks, benefiting from extensive world knowledge acquired through extensive pretraining. |
| Approach: | They propose a method to generate knowledge explanations and to automatically assign labels based on the probability of correct answers. |
| Outcome: | The proposed method outperforms baselines on four widely-used commonsense reasoning benchmarks and shows that it can generate high quality knowledge leading to correct answers. |
G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks (2022.emnlp-main)
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| Challenge: | Existing domain-adaptive pre-training (DAPT) models tend to forget the general knowledge acquired by general PLMs, leading to catastrophic forgetting and sub-optimal performance. |
| Approach: | They propose a framework which augments the domain-specific PLM by a memory built from the frozen general PLM without losing the general knowledge. |
| Outcome: | The proposed framework augments the domain-specific PLM by a memory built from the frozen general PLM without losing the general knowledge. |
Retrieval and Reasoning on KGs: Integrate Knowledge Graphs into Large Language Models for Complex Question Answering (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have performed impressively in various NLP tasks, but their inherent hallucination phenomena severely challenge their credibility in complex reasoning. |
| Approach: | They propose to integrate explainable Knowledge Graphs (KGs) with LLMs to alleviate hallucinations . they construct subgraphs to enhance the retrieval capabilities of KGs via CoT reasoning. |
| Outcome: | Extensive experiments on two KGQA datasets show that the proposed model achieves convincing performance compared to strong baselines. |
Mixture of Ordered Scoring Experts for Cross-prompt Essay Trait Scoring (2025.acl-long)
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| Challenge: | Existing approaches to automate essay scoring overlook critical information, authors say . evaluators often limit their performance to unseen topics, resulting in incomplete assessment perspectives. |
| Approach: | They propose a framework that integrates information from prompts and essays into an AES framework. |
| Outcome: | The proposed framework achieves state-of-the-art in cross-prompt scoring and multi-trait scoring on the ASAP++ dataset. |
BSFA: Leveraging the Subspace Dichotomy to Accelerate Neural Network Training (2025.emnlp-main)
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| Challenge: | Recent studies highlight a fundamental dichotomy in deep learning optimization: parameter updates along the top eigendirections of the loss Hessian (Dom-space) capture most of the update magnitude, while updates in the orthogonal component (Bulk-space), have smaller magnitudes but drive most learning progress. |
| Approach: | They propose a plug-and-play framework that scales update components projected onto distinct subspaces and a block-wise strategy that applies this estimation on a per-parameter-block basis. |
| Outcome: | The proposed framework accelerates training by differentially scaling update components projected onto distinct subspaces, while enhancing stability by moderating updates in dominant subspace and boosting convergence speed by amplifying updates in bulk-space. |
MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction (2022.findings-acl)
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| Challenge: | Keyphrase extraction (KPE) extracts phrases in a document that provide a concise summary of the core content. |
| Approach: | They propose an unsupervised keyphrase extraction method that ranks candidates by similarity between embeddings of source document and masked document. |
| Outcome: | The proposed method outperforms state-of-the-art methods on six benchmarks . it achieves average 3.53 improvement over the existing method . |
XDoc: Unified Pre-training for Cross-Format Document Understanding (2022.findings-emnlp)
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| Challenge: | Existing pre-trained models target one document format at a time, making it difficult to combine knowledge from multiple document formats. |
| Approach: | They propose a unified pre-trained model which deals with different document formats in a single model. |
| Outcome: | The proposed model achieves comparable or even better performance on a variety of downstream tasks compared with the individual pre-trained models. |
IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web (2025.findings-acl)
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Hongcheng Guo, Wei Zhang, Junhao Chen, Yaonan Gu, Jian Yang, Junjia Du, Shaosheng Cao, Binyuan Hui, Tianyu Liu, Jianxin Ma, Chang Zhou, Zhoujun Li
| Challenge: | Existing models have been introduced to improve image comprehension, but there is no robust benchmark for imagetoweb conversion. |
| Approach: | They propose a benchmark to assess imagetoweb conversion proficiency of large multimodal models . they propose to measure layout information of web pages by parsing the Document Object Model tree . |
| Outcome: | The proposed benchmark measures the layout information of web pages—i.e., the positional relationships between elements—which has been overlooked by prior work. |
Towards Boosting LLMs-driven Relevance Modeling with Progressive Retrieved Behavior-augmented Prompting (2025.coling-industry)
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| Challenge: | Existing approaches to relevance modeling have lacked generalization and accuracy . recent studies have focused on capturing the semantic relationships between queries and items . |
| Approach: | They propose a framework that integrates world knowledge stored in LLMs with specialized domain knowledge represented by user behavior data for promising performance. |
| Outcome: | The proposed framework can handle full-scale search traffics of Alipay with acceptable cost and latency. |
IterAlign: Iterative Constitutional Alignment of Large Language Models (2024.naacl-long)
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| Challenge: | Empirical results show that iterAlign improves truthfulness, helpfulness, harmlessness and honesty, improving the LLM alignment by up to 13.5% in harmlessness. |
| Approach: | They propose a data-driven constitution discovery and self-alignment framework called IterAlign to overcome these drawbacks by leveraging red teaming to uncover weaknesses of an LLM. |
| Outcome: | Empirical results show that iterAlign improves truthfulness, helpfulness, harmlessness and honesty by up to 13.5%. |
How Do Answer Tokens Read Reasoning Traces? Self-Reading Patterns in Thinking LLMs for Quantitative Reasoning (2026.findings-acl)
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| Challenge: | Prior work on activation steering has focused on shaping reasoning traces, but it remains unclear how answer tokens actually read and integrate the reasoning to produce reliable outcomes. |
| Approach: | They propose a training-free steering method that uses self-reading quality scores to guide inference toward benign self-readiness and away from uncertain and disorganized reading. |
| Outcome: | The proposed method yields consistent accuracy gains in the reasoning traces generated by thinking LLMs. |
A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery (2024.emnlp-main)
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| Challenge: | Existing surveys on scientific LLMs focus on one or two fields or a single modality. |
| Approach: | They survey 260 scientific LLMs and examine their architectures and pre-training techniques . they also discuss commonalities and differences between LLM architectures . |
| Outcome: | The proposed model architectures and evaluation techniques are used to improve scientific discovery. |
Timeline-based Sentence Decomposition with In Context Learning for Temporal Fact Extraction (2024.acl-long)
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| Challenge: | Recent research on temporal fact extraction fails to establish time-to-fact correspondences in complex sentences. |
| Approach: | They propose a timeline-based sentence decomposition strategy using large language models with in-context learning to extract temporal facts from natural language text. |
| Outcome: | The proposed method achieves state-of-the-art on a complex temporal fact extraction dataset. |
Generative Annotation for ASR Named Entity Correction (2025.emnlp-main)
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Yuanchang Luo, Daimeng Wei, Shaojun Li, Hengchao Shang, Jiaxin Guo, Zongyao Li, Zhanglin Wu, Xiaoyu Chen, Zhiqiang Rao, Jinlong Yang, Hao Yang
| Challenge: | Existing named entity correction models fail to transcribe domain-speciffcnamed entities when theforms of the wrongly-transcribed words and the ground-truth entity are signiffcantly different. |
| Approach: | They propose a method that utilizes speech sound features to retrieve candidate entities . it uses speech sound feature to annotate entityerrors in ASR transcripts . |
| Outcome: | The proposed method can bring signiffcant improvement to entity accuracy. |
Data Interpreter: An LLM Agent for Data Science (2025.findings-acl)
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Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Robert Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Yongxin Ni, Zhibin Gou, Zongze Xu, Yuyu Luo, Chenglin Wu
| Challenge: | Large Language Models (LLMs) excel in various domains but face challenges when applied to data science workflows due to their complex, multi-stage nature. |
| Approach: | They propose a hierarchical graph-based agent that represents complexity and a progressive strategy for step-by-step verification, refinement, and consistent context management. |
| Outcome: | The proposed agent surpasses state-of-the-art baselines on the MATH dataset and performs better on InfiAgent-DABench. |
SafeSteer: A Decoding-level Defense Mechanism for Multimodal Large Language Models (2026.findings-acl)
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| Challenge: | Existing defense methods rely on fine-tuning or inefficient post-hoc interventions, limiting their ability to address novel attacks. |
| Approach: | They propose a decoding-level defense mechanism that employs a lightweight discriminator to iteratively steer the decoding process toward safety. |
| Outcome: | The proposed method improves safety performance by up to 33.40% without fine-tuning on multiple MLLMs. |
Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs (2024.findings-emnlp)
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Junjie Wang, Mingyang Chen, Binbin Hu, Dan Yang, Ziqi Liu, Yue Shen, Peng Wei, Zhiqiang Zhang, Jinjie Gu, Jun Zhou, Jeff Pan, Wen Zhang, Huajun Chen
| Challenge: | Recent studies have attempted to enhance the performance of large language models (LLMs) in complex question-answering (QA) tasks by combining step-wise planning with external retrieval. |
| Approach: | They propose a framework for enhancing LLMs’ planning capabilities by using planning data derived from knowledge graphs (KGs). |
| Outcome: | The proposed framework improves LLMs’ planning capabilities by using knowledge graphs (KGs) the proposed framework is compared with existing frameworks on multiple datasets and shows that it is effective for large language models. |
Temporal Scaling Law for Large Language Models (2025.emnlp-main)
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Yizhe Xiong, Xiansheng Chen, Xin Ye, Hui Chen, Zijia Lin, Haoran Lian, Zhenpeng Su, Wei Huang, Jianwei Niu, Jungong Han, Guiguang Ding
| Challenge: | Existing studies have found that the test loss of LLMs scales as power-laws with model size, computational budget, and dataset size. |
| Approach: | They propose a concept of Temporal Scaling Law to study test loss of LLMs . they break down test loss into fine-grained token positions and develop a dynamic hyperbolic-law . |
| Outcome: | The proposed model predicts the test loss of LLMs as the training steps scale up. |
Syllogistic Reasoning for Legal Judgment Analysis (2023.emnlp-main)
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Wentao Deng, Jiahuan Pei, Keyi Kong, Zhe Chen, Furu Wei, Yujun Li, Zhaochun Ren, Zhumin Chen, Pengjie Ren
| Challenge: | Legal judgment assistants are developing fast due to impressive progress of large language models. |
| Approach: | They construct and manually correct a syllogistic reasoning dataset for legal judgment analysis using large language models as benchmarks. |
| Outcome: | The proposed dataset contains 11,239 criminal cases covering 4 criminal elements, 80 charges and 124 articles. |
Hide and Seek in Noise Labels: Noise-Robust Collaborative Active Learning with LLMs-Powered Assistance (2024.acl-long)
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| Challenge: | Existing methods for learning from noisy labels are difficult to improve . existing methods identify noisy labels and use active learning to query experts . |
| Approach: | They propose a collaborative learning framework to combine LLMs and small models for learning from noisy labels. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines on synthetic and real-world noise datasets. |
Unveiling Selection Biases: Exploring Order and Token Sensitivity in Large Language Models (2024.findings-acl)
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| Challenge: | Using zero-shot or few-shot prompting, Large Language Models have been widely adopted in downstream applications. |
| Approach: | They propose to quantify the impact of option order and token usage on LLMs and propose mitigation strategies to enhance model performance. |
| Outcome: | The proposed mitigation strategies improve model performance and reduce the impact of token and order sensitivity on LLMs. |
MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering (2024.acl-long)
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| Challenge: | Recent advances in few-shot question answering rely on pre-trained large language models and fine-tuning in specific settings. |
| Approach: | They propose to select the most informative data for fine-tuning to improve efficiency . they use an approximate graph algorithm and unsupervised question generation to generate QA pairs . |
| Outcome: | The proposed framework improves the performance of the few-shot question answering task on the open-domain QA task. |
Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset (2022.emnlp-main)
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Haolin Deng, Yanan Zhang, Yangfan Zhang, Wangyang Ying, Changlong Yu, Jun Gao, Wei Wang, Xiaoling Bai, Nan Yang, Jin Ma, Xiang Chen, Tianhua Zhou
| Challenge: | Existing EE datasets define fixed event types and design specific schemas for each of them, failing to cover diverse events emerging from the online text. |
| Approach: | They propose to use a sentence-level dataset to benchmark Open Event Extraction without restricting event types. |
| Outcome: | The proposed dataset contains more than 42,000 news titles in 34 topics collected from Chinese web pages. |
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks (N19-1)
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| Challenge: | Existing distance supervised relation extraction models for long-tail data are inadequate for many applications. |
| Approach: | They propose to leverage implicit relational knowledge among class labels and learn explicit relational knowing using graph convolution networks. |
| Outcome: | The proposed approach outperforms baselines for long-tail relations on a large-scale dataset. |
When Do Decompositions Help for Machine Reading? (2023.emnlp-main)
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| Challenge: | Existing work on decompositions of complex questions has focused on multi-step reasoning . but, in machine reading, it is unclear when decomposing is helpful . |
| Approach: | They conduct experiments on decompositions in machine reading to unify recent work . they find that decomposing complex questions can be helpful in zero or limited-data settings . |
| Outcome: | The proposed model can learn decompositions implicitly even with limited data, the study shows . the results are consistent with previous work on decomposing complex questions . |
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 . |
RubricHub: A Comprehensive and Highly Discriminative Rubric Dataset via Automated Coarse-to-Fine Generation (2026.acl-long)
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Sunzhu Li, Jiale Zhao, Huimin Ren, Zhenlin Wei, Yang Zhou, Jingwen Yang, Shunyu Liu, Kaike Zhang, Chen Wei
| Challenge: | Existing methods for generating open-ended rubrics suffer from scalability bottlenecks and coarse criteria resulting in a supervision ceiling effect. |
| Approach: | They propose a framework for automated Coarse-to-Fine Rubric Generation . their framework uses principle-guided synthesis, multi-model aggregation, difficulty evolution . |
| Outcome: | The proposed framework produces comprehensive and highly discriminative criteria capable of capturing the subtle nuances. |
Squrve: A Unified and Modular Framework for Complex Real-World Text-to-SQL Tasks (2026.acl-demo)
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| Challenge: | Existing methods are designed for specific settings, each with its own set of challenges. |
| Approach: | They propose a unified, modular, and extensive Text-to-SQL framework . it proposes a universal execution paradigm and a multi-actor collaboration mechanism . |
| Outcome: | Squrve proposes a unified, modular, and extensive Text-to-SQL framework . the framework outperforms existing methods on widely adopted benchmarks . |
Distributed LLM Serving on Consumer-Grade GPUs by Reconciling Computation and Communication (2025.findings-emnlp)
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Lewei Jin, Kui Zhang, Yongqi Chen, null Zhuoyifan, Renjie Li, Yi Gao, Bowei Yang, Zhengong Cai, Wei Dong
| Challenge: | Large language models are reshaping internet services, and serving them is costly. |
| Approach: | They propose an efficient distributed LLM serving system that splits prefill and decode requests into smaller chunks . |
| Outcome: | The proposed system reduces TTFT, TPOT, and latency compared to the state-of-the-art system. |
NOVER: Incentive Training for Language Models via Verifier-Free Reinforcement Learning (2025.emnlp-main)
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| Challenge: | Recent advances in reinforcement learning, such as DeepSeek R1-Zero, highlight the effectiveness of incentive training, but these methods rely on external verifiers, which limits their applicability to domains like mathematics and coding, where such verifier is readily available. |
| Approach: | They propose a general reinforcement learning framework that requires only standard supervised fine-tuning data with no need for an external verifier. |
| Outcome: | The proposed framework outperforms the model of the same size distilled from large reasoning models such as DeepSeek R1 671B by 7.7%. |
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)
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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He
| Challenge: | Document images are characterized by higher resolutions, denser content, and more complex structural layouts. |
| Approach: | They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition. |
| Outcome: | The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead. |
ImPart: Importance-Aware Delta-Sparsification for Improved Model Compression and Merging in LLMs (2025.acl-long)
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| Challenge: | Recent approaches to reduce resource requirements for task-specific large language models have been developed. |
| Approach: | They propose a delta compression approach that optimizes for importance of a model . they use SVD to dynamically adjust the sparsity ratios of different vectors based on their importance . |
| Outcome: | The proposed approach achieves state-of-the-art in retaining task-specific knowledge even at high sparsity ratios. |
Uncertainty-Aware Test-Time Search for Optimization Problem Solving (2026.acl-long)
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Linlin Yu, Xujiang Zhao, Dong Li, Yanchi Liu, Wei Cheng, Zhengzhang Chen, Chen Zhao, Feng Chen, Haifeng Chen
| Challenge: | Language model hallucinations and limited availability of labeled datasets often result in misaligned formulations, code errors and feasibility failures. |
| Approach: | They propose a Monte Carlo Tree Search framework that automates optimization problems from natural language descriptions with efficiency and reliability. |
| Outcome: | The proposed framework achieves state-of-the-art solution accuracy and reduces token usage. |
Demonstrating ViviDoc: Generating Interactive Documents through Human-Agent Collaboration (2026.acl-demo)
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| Challenge: | Recent LLM-based agents can automate content creation, but naively applying them yields uncontrollable and unverifiable outputs. |
| Approach: | They propose a human-agent collaborative system that generates interactive educational documents from a single topic input. |
| Outcome: | The proposed system generates documents comparable in quality to human-authored ones. |
A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAM𝛥 Integration into Upcycled MoE (2026.acl-long)
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Hao Zhou, Tianhao Li, Zhijun Wang, Shuaijie She, Linjuan Wu, Hao-Ran Wei, Baosong Yang, Jiajun Chen, Shujian Huang
| Challenge: | Large Language Models (LLMs) are expensive and require extensive Continued Pre-Training and data-intensive alignment to expand. |
| Approach: | They propose a method which upcycles a dense model into a Mixture-of-Experts architecture, allocating different experts to different languages. |
| Outcome: | Experiments show that the proposed model upcycles a dense model into a Mixture-of-Experts(MoE) architecture, allocating different experts to different languages. |
CoinMath: Harnessing the Power of Coding Instruction for Math LLM (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have shown strong performance in solving mathematical problems, with code-based solutions proving particularly effective. |
| Approach: | They propose a learning strategy to enhance mathematical reasoning by diversifying the coding styles of code-based rationales. |
| Outcome: | The proposed learning strategy outperforms its baseline model, MAmmoTH, which uses code-based solutions. |
MiLoRA: Efficient Mixture of Low-Rank Adaptation for Large Language Models Fine-tuning (2024.findings-emnlp)
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| Challenge: | Low-rank adaptation and its mixture-of-experts (MOE) methods are highly effective but introduce significant latency in multi-tenant settings due to the LoRA modules and MOE routers added to multiple linear modules. |
| Approach: | They propose a low-rank adaptation variant that considers each LoRA module as an expert and employs a prompt-aware routing mechanism. |
| Outcome: | Extensive analysis on commonsense reasoning tasks and math reasoning tasks show that MiLoRA outperforms strong PEFT baselines with comparable tunable parameter budgets. |
Towards Fine-Grained and Multi-Granular Contrastive Language-Speech Pre-training (2026.acl-long)
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Yifan Yang, Bing Han, Hui Wang, Wei Wang, Ziyang Ma, Long Zhou, Zengrui Jin, Guanrou Yang, Tianrui Wang, Xu Tan, Xie Chen
| Challenge: | Existing models for fine-grained speaking styles are limited in terms of accuracy, coverage, and naturalness. |
| Approach: | They propose a model that pre-trains with coarse captions and annotates with a pipeline that grounds captions in audio. |
| Outcome: | The proposed model outperforms existing models with fine-grained style annotations . it integrates global and fine-granular supervision, enabling unified representations based on the proposed model . |
Infinity-Parser: Layout-Aware Reinforcement Learning with High-quality Document Parsing Dataset (2026.findings-acl)
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Baode Wang, Biao Wu, Weizhen Li, Meng Fang, Zuming Huang, Jun Huang, Yanjie Liang, Haozhe Wang, Ling Chen, Wei Chu, Yuan Qi
| Challenge: | Existing supervised fine-tuning methods struggle to generalize across document types, leading to poor performance. |
| Approach: | They propose layoutRL, a reinforcement learning framework that optimizes layout understanding through composite rewards integrating normalized edit distance, paragraph count accuracy, and reading order preservation. |
| Outcome: | The proposed model outperforms specialized document parsing systems and general-purpose vision-language models on a broad range of document types, languages, and structural complexities. |
Resilience of Large Language Models for Noisy Instructions (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) are powerful tools for interpreting human commands and generating text. |
| Approach: | They examine the resilience of large language models against five common types of disruptions including ASR, OCR, grammatical errors, typographical errors and distractive content. |
| Outcome: | The models show resistance to noise, but their performance suffers . authors evaluated the models against five common types of disruptions based on their results . |
A Deep Relevance Model for Zero-Shot Document Filtering (P18-1)
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| Challenge: | Existing methods for document classification do not consider document filtering . existing methods do not include document filter. |
| Approach: | They propose a deep relevance model for zero-shot document filtering called DAZER . they use word embeddings to extract the relevance signals from word embeds . |
| Outcome: | The proposed model outperforms existing models on two document collections . it estimates the relevance between a document and a category by using seed words . |
SAFER: Advancing Safety Alignment via Efficient Ex-Ante Reasoning (2026.findings-acl)
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| Challenge: | Existing alignment methods struggle to cover diverse safety scenarios and remain vulnerable to adversarial attacks. |
| Approach: | They propose a framework for 'S**afety' alignment via e**F**ficient' E**x-Ante-R**easoning that instantiates structured Ex-Ance reasoning and embeds predefined safety rules. |
| Outcome: | The proposed framework enhances safety performance while maintaining usefulness and efficiency. |
AgentV-RL: Scaling Reward Modeling with Agentic Verifier (2026.findings-acl)
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Jiazheng Zhang, Ziche Fu, Zhiheng Xi, Wenqing Jing, Mingxu Chai, Wei He, Guoqiang Zhang, Chenghao Fan, Chenxin An, Wenxiang Chen, Zhicheng Liu, Haojie Pan, Dingwei Zhu, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing approaches to improve LLM reasoning are limited in complex domains and lack external grounding makes verifiers unreliable on computation-intensive tasks. |
| Approach: | They propose a framework that transforms reward modeling into a multi-turn, tool-augmented deliberative process. |
| Outcome: | The proposed framework surpasses state-of-the-art ORMs by 25.2% under parallel and sequential TTS. |
Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning (2024.acl-long)
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| Challenge: | Experimental results show that fine-tuning of large language models for specific tasks can be challenging . distribution shift during fine-timing can lead to performance degradation in general task capabilities . |
| Approach: | They propose a new approach that bridges the distribution gap between task datasets and LLMs by guiding fine-tuning with a distilled dataset generated by the model itself. |
| Outcome: | The proposed approach achieves comparable or superior performance on downstream tasks compared to the vanilla approach. |
Watermarking Large Language Models: An Unbiased and Low-risk Method (2025.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have highlighted the risk of misusing them, raising the need for accurate detection of LLM-generated content. |
| Approach: | They propose a method to inject imperceptible identifiers into large language models (LLMs) this method is unbiased and preserves the original token distribution in expectation . |
| Outcome: | The proposed method preserves the original token distribution in expectation and has lower risk of producing unsatisfactory outputs in low-entropy scenarios compared to existing unbiased watermarks. |
Federated LoRA Fine-Tuning with Pipelined Error-Mitigated Aggregation and Matrix-Wise Freezing (2026.findings-acl)
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| Challenge: | Existing methods for fine-tuning large language models often suffer from biased model aggregation and are hindered by significant communication and computation burden. |
| Approach: | They propose a Federated low-rank adaptation system for large language models that leverages pipelined error-mitigated model aggregation and adaptive matrix-wise parameter freezing to mitigate aggregations. |
| Outcome: | The proposed system improves time-to-target by 2.17-8.48 on real-world datasets. |
Retrieving, Rethinking and Revising: The Chain-of-Verification Can Improve Retrieval Augmented Generation (2024.findings-emnlp)
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| Challenge: | Recent Retrieval Augmented Generation (RAG) aims to enhance Large Language Models . however, such approach can generate inconsistent answer with external references . |
| Approach: | They propose to integrate the verification module into the RAG to improve external retrieval correctness and internal generation consistency. |
| Outcome: | The proposed model can significantly surpass the state-of-the-art baselines using different LLM backbones. |
E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning (2022.findings-acl)
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Jiangjie Chen, Rui Xu, Ziquan Fu, Wei Shi, Zhongqiao Li, Xinbo Zhang, Changzhi Sun, Lei Li, Yanghua Xiao, Hao Zhou
| Challenge: | Existing benchmarks to test word analogy do not reveal the underneath process of analogical reasoning of neural models. |
| Approach: | They propose an explanation benchmark for analogical reasoning using a Civil Service exam . they use a free-text explanation scheme to explain whether an analogy should be drawn . |
| Outcome: | The proposed benchmark is very challenging for state-of-the-art models, it is found. |
CR-LLM: A Dataset and Optimization for Concept Reasoning of Large Language Models (2024.findings-acl)
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Nianqi Li, Jingping Liu, Sihang Jiang, Haiyun Jiang, Yanghua Xiao, Jiaqing Liang, Zujie Liang, Feng Wei, Jinglei Chen, Zhenghong Hao, Bing Han
| Challenge: | Existing concept reasoning related datasets suffer from modeledge leakage and context leakage. |
| Approach: | They propose a concept reasoning for large language models with modeledge leakage prevention and context leakage preventive methods to improve the models' conceptual reasoning abilities. |
| Outcome: | The proposed method significantly improves the existing models and reasoning methods, achieving a 7% increase in accuracy compared to CoT and showing better granularity. |
KPatch: Knowledge Patch to Pre-trained Language Model for Zero-Shot Stance Detection on Social Media (2024.lrec-main)
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Shuohao Lin, Wei Chen, Yunpeng Gao, Zhishu Jiang, Mengqi Liao, Zhiyu Zhang, Shuyuan Zhao, Huaiyu Wan
| Challenge: | Existing knowledge injection methods fail to understand the semantics of tweets . |
| Approach: | They propose a method to flexibly inject knowledge into a pre-trained language model and adaptively expand tweets context. |
| Outcome: | The proposed method is based on two training stages to flexibly inject knowledge into the pre-trained language model and adaptively expand tweets context. |
FAITH: Factuality Alignment through Integrating Trustworthiness and Honestness (2026.findings-acl)
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| Challenge: | Existing approaches to correct factually inaccurate outputs are lacking the semantic richness needed to properly understand its internal states of trustworthiness and honesty. |
| Approach: | They propose a framework for factuality alignment that integrates natural-language uncertainty signals with external knowledge and computes confidence scores and semantic entropy from LLM outputs. |
| Outcome: | Extensive experiments on four knowledge-intensive benchmarks show that FAITH improves the factual accuracy and truthfulness of Large Language Models (LLMs). |
Beyond Black-Box Interventions: Latent Probing for Faithful Retrieval-Augmented Generation (2026.findings-acl)
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Linfeng Gao, Qinggang Zhang, Baolong Bi, Bo Zeng, Zheng Yuan, Zerui Chen, Zhimin Wei, Shenghua Liu, Linlong Xu, Longyue Wang, Weihua Luo, Jinsong Su
| Challenge: | Existing approaches to improve contextual faithfulness treat the LLM as a black box, generating responses that are inconsistent with the provided context. |
| Approach: | They propose a framework for faithful RAG that operates in three stages: (i) fine-grained knowledge pruning to filter irrelevant context, (ii) latent conflict probing to identify hard conflicts in the model’s latent space, and (iv) conflict-aware attention to modulate attention heads toward faithful context integration. |
| Outcome: | Experiments show that ProbeRAG significantly improves both accuracy and contextual faithfulness. |
Improving Relation Extraction with Relational Paraphrase Sentences (2020.coling-main)
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| Challenge: | Existing annotated data is expensive and non-scalable, limiting performance of relation extraction models. |
| Approach: | They propose to enrich relation expressions by relational paraphrase sentences by annotating human-annotated data. |
| Outcome: | The proposed model improves performance even on a strong baseline. |
PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation (2020.emnlp-main)
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| Challenge: | Existing techniques for natural language understanding and generation use autoencoding and/or autoregressive objectives to train models. |
| Approach: | They propose a self-supervised pre-training scheme that pre-trains an autoencoding and autoregressive language model on a large unlabeled corpus for generating new text conditioned on context. |
| Outcome: | The proposed scheme achieves state-of-the-art results on a variety of language generation benchmarks covering generative question answering, abstractive summarization and conversational response generation. |
Investigating Multi-Hop Factual Shortcuts in Knowledge Editing of Large Language Models (2024.acl-long)
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| Challenge: | Recent work has demonstrated the power of large language models in recalling knowledge and reasoning. |
| Approach: | They propose to erase shortcut neurons to mitigate the associated risks . 20% of the failures are attributed to shortcuts, they find . |
| Outcome: | The proposed approach reduces failures in multi-hop knowledge editing caused by shortcuts by 20% . |
Robust Learning for Multi-party Addressee Recognition with Discrete Addressee Codebook (2023.acl-short)
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| Challenge: | Existing addressee recognition models suffer from the issue of robustness when applied in real-world scenes. |
| Approach: | They propose a method which discretes addressees into a character codebook and makes it robust in a noisy environment. |
| Outcome: | The proposed method represents open set addressees and is robust even in noisy environments. |
Automatic Expert Discovery in LLM Upcycling via Sparse Interpolated Mixture-of-Experts (2025.acl-long)
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| Challenge: | Sparse Interpolated Mixture-of-Experts (SIMoE) instruction-tuning is an end-to-end algorithm designed to fine-tune a dense pre-trained Large Language Model (LLM) into a MoE-style model that possesses capabilities in multiple specialized domains. |
| Approach: | They propose an algorithm to fine-tune a dense pre-trained Large Language Model into a MoE-style model that possesses capabilities in multiple specialized domains. |
| Outcome: | The proposed algorithm achieves state-of-the-art on common instruction-tuning benchmarks while maintaining an optimal performance-compute trade-off compared to baselines. |
PaT: Planning-after-Trial for Efficient Test-Time Code Generation (2026.acl-long)
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| Challenge: | Existing methods for scaling test-time computation are rigid and inefficient . a heterogeneous configuration achieves performance comparable to a large homogeneously model . |
| Approach: | They propose an adaptive planning policy that invokes a planner only upon verification failure. |
| Outcome: | The proposed model achieves comparable performance to a large homogeneous model while reducing inference cost by approximately 69% across multiple benchmarks and model families. |
Mutual-Learning Improves End-to-End Speech Translation (2021.emnlp-main)
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| Challenge: | Existing approaches to end-to-end speech translation (E2E) models only allow one way knowledge transfer, which is limited by the performance of the teacher model. |
| Approach: | They propose a one-way knowledge transfer paradigm where the MT and ST models are collaboratively trained and considered as peers rather than teacher/student. |
| Outcome: | The proposed model improves the performance of end-to-end speech translation (ST) task by combining knowledge from two models with peer models. |
Incorporating External Knowledge into Machine Reading for Generative Question Answering (D19-1)
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| Challenge: | Existing knowledge-aware QA models do not have commonsense and background knowledge to answer nontrivial questions. |
| Approach: | They propose a new neural model which exploits external knowledge to generate answers in natural language for a given question with context. |
| Outcome: | The proposed model improves answer quality over existing models without knowledge and knowledge-aware models, a study shows . state officials in Hawaii confirmed that president Barack Obama was born in the U.S. |
Unveiling the Unknown: Open-Set Entity Typing via Two-Stage Generation (2026.acl-long)
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| Challenge: | Existing fine-grained entity typing models are susceptible to misclassify unknown-type instances . manual collection and annotation of large unknown-Type instances is time-consuming and labor-intensive in open environments. |
| Approach: | They propose a novel task that uses open-set entity typing to classify entities of unknown types . they propose 'two-stage generation model' that automatically produces large-scale pseudo unknown-type instances . |
| Outcome: | The proposed framework surpasses baselines on two newly established benchmark datasets. |
Through the Valley: Path to Effective Long CoT Training for Small Language Models (2025.emnlp-main)
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| Challenge: | Long chain-of-thought (CoT) supervision is effective for large language models . but small models trained on limited long CoT data experience performance degradation . |
| Approach: | They identify a phenomenon called Long CoT Degradation in small language models . long CoT data can be used to generate long chain-of-thought (CoT) responses . |
| Outcome: | The results show that models trained on 8k long CoT examples lose up to 75% of their original performance before fine-tuning. |
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. |
HiEdit: Lifelong Model Editing with Hierarchical Reinforcement Learning (2026.acl-long)
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| Challenge: | Existing approaches to lifelong model editing apply parameter perturbations to static and dense layers for all instances. |
| Approach: | They propose a hierarchical reinforcement learning framework that identifies the most knowledge-relevant layers for each editing instance. |
| Outcome: | The proposed framework boosts the performance of the competitive RLEdit by 8.48% with perturbing only half of the layers per edit. |
AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios (2025.naacl-long)
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Xinyi Mou, Jingcong Liang, Jiayu Lin, Xinnong Zhang, Xiawei Liu, Shiyue Yang, Rong Ye, Lei Chen, Haoyu Kuang, Xuanjing Huang, Zhongyu Wei
| Challenge: | Large language models are increasingly employed to empower autonomous agents to simulate human behavior. |
| Approach: | They propose to evaluate LLM-driven agents through multi-turn interactions using a bottom-up approach to create diverse social scenarios constructed from extensive scripts. |
| Outcome: | The proposed model evaluates LLM-driven agents through multi-turn interactions emphasizing goal completion and implicit reasoning. |
EARL: Informative Knowledge-Grounded Conversation Generation with Entity-Agnostic Representation Learning (2021.emnlp-main)
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| Challenge: | Existing knowledge-grounded conversation models lack knowledge that occurs in training data, resulting in incomplete knowledge generation. |
| Approach: | They propose an Entity-Agnostic Representation Learning method to introduce knowledge graphs to informative conversation generation using context of conversations and relational structure of knowledge graph. |
| Outcome: | The proposed model generates more informative, coherent, and natural responses than baseline models. |
AdaFuse: Adaptive Ensemble Decoding for Large Language Models (2026.acl-long)
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Chengming Cui, Tianxin Wei, Ziyi Chen, Ruizhong Qiu, Zhichen Zeng, Zhining Liu, Xuying Ning, Duo Zhou, Jingrui He
| Challenge: | Existing ensemble approaches to large language models lack flexibility for mid-generation adaptation. |
| Approach: | They propose an adaptive ensemble decoding framework that dynamically selects semantically appropriate fusion units during generation. |
| Outcome: | The proposed framework outperforms existing ensemble frameworks on open-domain QA, arithmetic reasoning, and machine translation tasks. |
Beyond Sequences: Two-dimensional Representation and Dependency Encoding for Code Generation (2025.acl-long)
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| Challenge: | Existing code generation approaches represent code as a linear sequence of tokens, but positional encodings compromise generalization . explicit positional encoders sacrifice permutation invariance, imposes a strict order on the input sequence . |
| Approach: | They propose to represent code snippets as two-dimensional entities with explicit encodings . they propose to use dictionary learning to perform semantic matching between code lines . |
| Outcome: | The proposed model captures the hierarchical and spatial structure of code, especially the dependencies between code lines. |
AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)
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Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang
| Challenge: | Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents. |
| Approach: | They propose a framework that features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
| Outcome: | The proposed framework features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
Granular Privacy Control for Geolocation with Vision Language Models (2024.emnlp-main)
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| Challenge: | Vision Language Models (VLMs) are rapidly advancing in their capability to answer information-seeking questions. |
| Approach: | They develop a benchmark to evaluate the ability of VLMs to moderate geolocation dialogues with users. |
| Outcome: | a new benchmark evaluates the ability of VLMs to moderate geolocation conversations with users. |
STK-Adapter: Incorporating Evolving Graph and Event Chain for Temporal Knowledge Graph Extrapolation (2026.acl-long)
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Shuyuan Zhao, Wei Chen, Weijie Zhang, Xinrui Hou, Junfeng Shen, Boyan Shi, Shengnan Guo, Youfang Lin, Huaiyu Wan
| Challenge: | Temporal Knowledge Graphs (TKGs) store dynamic facts in the real world. |
| Approach: | They propose a Spatial-Temporal Knowledge Adapter which integrates the evolving graph encoder and the LLM to facilitate TKG reasoning. |
| Outcome: | The proposed method outperforms state-of-the-art methods on benchmark datasets and exhibits strong generalization capabilities in cross-dataset task. |
Multi-Granularity Semantic Revision for Large Language Model Distillation (2026.acl-long)
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Xiaoyu Liu, Yun Zhang, Wei Li, Simiao Li, Xudong Huang, Hanting Chen, Yehui Tang, Jie Hu, Zhiwei Xiong, Yunhe Wang
| Challenge: | Existing methods for generating large language models rely on student-generated outputs, which introduce generation errors and misguide the distillation process. |
| Approach: | They propose a multi-granularity semantic revision method for LLM distillation that corrects errors using teacher-generated tokens and re-generates the sequence to minimize errors. |
| Outcome: | The proposed method reduces errors and misguides distillation on student models and improves consistency between teacher and student outputs. |
Modeling Evolution of Message Interaction for Rumor Resolution (2020.coling-main)
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| Challenge: | Existing methods for rumor resolution ignore local interactions during the message diffusion which is important for the identification of rumors. |
| Approach: | They propose to model confrontation and reciprocity between message pairs via discrete variational autoencoders which effectively reflects the diversified opinion interactivity. |
| Outcome: | Experiments on a PHEME dataset show that the proposed model achieves higher accuracy than existing methods. |
DocumentNet: Bridging the Data Gap in Document Pre-training (2023.emnlp-industry)
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| Challenge: | Document understanding tasks are a tedious task that requires extensive training and privacy constraints. |
| Approach: | They propose a method to collect weakly labeled data from the web to benefit VDER training . the collected dataset does not depend on specific document types or entity sets . |
| Outcome: | The proposed method does not depend on specific document types or entity sets, making it universally applicable to all VDER tasks. |
Past Meets Present: Creating Historical Analogy with Large Language Models (2025.acl-long)
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Nianqi Li, Siyu Yuan, Jiangjie Chen, Jiaqing Liang, Feng Wei, Zujie Liang, Deqing Yang, Yanghua Xiao
| Challenge: | Historical analogies are important abilities that help people make decisions and understand the world. |
| Approach: | They propose a historical analogy acquisition task that uses large language models to acquire historical analogies. |
| Outcome: | The proposed method mitigates hallucinations and stereotypes when LLMs generate historical analogies. |
Multi-Agent Collaboration via Cross-Team Orchestration (2025.findings-acl)
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Zhuoyun Du, Chen Qian, Wei Liu, Zihao Xie, YiFei Wang, Rennai Qiu, Yufan Dang, Weize Chen, Cheng Yang, Ye Tian, Xuantang Xiong, Lei Han
| Challenge: | Large Language Models (LLMs) have significantly impacted various domains, especially through organized LLM-driven autonomous agents. |
| Approach: | They propose a framework that enables orchestrated teams to jointly propose various task-oriented solutions and interact with their insights in a self-independence while cross-team collaboration environment for superior solutions generation. |
| Outcome: | Experiments show that the framework can generate better software quality compared to state-of-the-art frameworks. |
Enhancing Dialogue Symptom Diagnosis with Global Attention and Symptom Graph (D19-1)
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| Challenge: | Existing studies on symptom diagnosis based on EHRs focus on the standard electronic medical records, but the dialogues between doctors and patients that contain more rich information are not well studied. |
| Approach: | They propose to build a global attention mechanism to capture more symptom related information and build symptom graphs to model the associations between symptoms rather than treating each symptom independently. |
| Outcome: | The proposed model achieves the state-of-the-art on the constructed dataset. |
DialMed: A Dataset for Dialogue-based Medication Recommendation (2022.coling-1)
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| Challenge: | Existing studies on medication recommendation mainly rely on EHRs, but some details of interactions between doctors and patients may be ignored or omitted in EHR. |
| Approach: | They propose to use medical dialogues to recommend medications with medical dialogue data . they propose to model dialogue structure and disease knowledge aware network . |
| Outcome: | The proposed method is a promising solution to recommend medications with medical dialogues. |
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. |
Mitigating Boundary Ambiguity and Inherent Bias for Text Classification in the Era of Large Language Models (2024.findings-acl)
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| Challenge: | a new text classification framework for large language models addresses the problem of boundary ambiguity and inherent biases in LLMs. |
| Approach: | They propose a two-stage classification framework for large language models to mitigate bottlenecks . their approach uses pairwise comparisons to efficiently narrow down options . |
| Outcome: | The proposed framework reduces the number of options and improves on four datasets. |
Rectified Sparse Attention for Efficient Long-Sequence Generation (2026.findings-acl)
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Yutao Sun, Tianzhu Ye, Li Dong, Yuqing Xia, Jian Chen, Yizhao Gao, Shijie Cao, Jianyong Wang, Furu Wei
| Challenge: | Recent sparse decoding methods improve efficiency but suffer from KV cache misalignment, resulting in performance degradation. |
| Approach: | They propose a method that combines block-sparse attention with periodic dense rectification to bound error accumulation and preserve alignment with the pretraining distribution. |
| Outcome: | Experiments on math reasoning, language modeling, and retrieval tasks show that ReSA achieves near-lossless generation quality with significantly improved efficiency. |
EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification (2024.findings-acl)
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| Challenge: | Existing studies on fact verification lack a high-quality dataset for explainability . existing systems lack evidence retrieval and veracity prediction, limiting the ability to verify a claim . |
| Approach: | They propose a dataset for multi-hop explainable fact verification that summarises and modifies Wikipedia documents. |
| Outcome: | The proposed dataset aims to improve the accuracy of multi-hop explainable fact verification systems. |
Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework (2023.findings-acl)
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| Challenge: | Existing methods for adversarial samples are poorly applied in computer vision . however, textual adversarials are still vulnerable to small perturbations . |
| Approach: | They propose a framework to extend existing adversarial attack methods to textual adversarials by adding optimized perturbations to embedding layer and amplifying them in forward propagation process. |
| Outcome: | The proposed framework achieves better performance even using proxy gradient information and produces more fluent and grammatical adversarial samples compared to baseline methods. |
EGSS: Entropy-guided Stepwise Scaling for Reliable Software Engineering (2026.acl-long)
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Chenhui Mao, Yuanting Lei, Zhixiang Wei, Ming Liang, Zhixiang Wang, Jingxuan Xu, Dajun Chen, Wei Jiang, Yong Li
| Challenge: | Entropy-Guided Stepwise Scaling (EGSS) is a novel TTS framework for software engineering tasks. |
| Approach: | They propose an entropy-guided stepwise scaling framework that balances efficiency and effectiveness through entropic-guide encoding and robust test-suite augmentation. |
| Outcome: | EGSS boosts performance by 5–10% across all evaluated models, and reduces inference-time token usage by over 28% . compared to existing methods, EGS reduces token usage and reduce inference time by over 20% . |
Explore More Guidance: A Task-aware Instruction Network for Sign Language Translation Enhanced with Data Augmentation (2022.findings-naacl)
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| Challenge: | Existing studies focus on the recognition step, while paying less attention to sign language translation. |
| Approach: | They propose a task-aware instruction network, namely TIN-SLT, for sign language translation, by introducing the isntruction module and the learning-based feature fuse strategy into a Transformer network. |
| Outcome: | The proposed system outperforms existing solutions on two benchmark datasets, PHOENIX-2014-T and ASLG-PC12, and outperformed previous best solutions by 1.65 and 1.42 in terms of BLEU-4. |
AirConcierge: Generating Task-Oriented Dialogue via Efficient Large-Scale Knowledge Retrieval (2020.findings-emnlp)
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| Challenge: | Existing neural task-oriented dialogue systems cannot be encoded by memory networks, such as memory networks. |
| Approach: | They propose an end-to-end trainable text-to SQL guided framework to learn a neural agent that interacts with KBs using the generated SQL queries. |
| Outcome: | The proposed method significantly improves on the AirDialogue dataset, which contains the conversations of customers booking flight tickets from the agent. |
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. |
Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction (2020.coling-main)
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| Challenge: | Existing approaches to supervised relational triple extraction require huge amounts of labeled data. |
| Approach: | They propose a multi-prototype embedding network model to extract the composition of relational triples from unstructured text. |
| Outcome: | The proposed method improves the performance of the few-shot relational triple extraction problem. |
Octopus: On-device language model for function calling of software APIs (2025.naacl-industry)
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| Challenge: | Large Language Models (LLMs) are pivotal for advanced text processing and generation. |
| Approach: | They propose a framework to train on-device Large Language Models optimized for invoking software APIs. |
| Outcome: | The proposed model outperforms GPT-4 in API calling tasks while maintaining inference speed. |
Focus-Constrained Attention Mechanism for CVAE-based Response Generation (2020.findings-emnlp)
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| Challenge: | Existing models generate high-frequency but trivial responses such as "I don't know" or "I'm ok" due to the discrepancy in discourse-level information, standard models generate one-to-many relationships. |
| Approach: | They propose to transform coarse-grained discourse-level information into fine-grounded word-level knowledge by introducing a fine-grain focus signal and a focus-constrained attention mechanism to take full advantage of focus. |
| Outcome: | The proposed model can generate more diverse and informative responses compared with state-of-the-art models. |
Structural Bias for Aspect Sentiment Triplet Extraction (2022.coling-1)
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| Challenge: | Existing structural bias adapters for aspect sentiment triplet extraction are under-confident . a large-scale dataset for ASTE shows the adapter is effective and efficient to a larger scale. |
| Approach: | They propose to use a structural adapter to integrate structural bias into pretrained language models . they propose to add a relative position structure in place of the syntactic dependency structure . |
| Outcome: | The proposed adapter achieves state-of-the-art performance over strong baselines, but with a light parameter demand and low latency. |
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. |
ADEPT-SQL: A High-performance Text-to-SQL Application for Real-World Enterprise-Level Databases (2025.acl-demo)
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Yongnan Chen, Zhuo Chang, Shijia Gu, Yuanhang Zong, Zhang Mei, Shiyu Wang, Hezixiang Hezixiang, Hongzhi Chen, Jin Wei, Bin Cui
| Challenge: | et al., 2017) address domain-specific knowledge barriers, schemas complexity, and computational costs of large LLMs. |
| Approach: | They propose a domain-adapted Text2SQL system that addresses critical deployment challenges in professional fields. |
| Outcome: | The proposed system achieves 97% execution accuracy on real-world databases . it is faster than existing systems and has a higher performance than existing ones. |
Unsupervised Concept Representation Learning for Length-Varying Text Similarity (2021.naacl-main)
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| Challenge: | Existing document similarity approaches suffer from the information gap caused by context and vocabulary mismatches when comparing varying-length texts. |
| Approach: | They propose an unsupervised concept representation learning approach to address this issue . they propose a concept-based document matching method to leverage recognition of local phrase features . |
| Outcome: | The proposed method achieves a better F1 score than baseline models on real-world data sets. |
Reinforcement Learning with Token-level Feedback for Controllable Text Generation (2024.findings-naacl)
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| Challenge: | Existing methods for controllable text generation are guided by coarse-grained feedback, which may lead to suboptimal performance owing to semantic twists or progressions within sentences. |
| Approach: | They propose a reinforcement learning algorithm which formulates TOken-LEvel rewards for controllable text generation and employs a "first-quantize-then-noise" paradigm to enhance the robustness of the RL algorithm. |
| Outcome: | The proposed algorithm can achieve superior performance on single-attribute and multi-attract control tasks. |
Mitigating the Language Mismatch and Repetition Issues in LLM-based Machine Translation via Model Editing (2024.emnlp-main)
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| Challenge: | Existing studies have focused on using Large Language Models to improve translation quality . language mismatch and repetition are two of the main problems with LLMs . |
| Approach: | They propose to leverage model editing methods to reduce language mismatch and repetition . they propose to fetch intersections of locating results under different language settings . |
| Outcome: | The proposed methods reduce language mismatch and repetition ratios and enhance translation quality in most cases. |
BotChat: Evaluating LLMs’ Capabilities of Having Multi-Turn Dialogues (2024.findings-naacl)
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Haodong Duan, Jueqi Wei, Chonghua Wang, Hongwei Liu, Yixiao Fang, Songyang Zhang, Dahua Lin, Kai Chen
| Challenge: | Modern Large Language Models (LLMs) facilitate high-quality, multi-turn dialogues with humans, but human-based evaluation of such a capability requires substantial manual effort. |
| Approach: | They propose to evaluate LLMs' ability to emulate human-like, multi-turn conversations using an LLM-centric approach. |
| Outcome: | The proposed model emulates human-like, multi-turn conversations using an LLM-centric approach. |
ACORD: An Expert-Annotated Retrieval Dataset for Legal Contract Drafting (2025.acl-long)
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Steven H Wang, Maksim Zubkov, Kexin Fan, Sarah Harrell, Yuyang Sun, Wei Chen, Andreas Plesner, Roger Wattenhofer
| Challenge: | Contract clause retrieval is critical to contract drafting because of its high quality and complexity. |
| Approach: | They propose the first expert-annotated benchmark specifically designed for contract clause retrieval . ACORD focuses on complex contract clauses such as Limitation of Liability, Indemnification, Change of Control . |
| Outcome: | The atticus clause retrieval dataset shows promising results but needs improvement . the benchmark can be used as an IR benchmark for the NLP community . |
Align Voting Behavior with Public Statements for Legislator Representation Learning (2021.acl-long)
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| Challenge: | Existing studies rely on roll call data to estimate political preference of legislators. |
| Approach: | They propose to integrate voting behavior and public statements on Twitter to jointly model legislators. |
| Outcome: | The proposed model improves on the task of roll call vote prediction . it also shows that the model captures nuances in statements . |
Logical Phase Transitions: Understanding Collapse in LLM Logical Reasoning (2026.acl-long)
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| Challenge: | Symbolic logical reasoning is a critical yet underexplored capability of large language models (LLMs). |
| Approach: | They propose a framework that aligns natural language with logical symbols to establish a shared representation and reshapes training dynamics around phase-transition boundaries to progressively strengthen reasoning at increasing logical depths. |
| Outcome: | The proposed framework mitigates logical reasoning collapse at high complexity while improving generalization to unseen logical compositions. |
Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce (P18-2)
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| Challenge: | Recent researches focus on deep learning and reinforcement learning for multi-turn information seeking conversation systems. |
| Approach: | They propose an efficient and effective multi-turn conversation model based on convolutional neural networks and extend it to adapt the knowledge learned from a resource-rich domain to enhance the performance. |
| Outcome: | The proposed model performs better than the existing model on an industrial chatbot called AliMe Assist. |
SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation (2025.findings-acl)
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| Challenge: | Existing process annotation approaches are computationally expensive. |
| Approach: | They propose a compression-based approach that transforms reasoning steps into code and normalizes them through Abstract Syntax Tree. |
| Outcome: | The proposed method outperforms existing methods on Best-of-N strategy and ProcessBench. |
Evaluating Implicit Bias in Large Language Models by Attacking From a Psychometric Perspective (2025.findings-acl)
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| Challenge: | Existing studies have shown that large language models (LLMs) can elicit implicit biases that hurt certain demographics without explicit harmful words. |
| Approach: | They propose three attack approaches to elicit agreements to biased viewpoints from LLMs from a psychometric perspective and built two benchmarks to compare them. |
| Outcome: | The proposed methods elicit agreements to biased viewpoints more effectively than baselines. |
Multi-level Association Refinement Network for Dialogue Aspect-based Sentiment Quadruple Analysis (2025.acl-long)
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| Challenge: | Existing methods for identifying quadruples rely on predefined dialogue structure and word semantics to achieve accurate and comprehensive sentiment associations between utterances and words. |
| Approach: | They propose a multi-level association refinement network to achieve more accurate sentiment associations between utterances and words. |
| Outcome: | The proposed framework achieves state-of-the-art performance under low-resource conditions. |
Assessing Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks (2025.acl-long)
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Fangru Lin, Shaoguang Mao, Emanuele La Malfa, Valentin Hofmann, Adrian de Wynter, Xun Wang, Si-Qing Chen, Michael J. Wooldridge, Janet B. Pierrehumbert, Furu Wei
| Challenge: | a study aims to assess the fairness and robustness of Large Language Models in dialectal queries . speakers of "non-standard" dialects are known to experience implicit and explicit discrimination . |
| Approach: | They propose to use a benchmark to assess the fairness of large language models in dialects . they hire speakers with computer science backgrounds to rewrite seven popular benchmarks based on AAVE . |
| Outcome: | The proposed benchmarks show that most models show significant brittleness and unfairness to queries in AAVE. |
MM-Align: Learning Optimal Transport-based Alignment Dynamics for Fast and Accurate Inference on Missing Modality Sequences (2022.emnlp-main)
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| Challenge: | Existing approaches to multimodal learning assume a complete input modality setting, i.e., each modality is either complete or completely missing in both training and test sets. |
| Approach: | They propose an alignment dynamics learning module based on the theory of optimal transport for missing data imputation and a denoising training algorithm to enhance the quality of iputation and accuracy of model predictions. |
| Outcome: | The proposed method performs faster and more accurate inferences under different missing conditions and alleviates the overfitting issue. |
Bias in the Ear of the Listener: Assessing Sensitivity in Audio Language Models Across Linguistic, Demographic, and Positional Variations (2026.findings-eacl)
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| Challenge: | Recent advances extend language understanding beyond text to speech, enabling unified reasoning across modalities. |
| Approach: | They construct and release a speech-augmented benchmark based on Global MMLU Lite and a data set spanning English, Chinese, and Korean. |
| Outcome: | The proposed model is robust to demographic factors but sensitive to language and option order, suggesting that speech can amplify structural biases. |
Tackling Modality Heterogeneity with Multi-View Calibration Network for Multimodal Sentiment Detection (2023.acl-long)
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| Challenge: | Existing studies focus on fusing different features but ignore the challenge of modality heterogeneity. |
| Approach: | They propose a text-guided fusion module with novel Sparse-Attention to reduce the negative impacts of redundant visual elements and a sentiment-based congruity constraint task to calibrate the feature shift in the representation space. |
| Outcome: | The proposed model is competitive against existing methods and achieves state-of-the-art results on two public benchmark datasets. |
MAPO: Advancing Multilingual Reasoning through Multilingual-Alignment-as-Preference Optimization (2024.acl-long)
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| Challenge: | Existing models exhibit inconsistent reasoning abilities across different languages . existing models lack consistency across languages due to imbalance of training data . |
| Approach: | They propose a multilingual alignment-as-preference optimization framework to align reasoning processes in other languages with the dominant language. |
| Outcome: | The proposed framework improves multilingual reasoning across languages on three benchmarks. |
Modeling Historical Relevant and Local Frequency Context for Representation-Based Temporal Knowledge Graph Forecasting (2024.findings-emnlp)
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| Challenge: | Existing representation-based approaches neglect candidate-specific temporal context, resulting in serious information loss or homogeneous prediction. |
| Approach: | They propose a temporal representation learning model that incorporates temporal contexts of candidates and models temporal contextual information from historiCal Relevant context and locAl Frequency contexT. |
| Outcome: | The proposed model can leverage temporal contextual information to achieve differential predictions on six benchmark datasets. |
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. |
See the World, Discover Knowledge: A Chinese Factuality Evaluation for Large Vision Language Models (2025.findings-acl)
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Jihao Gu, Yingyao Wang, Pi Bu, Chen Wang, Ziming Wang, Tengtao Song, Donglai Wei, Jiale Yuan, Yingxiu Zhao, Yancheng He, Shilong Li, Jiaheng Liu, Meng Cao, Jun Song, Yingshui Tan, Xiang Li, Wenbo Su, Xiaoyong Zhu, Bo Zheng
| Challenge: | Existing models for large vision language models do not fully reflect their knowledge capacity and reliability, resulting in erroneous outputs that do not align with the image content or provide answers lacking knowledge evidence. |
| Approach: | They propose a Chinese-based benchmark for visual factuality across 8 major topics and 56 subtopics and a multi-hop question construction. |
| Outcome: | The proposed model decouples visual factuality into two parts: seeing the world and discovering knowledge. |
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. |
AnchorSeg: Language Grounded Query Banks for Reasoning Segmentation (2026.acl-long)
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Rui Qian, Chuanhang Deng, Qiang Huang, Jian Xiong, Mingxuan Li, Yingbo Zhou, Wei Zhai, Jintao Chen, Dejing Dou
| Challenge: | Existing models rely on a single segmentation token whose hidden state implicitly encodes both semantic reasoning and spatial localization . Existing methods rely only on SEG>, which encodes semantic reasoning, limiting the model's ability to explicitly disentangle what to segment from where to segment. |
| Approach: | They propose a method which reformulates reasoning segmentation as a structured conditional generation process over image tokens conditioned on language grounded query banks. |
| Outcome: | The proposed model bridges token-level predictions and pixel-level supervision by decoupling spatial grounding from semantic reasoning through structured language grounded query banks. |
Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner (2022.findings-naacl)
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Danilo Neves Ribeiro, Shen Wang, Xiaofei Ma, Rui Dong, Xiaokai Wei, Henghui Zhu, Xinchi Chen, Peng Xu, Zhiheng Huang, Andrew Arnold, Dan Roth
| Challenge: | Large language models have achieved high performance on various natural language benchmarks, but the explainability of their output remains elusive. |
| Approach: | They propose an architecture called iterative retrieval-generation reasoner that generates an entailment tree that explains a given hypothesis by using premises from C. |
| Outcome: | The proposed model outperforms existing benchmarks on premise retrieval and entailment tree generation with around 300% gain in overall correctness. |
LegalGraphRAG: Multi-Agent Graph Retrieval-Augmented Generation for Reliable Legal Reasoning (2026.acl-long)
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Zerui Chen, Qinggang Zhang, Zhishang Xiang, Zhimin Wei, Linfeng Gao, Xiao Huang, Zhihong Zhang, Jinsong Su
| Challenge: | Graph-based Retrieval-Augmented Generation (GraphRAG) is a new approach to document retrieval, but it is not suitable for legal reasoning. |
| Approach: | They propose a framework for reliable legal reasoning that structures knowledge as relational graphs and uses a multi-agent system to verify validity. |
| Outcome: | The proposed framework outperforms existing GraphRAG models in accurate and trustworthy legal analysis. |
Forget the Token and Pixel: Rethinking Gradient Ascent for Concept Unlearning in Multimodal Generative Models (2025.findings-acl)
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Jiaqi Li, Chuanyi Zhang, Miaozeng Du, Hui Zhang, Yongrui Chen, Qianshan Wei, Junfeng Fang, Ruipeng Wang, Sheng Bi, Guilin Qi
| Challenge: | Gradient Ascent (GA) has emerged as a promising approach for concept unlearning in Multimodal Generative Models (MGMs). |
| Approach: | They propose a novel approach that selectively applies GA to targeted Conceptual Knowledge while preserving Natural Knowledge through Gradient Descent (GD). |
| Outcome: | The proposed approach removes Conceptual Knowledge and inadvertently diminishes Natural Knowledge, resulting in utility degradation. |
CLOWER: A Pre-trained Language Model with Contrastive Learning over Word and Character Representations (2022.coling-1)
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Borun Chen, Hongyin Tang, Jiahao Bu, Kai Zhang, Jingang Wang, Qifan Wang, Hai-Tao Zheng, Wei Wu, Liqian Yu
| Challenge: | Pre-trained language models (PLMs) have achieved remarkable performance gains across numerous downstream tasks in natural language understanding. |
| Approach: | They propose a Chinese pre-trained language model that implicitly encodes words into characters . they propose 'contrastive learning over word' and 'character' representations to improve learning . |
| Outcome: | The proposed model can encode words into fine-grained representations without modification of production pipelines. |
CADReview: Automatically Reviewing CAD Programs with Error Detection and Correction (2025.acl-long)
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| Challenge: | Computer-aided design (CAD) is crucial in prototyping 3D objects through geometric instructions. |
| Approach: | They propose a CAD review task to automatically detect and correct potential errors . they propose CAD program repairer framework to provide helpful feedback on error correction . |
| Outcome: | The proposed framework outperforms existing MLLMs in detecting errors and providing feedback on error correction. |
SAMoRA: Semantic-Aware Mixture of LoRA Experts for Task-Adaptive Learning (2026.findings-acl)
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| Challenge: | Existing methods for multitask learning fail to match input semantics with expert capabilities, leading to weak expert specialization. |
| Approach: | They propose a parameter-efficient mixture-of-experts framework for task-adaptive learning that aligns textual semantics with the most suitable experts for precise routing. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods and holds excellent task generalization capabilities. |
PLAWBENCH: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice (2026.acl-long)
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Yuzhen Shi, Huanghai Liu, Yiran HU, Song Gaojie, Xu Xinran, Yubo Ma, Tianyi Tang, Li Zhang, Qingjing Chen, Feng Di, Wenbo Lv, Weiheng Wu, Kexin Yang, Sen Yang, Wei Wang, Rongyao Shi, Qiu Yuanyang, Yuemeng Qi, Zhang Jingwen, Sui Xiaoyu, Yifan Chen, Zhang Yi, An Yang, Bowen Yu, Dayiheng Liu, Junyang Lin, Weixing Shen, Bing Zhao, Charles L. A. Clarke, HU Wei
| Challenge: | Existing benchmarks for large language models (LLMs) are coarse, single-dimensional metrics and do not explicitly assess fine-grained legal reasoning. |
| Approach: | They propose a Practical Law Benchmark to evaluate large language models in real-world legal practice scenarios. |
| Outcome: | The proposed model is based on 850 questions and 13 scenarios with expert-designed evaluation rubrics. |
Why Did Apple Fall: Evaluating Curiosity in Large Language Models (2026.findings-acl)
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| Challenge: | Existing methods for evaluating curiosity-like behaviors in large language models lack curiosity-inspired features. |
| Approach: | They propose a psychology-inspired framework to evaluate curiosity in large language models . they adapt the Five-Dimensional Curiosity scale Revised (5DCR) to LLMs . |
| Outcome: | The proposed framework evaluates curiosity in large language models using questionnaires and behavioral studies. |
Beyond Quantity: Trajectory Diversity Scaling for Code Agents (2026.findings-acl)
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Guhong Chen, Chenghao Sun, Cheng Fu, Qiyao Wang, Zhihong Huang, ChaoPeng Wei, Guangxu Chen, Feiteng Fang, Ahmadreza Argha, Bing Zhao, Xander Xu, Qi Han, Hamid Alinejad-Rokny, Qiang Qu, Binhua Li, Shiwen Ni, Min Yang, HU Wei, Yongbin Li
| Challenge: | Code large language models (LLMs) are becoming tool-interactive agents . quantity-centric scaling exhibits an early bottleneck that underutilizes trajectory data . et al.: a new approach to scale trajectory diversity improves tool-use generalization . |
| Approach: | They propose a Trajectory Diversity Scaling-based data synthesis framework for code agents that scales performance through diversity rather than raw volume. |
| Outcome: | Experiments on general tool-use benchmarks and code agent tasks show that TDScaling improves tool-user generalization and inherent coding proficiency. |
Making Parameter-efficient Tuning More Efficient: A Unified Framework for Classification Tasks (2022.coling-1)
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Xin Zhou, Ruotian Ma, Yicheng Zou, Xuanting Chen, Tao Gui, Qi Zhang, Xuanjing Huang, Rui Xie, Wei Wu
| Challenge: | Large pre-trained language models (PLMs) have demonstrated superior performance in industrial applications. |
| Approach: | They propose a framework that re-uses existing parameter-efficient methods with a unified classifier. |
| Outcome: | The proposed framework improves the efficiency of existing parameter-efficient methods with a unified classifier. |
Agent-GWO: Collaborative Agents for Dynamic Prompt Optimization in Large Language Models (2026.findings-acl)
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Xudong Wang, Chaoning Zhang, Chenghao Li, Shuxu Chen, Qigan Sun, Jiaquan Zhang, Fachrina Dewi Puspitasari, Tae-Ho Kim, Jiwei Wei, Malu Zhang, Guoqing Wang, Yang Yang, Heng Tao Shen
| Challenge: | Existing automatic prompt optimization methods fail to optimize prompts and decoding hyperparameters within a unified framework to achieve stable global improvements. |
| Approach: | They propose a dynamic prompt optimization framework for complex reasoning that unifies prompt templates and decodes hyperparameters as inheritable agent configurations. |
| Outcome: | Experiments on multiple mathematical and hybrid reasoning benchmarks show that Agent-GWO improves accuracy and stability over existing prompt optimization methods. |
Semantic-Aware Logical Reasoning via a Semiotic Framework (2026.acl-long)
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Yunyao Zhang, Xinglang Zhang, Junxi Sheng, Wenbing Li, Junqing Yu, Yi-Ping Phoebe Chen, Wei Yang, Zikai Song
| Challenge: | Existing studies largely overlook the interplay between logical complexity and semantic complexity, limiting their robustness under abstract propositions, ambiguous contexts, and conflicting stances. |
| Approach: | They propose a semiotic-square-guided framework that integrates automated deduction with reflective verification to manage logical complexity across deeper reasoning chains. |
| Outcome: | The proposed framework achieves state-of-the-art performance on RepublicQA with 6.25% average gain, and generalizes well to four mainstream logical reasoning benchmarks with an additional 7.05% improvement. |
Do Before You Judge: Self-Reference as a Pathway to Better LLM Evaluation (2025.findings-emnlp)
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| Challenge: | LLM-as-Judge frameworks are increasingly popular for AI evaluation, yet research findings on the relationship between models’ generation and judgment abilities remain inconsistent. |
| Approach: | They propose a self-reference-guided evaluation strategy that leverages a model’s own answers as references to strengthen the correlation between generation and judgment abilities. |
| Outcome: | The proposed approach strengthens the correlation between model generation and judgment abilities and provides a reliable proxy for model selection in evaluation tasks. |
Don’t Forget Your Reward Values: Language Model Alignment via Value-based Calibration (2024.emnlp-main)
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| Challenge: | Existing methods for generating large language models have been criticized for their complexity and instability. |
| Approach: | They propose a value-based calibration method to better align Large Language Models with human preferences. |
| Outcome: | The proposed method surpasses existing methods on AI assistant and summarization datasets, providing impressive generalizability, robustness, and diversity in different settings. |
Nuclear Deployed!: Analyzing Catastrophic Risks in Decision-making of Autonomous LLM Agents (2025.findings-acl)
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| Challenge: | Large language models (LLMs) are evolving into autonomous decision-makers, raising concerns about catastrophic risks in high-stakes domains, particularly in Chemical, Biological, Radiological and Nuclear (CBRN) . |
| Approach: | They propose a framework that is carefully constructed to effectively and naturally expose catastrophic risks in high-stakes domains such as CBRN. |
| Outcome: | The proposed framework exposes LLM agents to catastrophic behaviors and deception without being deliberately induced. |
Zer0-Jack: A memory-efficient gradient-based jailbreaking method for black box Multi-modal Large Language Models (2026.eacl-long)
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| Challenge: | Multi-modal large language models are highly vulnerable to jailbreak attacks due to their additional modality. |
| Approach: | They propose a black-box jailbreak framework based on zeroth-order optimization . they propose generating malicious images and patch-wise block coordinate descent . |
| Outcome: | The proposed framework achieves 98.2% success on MiniGPT-4 and 95% on the Harmful Behaviors Multi-modal dataset while jailbreaking commercial models such as GPT-4o. |
DoubleMix: Simple Interpolation-Based Data Augmentation for Text Classification (2022.coling-1)
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| Challenge: | Existing methods to improve the robustness of text classification models are token-, sentence-, and hiddenlevel augmentation. |
| Approach: | They propose an interpolation-based data augmentation approach called DoubleMix to improve the robustness of text classification models by learning the “shifted” features in hidden space. |
| Outcome: | The proposed approach outperforms several popular methods on six text classification benchmark datasets and visual analysis shows that the model features are highly interpretable. |
Both Matter: Enhancing the Emotional Intelligence of Large Language Models without Compromising the General Intelligence (2024.findings-acl)
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| Challenge: | Emotional Intelligence (EI) is a key concept in the field of human intelligence. |
| Approach: | They propose a method to enhance EI of large language models by naive fine-tuning on EI-related tasks. |
| Outcome: | The proposed method improves EI of two LLM-based assistants without compromising GI. |
A Reinforced Generation of Adversarial Examples for Neural Machine Translation (2020.acl-main)
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| Challenge: | Neural machine translation systems fail on less decent inputs, which may harm the credibility of these systems. |
| Approach: | They propose a paradigm that generates adversarial examples using reinforcement learning to expose pitfalls for a given performance metric. |
| Outcome: | The proposed paradigm produces stable attacks with meaning-preserving adversarial examples. |
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. |
SpeechMedAssist: Efficiently and Effectively Adapting Speech Language Models for Medical Consultation (2026.acl-long)
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| Challenge: | Recent advances in speech language models have enabled more natural speech-based interactions, but the scarcity of medical speech data and the inefficiency of fine-tuning on speech data hinder adoption of SpeechLMs in medical consultation. |
| Approach: | They propose a SpeechLM natively capable of conducting speech-based multi-turn interactions with patients. |
| Outcome: | The proposed model outperforms baselines in both effectiveness and robustness in most evaluation settings. |
VFA: Empowering Multilingual MLLMs via Vision-Free Adaptation (2026.acl-long)
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Yixia Li, Yaqing Shi, Zhiwen Ruan, Dongdong Zhang, Lingjie Jiang, Shaohan Huang, Yun Chen, Guanhua Chen, Furu Wei
| Challenge: | Multimodal large language models have advanced rapidly, yet most remain English-centric . scaling multilingual multimodal instruction tuning is limited by the scarcity and high cost of non-English image–text supervision. |
| Approach: | They propose a framework that decouples multilingual language enhancement from visual alignment by composing complementary task vectors over a shared LLM backbone. |
| Outcome: | The proposed framework achieves competitive performance with a fully multimodally trained model using less than 2% of the text data. |
Lost in Pronunciation: Detecting Chinese Offensive Language Disguised by Phonetic Cloaking Replacement (2025.emnlp-industry)
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Haotan Guo, Jianfei He, Jiayuan Ma, Hongbin Na, Zimu Wang, Haiyang Zhang, Qi Chen, Wei Wang, Zijing Shi, Tao Shen, Ling Chen
| Challenge: | Phonetic Cloaking Replacement (PCR) is a problem in content moderation in China. |
| Approach: | They organize PCR into a four-way surface-form taxonomy and compile PCR-ToxiCN, a dataset of 500 phonetically cloaked offensive posts gathered from the RedNote platform. |
| Outcome: | The proposed model achieves only an F1-score and zero-shot chain-of-thought prompting pushes performance even lower. |
Continual Learning for Neural Machine Translation (2021.naacl-main)
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| Challenge: | Neural machine translation models are data-driven and require large-scale training corpus . continual learning remains a big challenge for artificial intelligence systems and models . |
| Approach: | They propose a continual learning framework for NMT models that incorporates multiple stages of training to alleviate catastrophic forgetting problem. |
| Outcome: | The proposed framework achieves superior performance compared to baseline models in all settings. |
TabPrompt: Graph-based Pre-training and Prompting for Few-shot Table Understanding (2023.findings-emnlp)
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| Challenge: | Existing methods of Table Understanding (TU) focus on the textual content within the tabular data, disregarding the topological information of the table. |
| Approach: | They propose a framework that uses tabs to understand tabular data without ignoring the topological information of the table. |
| Outcome: | The proposed framework outperforms baselines in few-shot table understanding tasks. |
Can Large Language Models Translate Unseen Languages in Underrepresented Scripts? (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have demonstrated impressive performance in machine translation, but struggle with unseen low-resource languages. |
| Approach: | They propose a benchmark to evaluate translation for Mongolian and Yi using linguistic resources. |
| Outcome: | The proposed model can translate Mongolian (in traditional script) and Yi with the help of linguistic resources, but is limited in its ability to handle these languages effectively. |
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. |
Pre-training Language Model as a Multi-perspective Course Learner (2023.findings-acl)
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Beiduo Chen, Shaohan Huang, Zihan Zhang, Wu Guo, Zhenhua Ling, Haizhen Huang, Furu Wei, Weiwei Deng, Qi Zhang
| Challenge: | Experimental results show that our method significantly improves ELECTRA’s average performance by 2.8% and 3.2% absolute points respectively on GLUE and SQuAD 2.0 benchmarks. |
| Approach: | They propose a multi-perspective course learning method to fetch many degrees and visual angles for sample-efficient pre-training and to fully leverage the relationship between generator and discriminator. |
| Outcome: | The proposed method improves ELECTRA's performance on GLUE and SQuAD 2.0 benchmarks and overshadows recent advanced ELECL-style models under the same settings. |
Joint Enhancement of Relational Reasoning for Long-Context LLMs (2025.findings-emnlp)
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| Challenge: | JERR is a graph-based reasoning framework for large language models . it enables LLMs to handle extended contexts with improved reliability and transparency . |
| Approach: | They propose a graph-based reasoning framework that integrates synopsis extraction, graph construction, and relational reasoning. |
| Outcome: | The proposed framework outperforms baselines on ROUGE and F1 metrics and achieves the highest scores on the LLM-Rater evaluation. |
LLM-Based Multi-Agent Systems are Scalable Graph Generative Models (2025.findings-acl)
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Jiarui Ji, Runlin Lei, Jialing Bi, Zhewei Wei, Xu Chen, Yankai Lin, Xuchen Pan, Yaliang Li, Bolin Ding
| Challenge: | Social graphs are mathematical structures stem from pairwise interactions between entities through nodes and edges. |
| Approach: | They propose a framework for dynamic, text-attributed social graph generation that simulates the temporal node and edge generation processes for zero-shot social graphs. |
| Outcome: | The proposed framework improves macroscopic graph structure metrics by 11% . the proposed model can generate graphs with up to 100,000 nodes or 10 million edges . |
Improved Pseudo Data for Machine Translation Quality Estimation with Constrained Beam Search (2023.emnlp-main)
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Xiang Geng, Yu Zhang, Zhejian Lai, Shuaijie She, Wei Zou, Shimin Tao, Hao Yang, Jiajun Chen, Shujian Huang
| Challenge: | evaluating the quality of machine translation outputs becomes increasingly essential with the rapid development of machine language (MT). |
| Approach: | They propose to generate pseudo data using the MT model with constrained beam search (CBSQE) they propose to preserve the reference parts with high MT probabilities as correct translations . |
| Outcome: | The proposed model outperforms strong baselines in both supervised and unsupervised settings. |
Lion: Adversarial Distillation of Proprietary Large Language Models (2023.emnlp-main)
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| Challenge: | Existing methods for transferring knowledge from a proprietary large language model to an open-source LLM have been criticized for not incorporating any feedback to boost student model's proficiency iteratively. |
| Approach: | They propose a novel adversarial distillation framework that integrates feedback to identify challenging instructions where the student model’s performance falls short. |
| Outcome: | The proposed framework surpasses existing state-of-the-art LLMs by 55.4% in challenging zero-shot reasoning benchmarks and 16.7% on AGIEval. |
Exploring the Capability Boundaries of LLMs in Mastering of Chinese Chouxiang Language (2026.findings-acl)
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Dianqing Lin, Tian Lan, Jiali Zhu, Jiang Li, Wei Chen, Xu Liu, null Aruukhan, Xiangdong Su, Hongxu Hou, Guanglai Gao
| Challenge: | Current state-of-the-art LLMs exhibit clear limitations on multiple tasks, while performing well on tasks that involve contextual semantic understanding. |
| Approach: | They propose a mouse-based benchmark to evaluate LLMs' performance on NLP tasks involving Chouxiang Language. |
| Outcome: | The proposed benchmark evaluates the performance of LLMs on six NLP tasks involving Chouxiang Language. |
Ready Jurist One: Benchmarking Language Agents for Legal Intelligence in Dynamic Environments (2026.acl-long)
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| Challenge: | Existing benchmarks for legal intelligence are limited to static evaluation paradigms or simplified scenarios. |
| Approach: | They introduce J1-ENVS, the first interactive and dynamic legal environment tailored for LLM-based agents. |
| Outcome: | The proposed framework assesses task performance and procedural compliance across legal proficiency levels. |
Improving the Efficiency of Grammatical Error Correction with Erroneous Span Detection and Correction (2020.emnlp-main)
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| Challenge: | Existing methods to improve the efficiency of GEC are not efficient enough for GEC. |
| Approach: | They propose a language-independent approach to improve the efficiency of GEC by dividing the task into two subtasks: ESD and ESC. |
| Outcome: | The proposed approach performs comparably to conventional seq2seq approaches in English and Chinese GEC benchmarks with less than 50% time cost for inference. |
Summarizing Chinese Medical Answer with Graph Convolution Networks and Question-focused Dual Attention (2020.findings-emnlp)
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| Challenge: | Existing approaches to generate answer summarization for medical questions are not straightforward to apply to the medical domain. |
| Approach: | They propose an approach that utilizes graph convolution networks and question-focused dual attention for Chinese medical answer summarization. |
| Outcome: | The proposed model generates more coherent and informative summaries compared with baseline models. |
Memory-Efficient Differentiable Transformer Architecture Search (2021.findings-acl)
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| Challenge: | Current neural architecture search methods suffer from huge computational cost. |
| Approach: | They propose a reversible recursive backpropagation algorithm that uses the last layer to store the outputs of the network. |
| Outcome: | The proposed algorithm outperforms standard Transformers on three sequence-to-sequence datasets. |
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation (2022.lrec-1)
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Wenhao Zhu, Shujian Huang, Tong Pu, Pingxuan Huang, Xu Zhang, Jian Yu, Wei Chen, Yanfeng Wang, Jiajun Chen
| Challenge: | Recent research on domain adaptation neglects diversity in translation within a domain . current research on NMT models considers very broad target domains . |
| Approach: | They propose a fine-grained domain adaptation task for autonomous vehicles, AI education, real-time networks, and smart phone. |
| Outcome: | The proposed task is compared with a dataset of Chinese-English translation tasks for four sub-domains of information technology: autonomous vehicles, AI education, real-time networks, and smart phone. |
PAPERMIND: Benchmarking Agentic Reasoning and Critique over Scientific Papers in Multimodal LLMs (2026.findings-acl)
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Yanjun Zhao, Tianxin Wei, Jiaru Zou, Xuying Ning, Yuanchen Bei, Lingjie Chen, Simmi Rana, Wendy H. Yang, Hanghang Tong, Jingrui He
| Challenge: | Existing benchmarks assess integrated and agent-oriented scientific reasoning in isolation . Existing systems assess integrated reasoning in isolated tasks . |
| Approach: | They propose a benchmark to evaluate integrated and agent-oriented scientific reasoning over research papers. |
| Outcome: | The proposed benchmark evaluates integrated and agent-oriented scientific reasoning over scientific papers. |
INarIG: Iterative Non-autoregressive Instruct Generation Model For Word-Level Auto Completion (2023.findings-emnlp)
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| Challenge: | Existing models for word-level autocompletion (WLAC) only use human typed sequences as prefixes in decoding module. |
| Approach: | They propose a novel iterative nonautoregressive instruct generation model for WLAC task . it uses human typed sequences and iterating decoding with subwords to fully utilize input information. |
| Outcome: | The proposed model is more competent in dealing with low-frequency words, and achieves state-of-the-art results on the WMT22 and benchmark datasets. |
C3KG: A Chinese Commonsense Conversation Knowledge Graph (2022.findings-acl)
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| Challenge: | Existing commonsense knowledge bases organize tuples in an isolated manner, causing problems for chatbots . |
| Approach: | They create a Chinese commonsense conversation knowledge graph which integrates social commonsensm and dialog flow information. |
| Outcome: | The proposed graph incorporates social commonsense knowledge and dialog flow information. |
Analyzing Code Embeddings for Coding Clinical Narratives (2021.findings-acl)
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| Challenge: | Recent work on automated ICD coding learn mappings between low-dimensional representations of clinical text reports and codes. |
| Approach: | They propose novel neural networks for encoding medical codes based on textual, structural and statistical characteristics using a single deep learning baseline model. |
| Outcome: | The proposed methods improve the accuracy of medical codes based on their textual, structural and statistical characteristics. |
AI-Press: A Multi-Agent News Generating and Feedback Simulation System Powered by Large Language Models (2025.coling-demos)
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Xiawei Liu, Shiyue Yang, Xinnong Zhang, Haoyu Kuang, Libo Sun, Yihang Yang, Siming Chen, Xuanjing Huang, Zhongyu Wei
| Challenge: | We introduce AI-Press, an automated news drafting and polishing system based on multi-agent collaboration and Retrieval-Augmented Generation. |
| Approach: | They introduce AI-Press, an automated news drafting and polishing system based on multi-agent collaboration and Retrieval-Augmented Generation. |
| Outcome: | The proposed system generates public responses considering demographic distributions. |
A Structure-Aware Argument Encoder for Literature Discourse Analysis (2022.coling-1)
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Yinzi Li, Wei Chen, Zhongyu Wei, Yujun Huang, Chujun Wang, Siyuan Wang, Qi Zhang, Xuanjing Huang, Libo Wu
| Challenge: | Existing research for argument representation learning treats tokens in sentences equally and ignores the implied structure information of argumentative context. |
| Approach: | They propose to separate tokens into two groups to capture structural information of arguments and to incorporate paragraph-level position information into the model. |
| Outcome: | The proposed model captures structural information of arguments and is able to identify arguments automatically. |
EchoVLM: Dynamic Mixture-of-Experts Vision-Language Model for Universal Ultrasound Intelligence (2026.acl-long)
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| Challenge: | Ultrasound is the preferred early cancer screening modality due to non-ionizing radiation, cost-effectiveness, and real-time imaging. |
| Approach: | They propose to use ultrasound-tailored vision-language models with a mixture-of-experts architecture to train ultrasound-specific knowledge across seven anatomical systems. |
| Outcome: | The proposed model outperforms Qwen2-VL by 7.58 BLEU-1 and 3.45 ROUGE-1 points in report generation. |
CoMIF: Modeling of Complex Multiple Interaction Factors for Conversation Generation (2025.coling-main)
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| Challenge: | Existing methods for generating human-like dialogues lack implicit correlations among factors . different factors may alternately dominate token-level response generation during decoding . |
| Approach: | They propose a framework that can model complex multiple interaction factors to generate human-like conversations. |
| Outcome: | The proposed framework generates human-like conversations with multiple factors compared to state-of-the-art methods . et al. show that the proposed framework produces superior results over existing methods compared with the state- of-the art methods based on multiple datasets . |
DGLF: A Dual Graph-based Learning Framework for Multi-modal Sarcasm Detection (2024.emnlp-main)
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Zhihong Zhu, Kefan Shen, Zhaorun Chen, Yunyan Zhang, Yuyan Chen, Xiaoqi Jiao, Zhongwei Wan, Shaorong Xie, Wei Liu, Xian Wu, Yefeng Zheng
| Challenge: | Existing methods for multimodal sarcasm detection neglect high-order relationships and underestimate high-frequency messages. |
| Approach: | They propose a Dual Graph-based Learning Framework to capture inter-modal inconsistencies . they propose combining a hypergraph and a vanilla graph to achieve enhanced propagation . |
| Outcome: | The proposed model outperforms existing state-of-the-art methods on two benchmark datasets. |
Diagnosing and Remedying Representation Deficiencies for Deterministic Reasoning in KGQA (2026.acl-long)
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| Challenge: | Existing approaches to solve non-deterministic reasoning problems in large language models are limited by their complexity and lack of a clear understanding of the problem. |
| Approach: | They propose a method to diagnose and correct non-deterministic reasoning behaviors in large language models. |
| Outcome: | The proposed method outperforms baselines and WebQSP benchmarks on the widely used WebQ SP and CWQ benchmarks. |
Toward Safe and Human-Aligned Game Conversational Recommendation via Multi-Agent Decomposition (2026.findings-eacl)
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| Challenge: | Existing systems for conversational recommender systems (CRS) have strong results in movies, but games present distinct challenges . MATCHA framework provides specialized agents for intent parsing, tool-augmented retrieval, multi-LLM ranking, and stronger safety. |
| Approach: | They propose a framework for conversational recommender systems that assigns specialized agents for intent parsing, tool-augmented retrieval, multi-LLM ranking and risk control. |
| Outcome: | MATCHA outperforms baselines on real user request dataset, improves Hit@5 by 20%, reduces popularity bias by 24%, and achieves 97.9% adversarial defense. |
From Long Videos to Engaging Clips: A Human-Inspired Video Editing Framework with Multimodal Narrative Understanding (2025.emnlp-industry)
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Xiangfeng Wang, Xiao Li, Yadong Wei, null Songxueyu, Yang Song, null Xiaxiaoqiang, Fangrui Zeng, Zaiyi Chen, null Liuliu, Gu Xu, Tong Xu
| Challenge: | Existing methods for video editing rely on textual cues from ASR transcripts and segment selection, often neglecting rich visual context. |
| Approach: | They propose a human-inspired automatic video editing framework that leverages multimodal narrative understanding to address these limitations. |
| Outcome: | The proposed framework outperforms existing baselines across general and advertisement-oriented editing tasks. |
HCL-TAT: A Hybrid Contrastive Learning Method for Few-shot Event Detection with Task-Adaptive Threshold (2022.findings-emnlp)
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| Challenge: | Existing methods for event detection have failed to address the problem of constantly emerging event types with limited data. |
| Approach: | They propose a novel method for event detection with a task-adaptive threshold . they propose to learn discriminative representations with 'two-view contrastive loss' |
| Outcome: | The proposed method achieves better results than the state-of-the-art methods on a benchmark dataset. |
UIOrchestra: Generating High-Fidelity Code from UI Designs with a Multi-agent System (2025.findings-emnlp)
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Chuhuai Yue, Jiajun Chai, Yufei Zhang, Zixiang Ding, Xihao Liang, Peixin Wang, Shihai Chen, Wang Yixuan, null Wangyanping, Guojun Yin, Wei Lin
| Challenge: | Recent advances in large language models have significantly improved automated code generation . however, the translation of complex mobile UI designs into high-fidelity front-end code remains a challenge . |
| Approach: | They propose a collaborative multi-agent system to reconstruct static single-page apps from mockups. |
| Outcome: | The proposed system outperforms existing methods in reconstructing complex app pages . the code and data will be released upon paper acceptance . |
Natural-Language Policies to Executable Decisions: An Interpretable Large Language Model Framework (2026.acl-industry)
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Ziqiang Zhang, Jing Ma, Zilong Wang, Jiayuan Chen, Yi Qiao, Yu He, Wei Zhang, Dai Cheng, Xiaoyu Shen
| Challenge: | a production-grade pricing system for tourism is challenging due to unstructured nature of travel orders and ever-evolving pricing policies. |
| Approach: | They propose a production-grade pricing system with a strict decision boundary . they propose to combine structured extraction and bounded policy/path selection with interpretable condition trees . |
| Outcome: | The proposed system processed 3,960 orders in six months and reduced the order management team from 15-20 to 3 . the system reduced the per-order handling time from 10 minutes to 2 minutes. |
MARK: Multi-agent Collaboration with Ranking Guidance for Text-attributed Graph Clustering (2025.findings-acl)
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Yiwei Fu, Yuxing Zhang, Chunchun Chen, JianwenMa JianwenMa, Quan Yuan, Rong-Cheng Tu, Xinli Huang, Wei Ye, Xiao Luo, Minghua Deng
| Challenge: | Existing approaches to cluster graphs with GNNs are limited due to label scarcity. |
| Approach: | They propose to leverage large language models to enhance text-attributed graph clustering by using three LLMs as ranking-based supervision signals. |
| Outcome: | The proposed approach generates reliable guidance using collaboration of three LLM-based agents as ranking-based supervision signals. |
SWE-Swiss: A Multi-Task Fine-Tuning and RL Recipe for High-Performance Issue Resolution (2026.findings-acl)
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Zhenyu He, Qingping Yang, Wei Shen, Xiaojian Zhong, Kechi Zhang, Chenxin An, Wenlei Shi, Tianle Cai, Di He, Jiaze Chen, Jingjing Xu
| Challenge: | SWE-Swiss-32B demonstrates strong generalization to other common LLM benchmarks. |
| Approach: | They propose a two-phase training recipe that decomposes issue resolution into three core skills: Localization, Repair, and Unit Test Generation. |
| Outcome: | The proposed model achieves a 60.2% score on the SWE-bench Verified benchmark and is in the top-tier performance bracket of much larger models. |
Event Extraction as Machine Reading Comprehension (2020.emnlp-main)
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| Challenge: | Event extraction (EE) is a crucial information extraction task that aims to extract event information in texts. |
| Approach: | They propose a new learning paradigm for event extraction by explicitly casting it as a machine reading comprehension problem. |
| Outcome: | The proposed model achieves state-of-the-art performance on the data-scarce scenario, achieving 49.8% in F1 for event argument extraction with only 1% data, compared with 2.2% of the previous method. |
Reasoning in the Dark: Interleaved Vision-Text Reasoning in Latent Space (2026.findings-acl)
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| Challenge: | Existing multimodal reasoning methods depend on explicit reasoning steps that require labor-intensive vision-text annotations and inherently introduce significant inference latency. |
| Approach: | They propose a method that integrates visual and visual information into the reasoning process to improve the performance of multimodal LLMs. |
| Outcome: | The proposed method achieves an average performance increase of 5.45% while achieving a speed increase of over 5 times compared to existing methods. |
R.R.: Unveiling LLM Training Privacy through Recollection and Ranking (2025.findings-acl)
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| Challenge: | Existing privacy attacks focus on membership inference or data extraction, but reconstructing specific personally identifiable information (PII) in training data remains challenging. |
| Approach: | They propose a two-step privacy stealing attack that enables attackers to reconstruct PII entities from scrubbed training data where the PI I entities have been masked. |
| Outcome: | The proposed attack can reconstruct PII entities from scrubbed training data where the PI I entities have been masked. |
Pruning as a Domain-specific LLM Extractor (2024.findings-naacl)
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Nan Zhang, Yanchi Liu, Xujiang Zhao, Wei Cheng, Runxue Bao, Rui Zhang, Prasenjit Mitra, Haifeng Chen
| Challenge: | Large Language Models (LLMs) have exhibited remarkable proficiency across a wide array of NLP tasks. |
| Approach: | They propose a method for pruning large language models using general or task-specific weights to extract a compressed, task-agnostic LLM. |
| Outcome: | The proposed method extracts a compressed, domain-specific, and task- agnostic LLM by identifying LLM weights that are pivotal for general capabilities, like linguistic capability and multi-task solving, and domain- specific knowledge. |
Task-oriented Dialogue System for Automatic Diagnosis (P18-2)
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Zhongyu Wei, Qianlong Liu, Baolin Peng, Huaixiao Tou, Ting Chen, Xuanjing Huang, Kam-fai Wong, Xiangying Dai
| Challenge: | Existing methods to identify phenotypes using electronic health records (EHRs) are expensive and difficult to transfer models from one disease to another. |
| Approach: | They propose a task-oriented dialogue system framework to make diagnosis for patients automatically, which can converse with patients to collect additional symptoms beyond their self-reports. |
| Outcome: | The proposed system can collect additional symptoms from conversation and improve disease identification accuracy. |
Structure-aware Domain Knowledge Injection for Large Language Models (2025.acl-long)
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| Challenge: | Structure-aware Continual Pre-Training (SCPT) and Structure-Aware Supervised Fine-Tuning (SSFT) are two-stage strategies for knowledge injection and alignment that reduces the training corpus needs to 5% while achieving 100% of traditional knowledge injection performance. |
| Approach: | They propose a method to efficiently transform foundation Large Language Models into domain specialists by using two-stage strategies: Structure-aware Continual Pre-Training and Structure-Aware Supervised Fine-Tuning. |
| Outcome: | The proposed method significantly reduces the training corpus needs to a mere 5% while achieving 100% of traditional knowledge injection performance. |
Simple-VGC: Enhancing Visual Grounding in Multimodal Reasoning via Adaptive Tool Composition (2026.acl-long)
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| Challenge: | Existing multimodal large language models suffer from systematic failures in basic visual understanding. |
| Approach: | They propose a tool-augmented reasoning framework with three targeted compensation strategies to address these problems. |
| Outcome: | The proposed framework improves visual grounding by re-injecting the original image to mitigate visual forgetting, the authors show . the proposed framework also improves the accuracy of the visual inputs, the researchers show - and the results are promising . |
PREMISE: Matching-based Prediction for Accurate Review Recommendation (2025.findings-naacl)
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| Challenge: | Experimental results show that PREMISE achieves promising performance with less computational cost. |
| Approach: | They propose a new architecture for matching-based learning in multimodal fields for the MRHP task. |
| Outcome: | The proposed architecture significantly boosts performance on multimodal tasks with less computational cost compared to the state-of-the-art fusion-based methods. |
Dynamic Transformers Provide a False Sense of Efficiency (2023.acl-long)
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| Challenge: | Pre-trained language models typically lead to high computational cost during inference. |
| Approach: | They propose a slowdown attack framework that can reduce inference efficiency by 80% by leveraging existing adversarial attacks targeting model accuracy. |
| Outcome: | The proposed framework can reduce the efficiency of multi-exit models by 80% on average, validating its effectiveness and generalization ability. |
Visualization Recommendation with Prompt-based Reprogramming of Large Language Models (2024.acl-long)
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| Challenge: | Traditional visualization recommendations require extensive manual maintenance and yet fail to fully comprehend tabular data. |
| Approach: | They propose a hierarchical table prompt-based reprogramming framework that integrates tabular data into LLMs through a strategically crafted prompt learning method. |
| Outcome: | The proposed framework achieves state-of-the-art performance and will be made publicly available upon acceptance. |
ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)
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Ruibin Yuan, Hanfeng Lin, Yi Wang, Zeyue Tian, Shangda Wu, Tianhao Shen, Ge Zhang, Yuhang Wu, Cong Liu, Ziya Zhou, Liumeng Xue, Ziyang Ma, Qin Liu, Tianyu Zheng, Yizhi Li, Yinghao Ma, Yiming Liang, Xiaowei Chi, Ruibo Liu, Zili Wang, Chenghua Lin, Qifeng Liu, Tao Jiang, Wenhao Huang, Wenhu Chen, Jie Fu, Emmanouil Benetos, Gus Xia, Roger Dannenberg, Wei Xue, Shiyin Kang, Yike Guo
| Challenge: | Despite LLMs' impressive capabilities in musical knowledge, music reasoning remains an unsolved task. |
| Approach: | They propose an open-source large language model (LLM) that integrates intrinsic musical abilities into LLaMA2 and GPT-3.5. |
| Outcome: | The proposed model can understand and generate music with a pure text tokenizer without external multi-modal neural structures or tokenizers. |
Cogito: A Cognitive Agentic Framework Driven by Dynamic Graph of Thoughts for Financial Report Generation (2026.findings-acl)
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| Challenge: | Existing approaches to financial report generation are insufficient to handle dynamic uncertainties of real-world financial environments. |
| Approach: | They propose a cognitively grounded agentic framework for professional financial report generation that is driven by Dynamic Graph of Thoughts and a social collaboration mechanism to facilitate coordinated agent interaction. |
| Outcome: | The proposed framework is based on a dynamic reasoning model and social collaboration mechanism. |
Controllable Natural Language Generation with Contrastive Prefixes (2022.findings-acl)
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| Challenge: | Existing work on controllable natural language generation has focused on fine-tuning existing models or using attribute discriminators. |
| Approach: | They propose a lightweight framework for controllable GPT2 generation that utilizes attribute-specific vectors to steer natural language generation. |
| Outcome: | The proposed framework can guide generation towards desired attributes while keeping high linguistic quality. |
EduMARS: Can Vision-Language Models Grade Like Teachers? Benchmarking Multimodal, Rubric-Based Assessment on Chinese K-12 Answers (2026.findings-acl)
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| Challenge: | Existing benchmarks for automated grading of student work fail to evaluate real student responses . existing models fail to assess real student work, especially on cognitively demanding tasks . |
| Approach: | They propose a multimodal benchmark for rubric-aligned evaluation of real Chinese K-12 student answers. |
| Outcome: | The proposed model improves performance and interpretability of existing models on EduMARS . existing models fail to perform on real-world, cognitively demanding tasks, authors say . |
Reward Modeling Requires Automatic Adjustment Based on Data Quality (2024.findings-emnlp)
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Binghai Wang, Rui Zheng, Lu Chen, Zhiheng Xi, Wei Shen, Yuhao Zhou, Dong Yan, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Reinforcement Learning from Human Feedback (RLHF) is a method for aligning language models with human values. |
| Approach: | They propose a method that automatically adjusts reward modeling based on data quality . they use preference data to train a reward model that is more aligned with human values . |
| Outcome: | The proposed method stabilizes reward model training and significantly improves alignment performance on human preference datasets. |
Double-Checker: Large Language Model as a Checker for Few-shot Named Entity Recognition (2024.findings-emnlp)
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| Challenge: | Recent studies have demonstrated remarkable performance on few-shot Named Entity Recognition tasks due to the high cost of obtaining high-quality labeled data. |
| Approach: | They propose to decompose the task into entity span detection and entity type classification using a type-independent entity span detector and then classify the detected spans based on their types. |
| Outcome: | The proposed method consistently yields improvements over two baseline approaches. |
Local Interpretation of Transformer Based on Linear Decomposition (2023.acl-long)
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| Challenge: | Existing work on local explanation generation attempts to understand model dynamics on word-level or phraselevel by assigning importance scores on input features. |
| Approach: | They propose to interpret neural networks by linear decomposition by a Transformer model on a single input and a linear decomposing of the output to generate local explanations. |
| Outcome: | The proposed method achieves competitive performance in sentiment classification and machine translation, and fidelity of explanation. |
Open Set Relation Extraction via Unknown-Aware Training (2023.acl-long)
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Jun Zhao, Xin Zhao, WenYu Zhan, Qi Zhang, Tao Gui, Zhongyu Wei, Yun Wen Chen, Xiang Gao, Xuanjing Huang
| Challenge: | Existing supervised relation extraction methods can still misclassify unknown relations into known relations due to the lack of supervision signals. |
| Approach: | They propose a method that regularizes the model by dynamically synthesizing negative instances that can provide the missing supervision signals. |
| Outcome: | The proposed method achieves SOTA unknown relation detection without compromising the classification of known relations. |
Bootstrapping Code Translation with Weighted Multilanguage Exploration (2026.acl-long)
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| Challenge: | Existing methods to improve code translation depend on abundant parallel code of high quality, which may not always be available. |
| Approach: | They propose a method that leverages functional invariance and cross-lingual portability of test suites to serve as universal verification oracles for multilingual reinforcement learning. |
| Outcome: | The proposed method leverages functional invariance and cross-lingual portability of test suites to serve as universal verification oracles for multilingual reinforcement learning (RL) training. |
Jailbreak LLMs through Internal Stance Manipulation (2025.emnlp-main)
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| Challenge: | Existing approaches to exploit LLMs' inherent safety mechanism, including GCG and AutoDAN, are ineffective for certain malicious requests. |
| Approach: | They propose a method that generates jailbreak prompts to suppress a refusal stance and induce affirmative responses by modifying adversarial prompts. |
| Outcome: | The proposed method outperforms the best baseline approach in Llama-2-7b-chat and achieves a 92.2% success rate across all models. |
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. |
You Never Know a Person, You Only Know Their Defenses: Detecting Levels of Psychological Defense Mechanisms in Supportive Conversations (2026.findings-acl)
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Hongbin Na, Zimu Wang, Zhaoming Chen, Peilin Zhou, Yining Hua, Grace Ziqi Zhou, Haiyang Zhang, Tao Shen, Wei Wang, John Torous, Shaoxiong Ji, Ling Chen
| Challenge: | Psychological defenses are strategies people use to manage distress. |
| Approach: | They propose a dialogue corpus with help seeker utterances labeled for defense level and a DMRS Co-Pilot pipeline that provides evidence-based pre-annotations. |
| Outcome: | The proposed framework reduces annotation time by 24.0% in a counterbalanced study. |
SynGhost: Invisible and Universal Task-agnostic Backdoor Attack via Syntactic Transfer (2025.findings-naacl)
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| Challenge: | Existing attacks are classified into end-to-end and pre-training types based on the attack phase . Existing backdoor attacks are based upon perplexity, fine-pruning, and maxEntropy. |
| Approach: | They propose an entropy-based poisoning filter that mitigates backdoor attacks . they propose an invisible and universal task-agnostic backdoor attack via syntactic transfer . |
| Outcome: | The proposed attack can transfer backdoors to various downstream tasks while preserving pre-trained language models' pre-training capabilities. |
SoMeLVLM: A Large Vision Language Model for Social Media Processing (2024.findings-acl)
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Xinnong Zhang, Haoyu Kuang, Xinyi Mou, Hanjia Lyu, Kun Wu, Siming Chen, Jiebo Luo, Xuanjing Huang, Zhongyu Wei
| Challenge: | Genereal domain large models lack nuanced multimodal understanding of social media . general domain models focus more on text than other modalities, which is not consistent with real-world user habits. |
| Approach: | They propose a Large Vision Language Model for Social Media Processing that combines five key capabilities to understand and generate real social media behavior. |
| Outcome: | The proposed model achieves state-of-the-art performance in multiple social media tasks. |
Keywords and Instances: A Hierarchical Contrastive Learning Framework Unifying Hybrid Granularities for Text Generation (2022.acl-long)
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Mingzhe Li, XieXiong Lin, Xiuying Chen, Jinxiong Chang, Qishen Zhang, Feng Wang, Taifeng Wang, Zhongyi Liu, Wei Chu, Dongyan Zhao, Rui Yan
| Challenge: | Existing studies focus on contrastive learning on the instance level without discriminating the contribution of each word. |
| Approach: | They propose a hierarchical contrastive learning mechanism which can unify semantic meaning in the input text. |
| Outcome: | The proposed model outperforms baselines on storytelling, paraphrasing, dialogue generation, and storytelling tasks. |
End-to-End Optimization of LLM-Driven Multi-Agent Search Systems via Heterogeneous-Group-Based Reinforcement Learning (2026.acl-long)
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| Challenge: | Existing multi-agent reinforcement learning methods depend on large critic networks to evaluate joint actions, leading to instability and high memory costs. |
| Approach: | They propose a method to optimize large language models for agent-specific roles . they propose combining agent-based frameworks with retrieval-augmented generation . |
| Outcome: | Experiments show that multi-agent group policy optimization outperforms baselines in task performance and computational efficiency. |
VIRT: Improving Representation-based Text Matching via Virtual Interaction (2022.emnlp-main)
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| Challenge: | Experimental results show that representation-based text matching methods suffer from performance degradation due to the lack of interactions between the pair of texts. |
| Approach: | They propose a virtual interaction mechanism that enables deep interaction between texts . they propose 'inteRacTion mechanism' that can be integrated into existing methods as plugins . |
| Outcome: | The proposed method outperforms state-of-the-art models on six text matching benchmarks. |
Exploring Label Hierarchy in a Generative Way for Hierarchical Text Classification (2022.coling-1)
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| Challenge: | Existing methods for hierarchical text classification are lacking in the field of natural language processing. |
| Approach: | They propose a hierarchy-aware T5 model with path-adaptive attention mechanism to exploit hierarchical dependency across different levels. |
| Outcome: | The proposed model outperforms state-of-the-art models especially in Macro-F1 and low Macro. |
THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption (2022.findings-acl)
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Tianyu Chen, Hangbo Bao, Shaohan Huang, Li Dong, Binxing Jiao, Daxin Jiang, Haoyi Zhou, Jianxin Li, Furu Wei
| Challenge: | enabling pre-trained models inference on ciphertext data is difficult due to the complex computations in transformer blocks. |
| Approach: | They propose an approximation approach for transformers which enables inference on ciphertext data. |
| Outcome: | The proposed approach can infer pre-trained models on encrypted data with negligible performance drop but enjoy theory-guaranteed privacy-preserving advantage. |
Human-in-the-loop Evaluation for Early Misinformation Detection: A Case Study of COVID-19 Treatments (2023.acl-long)
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| Challenge: | Existing evaluations of human-in-the-loop systems to combat misinformation are often set up automatically using datasets that were retrospectively constructed. |
| Approach: | They propose a human-in-the-loop evaluation framework for fact-checking novel misinformation claims and identifying social media messages that support them. |
| Outcome: | The proposed framework is based on modern NLP methods for human-in-the-loop fact-checking in the domain of COVID-19 treatments. |
DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction (P19-1)
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| Challenge: | Existing methods for labeling relational facts require significant expert labor to write relation-specific patterns, which makes them too sophisticated to generalize quickly. |
| Approach: | They propose a neural pattern diagnosis framework that can summarize and refine relation-specific patterns with human experts in the loop. |
| Outcome: | The proposed framework can summarize and refine high-quality relational patterns from noise data with human experts in the loop. |
InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews (2024.acl-long)
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Xintao Wang, Yunze Xiao, Jen-tse Huang, Siyu Yuan, Rui Xu, Haoran Guo, Quan Tu, Yaying Fei, Ziang Leng, Wei Wang, Jiangjie Chen, Cheng Li, Yanghua Xiao
| Challenge: | Existing methods focus on knowledge and linguistic patterns of characters. |
| Approach: | They propose to evaluate character fidelity of role-playing agents with psychological scales . they propose to use psychological scale to measure personality traits of RPAs based on personality traits. |
| Outcome: | The proposed model reproduces character fidelity with psychological scales and shows that it is effective in measuring personality traits. |
Personalized Topic Selection Model for Topic-Grounded Dialogue (2024.findings-acl)
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| Challenge: | Existing topic-grounded dialogue systems tend to predict user-uninteresting and contextually irrelevant topics due to noise within side information sources. |
| Approach: | They propose a personalized topic selection model for topic-grounded dialogue that selectively aggregates side information to generate engaging responses. |
| Outcome: | The proposed model outperforms state-of-the-art models on multiple evaluation metrics. |
XPrompt: Exploring the Extreme of Prompt Tuning (2022.emnlp-main)
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| Challenge: | Prompt tuning learns soft prompts to condition pre-trained Language Models for performing downstream tasks in a parameter-efficient manner. |
| Approach: | They propose a Prompt tuning model with an eXtremely small scale that learns soft prompts to condition the frozen Pre-trained Language Models for performing downstream tasks in a parameter-efficient manner. |
| Outcome: | The proposed model outperforms the vanilla Prompt-Tuning and can significantly improve across tasks and model scales. |
Unveiling Privacy Risks in Multi-modal Large Language Models: Task-specific Vulnerabilities and Mitigation Challenges (2025.findings-acl)
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| Challenge: | Privacy risks in text-only Large Language Models are well-documented, especially their tendency to memorize and leak sensitive information. |
| Approach: | They propose a dataset to assess privacy risks across multi-modal tasks and scenarios . they demonstrate how models leak sensitive data across various tasks . |
| Outcome: | The proposed model can leak sensitive data embedded in images or stored in memory, exposing privacy risks. |
SeqAR: Jailbreak LLMs with Sequential Auto-Generated Characters (2025.naacl-long)
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| Challenge: | Existing studies have focused on the potential misuse of large language models (LLMs) however, the ability to align LLMs with human values is still vulnerable to malicious attacks. |
| Approach: | They propose a red-teaming strategy to enhance LLM safety by using a framework to design jailbreak prompts automatically. |
| Outcome: | The proposed framework achieves attack success rates of 88% and 60% in cold-start scenarios. |
Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages (2023.emnlp-main)
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| Challenge: | Existing methods for zero-shot CoT are limited to a single language, making it difficult to generalize to other languages and hindering global development. |
| Approach: | They introduce cross-lingual prompting (CLP) to improve zero-shot CoT reasoning across languages. |
| Outcome: | The proposed method outperforms existing prompting methods on several benchmarks. |
Discrete Cross-Modal Alignment Enables Zero-Shot Speech Translation (2022.emnlp-main)
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| Challenge: | Existing zero-shot methods fail to align speech and text into a shared semantic space . Existing methods require expensive and expensive parallel ST data . |
| Approach: | They propose a method that uses a shared discrete vocabulary space to align speech and text into a common space. |
| Outcome: | The proposed method significantly improves the SOTA and even performs on par with the strong supervised ST baselines. |
V-GameGym: Visual Game Generation for Code Large Language Models (2026.findings-acl)
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Wei Zhang, Jian Yang, Renshuai Tao, Linzheng Chai, Shuyue Guo, Jiajun Wu, Xiaoming Chen, Ganqu Cui, Ning Ding, Xander Xu, HU Wei, Bowen Zhou
| Challenge: | Existing code-related benchmarks focus on single modality rather than visual game development. |
| Approach: | They propose a multimodal benchmark for evaluating code large language models in visual game generation that integrates a clustering-based curation methodology and a pipeline for visual code synthesis. |
| Outcome: | The proposed framework assesses code generation and visual game generation using a sandbox environment. |
Multi-Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering (P18-1)
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| Challenge: | Existing approaches to read comprehension style question answering are limited by the volume of annotated datasets. |
| Approach: | They propose a hierarchical attention network for reading comprehension style question answering . they first encode the question and paragraph with fine-grained language embeddings . then propose fusion approach to fuse information from both global and attended representations based on the hierarchic attention network . |
| Outcome: | The proposed method achieves state-of-the-art on the SQuAD and TriviaQA Wiki leaderboards and two adversarial SQu AD datasets. |
IGenBench: Benchmarking the Reliability of Text-to-Infographic Generation (2026.acl-long)
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Yinghao Tang, Xueding Liu, Boyuan Zhang, Tingfeng Lan, Yupeng Xie, Jiale Lao, Yiyao Wang, Haoxuan Li, Tingting Gao, Bo Pan, Luoxuan Weng, Xiuqi Huang, Minfeng Zhu, Yingchaojie Feng, Yuyu Luo, Wei Chen
| Challenge: | Generated infographics may appear correct at first glance but contain easily overlooked issues, such as distorted data encoding or incorrect textual content. |
| Approach: | They propose to evaluate reliability of text-to-infographic generation using IGenBench . they employ multimodal large language models to verify each question . |
| Outcome: | The proposed framework decomposes reliability verification into atomic yes/no questions based on a taxonomy of 10 question types. |
Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding (D18-1)
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| Challenge: | Existing methods to generate large scale labeled data for relation extraction produce noisy relation labels when there are multiple relationships between entities. |
| Approach: | They propose a method which assumes that a pair of entities appears in a Knowledge Graph and trains a relation classifier. |
| Outcome: | The proposed method performs well in the current distant supervision dataset. |
Don’t Click That: Teaching Web Agents to Resist Deceptive Interfaces (2026.acl-long)
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| Challenge: | Existing approaches to deception detection and defenses are inadequate . Existing methods do not integrate with agent decision-making . |
| Approach: | They propose a framework that integrates hybrid-reward learning with asymmetric penalties and experience summarization to distill failure patterns into transferable guidance. |
| Outcome: | The proposed framework reduces deception susceptibility by 53.8% while maintaining task performance, establishing an effective foundation for robust web agent deployment. |
SMART: Semantic Header Flattening and Pseudo-Code-Style Reasoning for LLM-based Complex Table Question Answering (2026.findings-acl)
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| Challenge: | Existing approaches to complex table question answering rely on handcrafted table linearization or prompts . Existing methods rely only on hand-crafted table and require hierarchical hierarchies to align conditions, attributes, and values. |
| Approach: | They propose a framework that explicitly decouples table structure understanding from reasoning execution. |
| Outcome: | Experiments show that SMART improves accuracy and robustness of complex table question answering (TQA) . SMart decouples table structure understanding from reasoning execution, enabling state-of-the-art performance. |
MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding (2023.emnlp-main)
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Steven Wang, Antoine Scardigli, Leonard Tang, Wei Chen, Dmitry Levkin, Anya Chen, Spencer Ball, Thomas Woodside, Oliver Zhang, Dan Hendrycks
| Challenge: | Merger Agreement Understanding Dataset (MAUD) is an expert-annotated reading comprehension dataset based on the American Bar Association's 2021 Public Target Deal Points Study. |
| Approach: | They propose a Merger Agreement Understanding Dataset with over 39,000 examples and over 47,000 annotations. |
| Outcome: | The Merger Agreement Understanding Dataset (MAUD) is an expert-annotated reading comprehension dataset based on the American Bar Association's 2021 Public Target Deal Points Study. |
Dr.ECI: Infusing Large Language Models with Causal Knowledge for Decomposed Reasoning in Event Causality Identification (2025.coling-main)
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| Challenge: | Existing solutions lack generalizability to unseen domains, underscoring the urgent need for generalization capabilities in the field of ECI. |
| Approach: | They propose a multi-agent Decomposed reasoning framework for Event Causality Identification that incorporates specialized agents such as Causal Explorer and Mediator Detector. |
| Outcome: | The proposed framework improves the state-of-the-art performance of LLMs for event causality identification (ECI) tasks compared with baselines based on LLM and supervised training. |
TokenSelect: Efficient Long-Context Inference and Length Extrapolation for LLMs via Dynamic Token-Level KV Cache Selection (2025.emnlp-main)
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| Challenge: | Rapid advances in Large Language Models have spurred demand for processing extended context sequences . however, performance degradation due to sequence lengths out-of-distribution and excessively long inference times are limiting LLMs in long-context scenarios. |
| Approach: | They propose a training-free method for efficient and accurate long-context inference . they selectively involves a few critical KV cache tokens in attention calculation . |
| Outcome: | The proposed method speeds up attention computation and accelerates inference time while reducing selection overhead. |
Towards Enhancing Relational Rules for Knowledge Graph Link Prediction (2023.findings-emnlp)
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| Challenge: | Existing knowledge graph reasoning methods are inadequate for missing knowledge . Various methods are explored to facilitate reasoning for missing information . |
| Approach: | They propose a novel knowledge graph reasoning approach that uses a query-related fusion gate unit to model the sequentiality of relation composition and a buffering update mechanism to alleviate lagged entity information propagation. |
| Outcome: | Experimental results show that the proposed approach is superior on both transductive and inductive link prediction tasks. |
MPPO: Multi Pair-wise Preference Optimization for LLMs with Arbitrary Negative Samples (2025.coling-main)
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Shuo Xie, Fangzhi Zhu, Jiahui Wang, Lulu Wen, Wei Dai, Xiaowei Chen, Junxiong Zhu, Kai Zhou, Bo Zheng
| Challenge: | Existing preference optimization methods such as DPO and KTO are inherently derived from PPO, requiring a reference model that adds GPU memory resources and relies heavily on abundant preference data. |
| Approach: | They propose an algorithm that leverages the average likelihood of model responses to fit the reward function and maximizes the utilization of preference data. |
| Outcome: | The proposed algorithm outperforms DPO, ORPO, and SimPO on MT-Bench and Arena-Hard. |
Skill Discovery for Software Scripting Automation via Offline Simulations with LLMs (2026.findings-eacl)
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Paiheng Xu, Gang Wu, Xiang Chen, Tong Yu, Chang Xiao, Franck Dernoncourt, Tianyi Zhou, Wei Ai, Viswanathan Swaminathan
| Challenge: | Large Language Models (LLMs) can generate code from natural language queries, but runtime code generation is limited due to unverified code, security risks, longer response times, and higher computational costs. |
| Approach: | They propose an offline simulation framework to curate a software-specific skillset by exploiting large language models and publicly available scripting guides. |
| Outcome: | The proposed framework significantly improves automation success rates, reduces response time, and saves runtime token costs compared to traditional runtime code generation. |
STEP: Success-Rate-Aware Trajectory-Efficient Policy Optimization (2026.findings-acl)
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| Challenge: | Existing GRPO-based methods allocate sampling uniformly across tasks regardless of difficulty, propagate misleading learning signals and incur high sample-collection costs. |
| Approach: | They propose a framework that allocates sampling based on per-task success rates and performs fine-grained step-level optimization. |
| Outcome: | The proposed method improves sample efficiency and training stability over existing GRPO variants and three ablation variants on OSWorld and AndroidWorld. |
Adversarial Preference Learning for Robust LLM Alignment (2025.findings-acl)
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Yuanfu Wang, Pengyu Wang, Chenyang Xi, Bo Tang, Junyi Zhu, Wenqiang Wei, Chen Chen, Chao Yang, Jingfeng Zhang, Chaochao Lu, Yijun Niu, Keming Mao, Zhiyu Li, Feiyu Xiong, Jie Hu, Mingchuan Yang
| Challenge: | Modern language models rely on Reinforcement Learning from Human Feedback (RLHF) to encourage safe behaviors, but they remain vulnerable to adversarial attacks due to three key limitations: (1) the inefficiency and high cost of human annotation; (2) the vast diversity of potential adversarials; and (3) the risk of feedback bias and reward hacking. |
| Approach: | They propose an iterative adversarial training method that incorporates three key innovations to address these challenges. |
| Outcome: | Experiments on Mistral-7B-Instruct-v0.3 show that the proposed method significantly enhances robustness and reduces harmful outputs from 5.88% to 0.43%. |
ThinkPilot: Steering Reasoning Models via Automated Think-prefixes Optimization (2026.findings-eacl)
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| Challenge: | Large Reasoning Models (LRMs) are powerful but still suffer from inefficient and off-target reasoning. |
| Approach: | They propose a training-free framework that automatically optimizes Large Reasoning Models' reasoning by generating think-prefixes that evolve driven by a taxonomy of reasoning behaviors. |
| Outcome: | The proposed framework significantly improves accuracy-length trade-off for efficient reasoning, drastically improves safety and improves instruction following. |
Fast Quiet-STaR: Thinking Without Thought Tokens (2025.findings-emnlp)
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| Challenge: | Large Language Models have achieved impressive performance across a range of tasks, but further gains require more than scaling up model sizes or training data. |
| Approach: | They propose a method that gradually reduces the number of thought tokens . this method allows models to internalize more abstract reasoning processes . |
| Outcome: | The proposed framework preserves the benefits of token-level reasoning while reducing computational cost. |
SAM3-I: Segment Anything with Instructions (2026.acl-long)
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Jingjing Li, Yue Feng, Yuchen Guo, Jincai Huang, Wei Ji, Qi Bi, Yongri Piao, Miao Zhang, Xiaoqi Zhao, Qiang Chen, Shihao Zou, Huchuan Lu, Li Cheng
| Challenge: | Existing methods for concept-level grounding and instruction-level reasoning use coarse representations and iterative mask filtering. |
| Approach: | They propose an instruction-following extension of the Segment Anything Model 3 family that unifies concept-level grounding and instruction-level reasoning within a single segmentation framework. |
| Outcome: | Experiments show that SAM3-I achieves appealing performance across referring and reasoning-based segmentation while maintaining its strong concept recall ability. |
Iterative Domain-Repaired Back-Translation (2020.emnlp-main)
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| Challenge: | Existing studies show that NMT models perform poorly in specific domains when in-domain parallel corpora are scarce or nonexistent. |
| Approach: | They propose an iterative domain-repaired back-translation framework to refine translations in bilingual data by round-trip translating monolingual sentences. |
| Outcome: | The proposed framework achieves 15.79 and 4.47 BLEU improvements over unadapted models and back-translation in domain-specific translations. |
Cross-lingual Multimodal Sentiment Analysis for Low-Resource Languages via Language Family Disentanglement and Rethinking Transfer (2025.findings-acl)
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| Challenge: | Existing multimodal sentiment analysis methods are limited to textual data and cannot handle multimodal scenarios. |
| Approach: | They propose a transfer learning framework that allows cross-lingual and cross-modal alignments and a language family disentanglement module that enhances the sharing of language universals within families. |
| Outcome: | The proposed method is superior to existing methods and can handle low-resource languages. |
Towards Effective and Efficient Continual Pre-training of Large Language Models (2025.acl-long)
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Jie Chen, Zhipeng Chen, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Yingqian Min, Xin Zhao, Zhicheng Dou, Jiaxin Mao, Yankai Lin, Ruihua Song, Jun Xu, Xu Chen, Rui Yan, Zhewei Wei, Di Hu, Wenbing Huang, Ji-Rong Wen
| Challenge: | Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks. |
| Approach: | They propose a Continual pre-training method that can greatly improve Chinese language ability and scientific reasoning ability of LLMs. |
| Outcome: | The proposed method can greatly improve Chinese language ability and scientific reasoning ability of LLMs. |
A Generative Adaptive Replay Continual Learning Model for Temporal Knowledge Graph Reasoning (2025.acl-long)
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| Challenge: | Existing Continual Learning (CL)-based Temporal Knowledge Graph Reasoning methods are incomplete and reorganize historical facts without preserving historical knowledge. |
| Approach: | They propose a method which generates and adaptively replays historical entity distributions from the whole historical context. |
| Outcome: | The proposed method outperforms baselines in reasoning and mitigating forgetting. |
Miracle: Towards Personalized Dialogue Generation with Latent-Space Multiple Personal Attribute Control (2023.findings-emnlp)
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| Challenge: | Personalized dialogue systems aim to endow the chatbot agent with more anthropomorphic traits for human-like interactions. |
| Approach: | They propose a method to generate personalized dialogues using latent-space energy-based models by using a latent space energy-model. |
| Outcome: | The proposed method outperforms baselines in personality controllability and response quality. |
InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration (2024.findings-emnlp)
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| Challenge: | Large Language Models have exceptional capabilities in open generation, yet they encounter difficulties with tasks that require intensive knowledge. |
| Approach: | They propose a framework that integrates unknown knowledge into LLMs without overlap . they propose integrating domain-specific knowledge graphs into Llms to reduce knowledge forgetting . |
| Outcome: | The proposed framework outperforms state-of-the-art baselines in integrating new knowledge into LLMs. |
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. |
SgSum:Transforming Multi-document Summarization into Sub-graph Selection (2021.emnlp-main)
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| Challenge: | Existing extractive multi-document summarization methods score each sentence individually and extract salient sentences one by one. |
| Approach: | They propose a novel framework for extractive multi-document summarization that selects a sub-graph as the summary instead of selecting salient sentences. |
| Outcome: | The proposed framework improves on existing methods on multi-document datasets and human evaluations show it produces more coherent and informative summaries. |
LR²Bench: Evaluating Long-chain Reflective Reasoning Capabilities of Large Language Models via Constraint Satisfaction Problems (2025.findings-acl)
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| Challenge: | Recent advances in o1-like models have significantly enhanced the reasoning abilities of Large Language Models (LLMs). |
| Approach: | They propose a benchmark to evaluate the Long-chain Reflective Reasoning capabilities of Large Language Models. |
| Outcome: | The proposed benchmark evaluates the Long-chain Reflective Reasoning capabilities of Large Language Models (LLMs) it consists of 850 samples across six Constraint Satisfaction Problems (CSPs) |
Minimal Distillation Schedule for Extreme Language Model Compression (2024.findings-eacl)
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| Challenge: | Existing methods for teacher assistant-based distillation require multiple trials to find the optimal teacher assistant. |
| Approach: | They propose a method that allows scheduling of an optimal teacher assistant in just one trial . they show that student performance is positively correlated with the scale-performance tradeoff . |
| Outcome: | The proposed method can select the optimal teacher assistant in just one trial . it can be used to compare performance of student and teacher assistants on GLUE benchmarks. |
Aligned Dual Channel Graph Convolutional Network for Visual Question Answering (2020.acl-main)
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| Challenge: | Existing graph-based methods focus only on relations between objects in an image and neglect the importance of syntactic dependency relations between words. |
| Approach: | They propose a dual channel graph convolutional network to capture relations between objects in an image and syntactic dependency relations between words in a question. |
| Outcome: | The proposed model achieves comparable performance with the state-of-the-art approaches. |
Question Directed Graph Attention Network for Numerical Reasoning over Text (2020.emnlp-main)
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Kunlong Chen, Weidi Xu, Xingyi Cheng, Zou Xiaochuan, Yuyu Zhang, Le Song, Taifeng Wang, Yuan Qi, Wei Chu
| Challenge: | Numerical reasoning requires both natural language understanding and arithmetic computation. |
| Approach: | They propose a graph representation for the context of the passage and question needed for numerical reasoning. |
| Outcome: | The proposed model achieves remarkable results in benchmark datasets such as DROP. |
ActorMind: Emulating Human Actor Reasoning for Speech Role-Playing (2026.findings-acl)
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| Challenge: | Existing work on role-playing focuses on textual modalities, neglecting speech . et al., 2025) show that speech role-players can generate spontaneous responses with personalized traits based on the context. |
| Approach: | They propose a framework that allows models to deliver spontaneous responses with personalized verbal traits based on their role, scene, and spoken dialogue. |
| Outcome: | The proposed framework enhances speech role-playing by generating spontaneous responses with personalized traits based on their role, scene, and spoken dialogue. |
Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis (2021.emnlp-main)
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| Challenge: | Existing work on multimodal sentiment analysis relies on back-propagated task loss or geometric property of feature spaces to produce favorable fusion results. |
| Approach: | They propose a framework which hierarchically maximizes the Mutual Information (MI) in unimodal input pairs and between multimodal fusion result and unimod input to maintain task-related information through multimodal integration. |
| Outcome: | The proposed framework maximizes the Mutual Information (MI) in unimodal input pairs and between multimodal fusion result and unimodulated input to maintain task-related information through multimodal integration. |
Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis (2022.findings-acl)
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| Challenge: | Aspect-based sentiment analysis (ABSA) predicts sentiment polarity towards a specific aspect in a sentence. |
| Approach: | They propose to use a dynamic aspect-oriented semantics-based method to learn ABSA. |
| Outcome: | The proposed method can learn dynamic aspect-oriented semantics for ABSA on three benchmark datasets. |
Position Really Matters: Towards a Holistic Approach for Prompt Tuning (2025.findings-naacl)
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| Challenge: | Prompt tuning is effective in extracting knowledge from foundation models, but its effectiveness is uncertain. |
| Approach: | They propose a parametric prompt tuning strategy that dynamically determines different factors of prompts based on specific tasks or instances. |
| Outcome: | The proposed approach improves performance across a wide range of tasks including NLP, vision recognition, and vision-language tasks. |
SDGO: Self-Discrimination-Guided Optimization for Consistent Safety in Large Language Models (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) excel at various tasks but are vulnerable to jailbreak attacks that induce harmful content generation. |
| Approach: | They propose a reinforcement learning framework that leverages the model’s own discrimination capabilities as a reward signal to enhance generation safety through iterative self-improvement. |
| Outcome: | The proposed framework improves model safety by iterative self-improvement without additional annotated data or external models during training phase. |
Towards Making the Most of Cross-Lingual Transfer for Zero-Shot Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing unsupervised neural machine translation systems can degrade when labeled data is limited. |
| Approach: | They propose a multilingual pretraining and multilingual fine-tuning for facilitating cross-lingual transfer in zero-shot translation using a parallel dataset. |
| Outcome: | The proposed model outperforms state-of-the-art models on many-to-English translation by over 7.2 and 5.0 BLEU. |
On the Off-Target Problem of Zero-Shot Multilingual Neural Machine Translation (2023.findings-acl)
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| Challenge: | Despite its success, multilingual neural machine translation suffers from the off-target issue, where the translation is in the wrong language. |
| Approach: | They propose a language-aware vocabulary sharing algorithm that can be used to increase the lexical distance between languages by isolating the vocab of different languages in the decoder. |
| Outcome: | The proposed algorithm reduces off-target rate for 90 translation tasks from 29% to 8%, while improving overall BLEU score by an average of 1.9 points without extra training cost or sacrificing the supervised directions’ performance. |
SAT: Improving Semi-Supervised Text Classification with Simple Instance-Adaptive Self-Training (2022.findings-emnlp)
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| Challenge: | Existing methods for semi-supervised text classification have shown great performance in few-shot scenarios, where both labeled and unlabeled data are utilized. |
| Approach: | They propose a simple instance-adaptive self-training method for semi-supervised text classification that generates two augmented views for each unlabeled data and trains a meta learner to identify relative strength of augmentations based on the similarity between the original view and the augmented view. |
| Outcome: | The proposed method consistently shows competitive performance with varying sizes of labeled training data compared to existing semi-supervised learning methods. |
Open-ended Commonsense Reasoning with Unrestricted Answer Candidates (2023.findings-emnlp)
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Chen Ling, Xuchao Zhang, Xujiang Zhao, Yanchi Liu, Wei Cheng, Mika Oishi, Takao Osaki, Katsushi Matsuda, Haifeng Chen, Liang Zhao
| Challenge: | Current approaches to commonsense reasoning are limited due to limited answer scope. |
| Approach: | They propose to solve a commonsense question without a pre-defined answer scope . they leverage pre-trained language models to iteratively retrieve reasoning paths on the external knowledge base . |
| Outcome: | The proposed method achieves better performance on two commonsense benchmark datasets. |
Online Distilling from Checkpoints for Neural Machine Translation (N19-1)
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| Challenge: | Existing neural machine translation models have a deep structure with large amounts of parameters, making them hard to train. |
| Approach: | They propose an online method to generate a teacher model from checkpoints . they show steady improvement over a strong self-attention-based baseline system . |
| Outcome: | The proposed method improves on-the-fly on several datasets and language pairs. |
RankCSE: Unsupervised Sentence Representations Learning via Learning to Rank (2023.acl-long)
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Jiduan Liu, Jiahao Liu, Qifan Wang, Jingang Wang, Wei Wu, Yunsen Xian, Dongyan Zhao, Kai Chen, Rui Yan
| Challenge: | Unsupervised sentence representation learning is one of the fundamental problems in natural language processing . contrastive learning methods fail to capture fine-grained ranking information among the sentences . |
| Approach: | They propose a novel approach for unsupervised sentence representation learning that integrates ranking consistency and ranking distillation with contrastive learning into a unified framework. |
| Outcome: | The proposed approach performs better over state-of-the-art models on STS and TR tasks. |
UER: An Open-Source Toolkit for Pre-training Models (D19-3)
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Zhe Zhao, Hui Chen, Jinbin Zhang, Xin Zhao, Tao Liu, Wei Lu, Xi Chen, Haotang Deng, Qi Ju, Xiaoyong Du
| Challenge: | Existing work on pre-training models have shown that it is important to use a framework to deploy various pre- training models efficiently. |
| Approach: | They propose an assemble-on-demand pre-training toolkit that assembles pre-trained models on demand and encapsulates them with rich modules. |
| Outcome: | The proposed framework can reproduce state-of-the-art models or develop models that remain unexplored. |
FrontCoder: Scaling Visual Fidelity in Front-End Code Generation (2026.findings-acl)
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Jun Feng, Jian Yang, Wei Zhang, Jing Wang, Keyi Chen, Xiaokun Yang, Weicheng Gu, Yihang Lou, Yan Bai, Xianglong Liu
| Challenge: | Existing work on front-end code generation fails to provide visual fidelity and rendering quality for front- end developers. |
| Approach: | They propose a three-stage pipeline to enhance front-end code generation capabilities in LLMs . they use synthetic data, quality-controlled supervised fine-tuning, and reinforcement learning . |
| Outcome: | The proposed model achieves competitive performance with frontier models while maintaining generation efficiency. |
LEAF: Large Language Diffusion Model for Time Series Forecasting (2025.findings-emnlp)
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| Challenge: | Recent work has applied large language models (LLMs) into time series forecasting, but they lack an understanding of holistic temporal patterns with potential error accumulation. |
| Approach: | They propose a framework that marries Larg e Langu age Diffusion Model with time series forecasting (LEAF) they propose converting time series into tokens and adopting language diffusion models to capture temporal dependencies. |
| Outcome: | The proposed framework generates future predictions with a diffusion model from a holistic view. |
Long-context Language Models Fail in Basic Retrieval Tasks Without Sufficient Reasoning Steps (2025.findings-emnlp)
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| Challenge: | despite their extensive context window, long-context language models fail in some basic cases . a recent study shows that long-cot methods are not necessary for long-constituency tasks . |
| Approach: | a new study evaluates long-context language models with a large context window . the authors propose a method that can be well addressed with arbitrary reasoning steps . |
| Outcome: | The proposed methods are well addressed with a sufficient number of reasoning steps, guided by specific CoT prompts. |
AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator (2025.coling-main)
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| Challenge: | Recent large language models (LLMs) have demonstrated superior performance in static medical question answering benchmarks, rivaling even human experts. |
| Approach: | They propose a multi-agent framework emulating dynamic medical interactions between Doctor as player and NPCs including Patient and Examiner to assess the performance of LLM-driven Doctor agents in simulated clinical scenarios. |
| Outcome: | The proposed framework emulates dynamic medical interactions between Doctor as player and NPCs including Patient and Examiner. |
Confidence is not Timeless: Modeling Temporal Validity for Rule-based Temporal Knowledge Graph Forecasting (2024.acl-long)
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| Challenge: | Existing literature on temporal knowledge Graph Forecasting lacks in-depth investigation into how confidence evolves with time. |
| Approach: | They propose a framework to model the temporal validity of rules for Temporal Knowledge Graph Forecasting (TKGF) they propose rule-adversarial negative sampling and time-aware negative sampling strategies to facilitate TempValid learning. |
| Outcome: | The proposed framework outperforms state-of-the-art (SOTA) rule-based methods on six TKGF datasets. |
Training Verifier to Assessing Complex Real-World Tool-Use Trajectories (2026.findings-acl)
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Linzhuang Sun, Mingyang Chen, Hao Liang, Tianpeng Li, Zhou Yijie, Chenzheng Zhu, Tianyu Guo, Huanyao Zhang, Jingxuan Wei, Bihui Yu, Fan Yang, Wentao Zhang
| Challenge: | Existing methods for training effective AI agents often resort to synthetic data generation. |
| Approach: | They propose a plug-and-play framework for data quality control in tool-use scenarios . they construct a tool-verify dataset and release a benchmark to assess its performance . |
| Outcome: | The proposed framework surpasses Qwen2.5-72B-Instruct on Tool-V-Bench and the previous APIGen-MT dataset. |
SimCSE++: Improving Contrastive Learning for Sentence Embeddings from Two Perspectives (2023.emnlp-main)
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| Challenge: | Experimental results show that combining both proposed methods leads to a gain of 1.8 points compared to the strong baseline SimCSE configured with BERT base. |
| Approach: | They propose a method to deal with dropout noise and a dimension-wise contrastive learning objective to address feature corruption. |
| Outcome: | The proposed method achieves 1.8 points compared to the strong baseline SimCSE and 1.4 points for DiffCSE. |
Do Current Video LLMs Have Strong OCR Abilities? A Preliminary Study (2025.coling-main)
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| Challenge: | a new benchmark evaluates video-based optical character recognition (Video OCR) performance of multi-modal models in videos . the benchmark aims to improve video LLMs' ability to extract text from video content . previous benchmarks have focused on video QA, but not video-related QA. |
| Approach: | They propose to evaluate the video OCR performance of multi-modal models in videos . they use a semi-automated approach that integrates the OCR ability of image LLMs with manual refinement . |
| Outcome: | The proposed benchmark includes 1,028 videos and 2,961 question-answer pairs . it integrates the OCR ability of image LLMs with manual refinement . |
LegalAgentBench: Evaluating LLM Agents in Legal Domain (2025.acl-long)
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Haitao Li, Junjie Chen, Jingli Yang, Qingyao Ai, Wei Jia, Youfeng Liu, Kai Lin, Yueyue Wu, Guozhi Yuan, Yiran Hu, Wuyue Wang, Yiqun Liu, Minlie Huang
| Challenge: | Existing general-domain benchmarks do not capture complexity of real-world judicial cognition and decision-making. |
| Approach: | They propose a benchmark specifically designed to evaluate LLM Agents in the legal domain. |
| Outcome: | The proposed benchmark includes 17 corpora from real-world legal scenarios and provides 37 tools for interacting with external knowledge. |
Vision Language Model Helps Private Information De-Identification in Vision Data (2025.findings-acl)
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| Challenge: | Visual Language Models (VLMs) have gained popularity due to their ability to solve imagerelated tasks. |
| Approach: | They propose a framework to enhance privacy awareness of visual language models . they use a specialized instruction-tuning dataset and a tailored training methodology . |
| Outcome: | The proposed framework outperforms existing approaches in handling private information. |
Enabling Agents to Communicate Entirely in Latent Space (2026.acl-long)
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Zhuoyun Du, Runze Wang, Huiyu Bai, Zouying Cao, Xiaoyong Zhu, Yu Cheng, Bo Zheng, Wei Chen, Haochao Ying
| Challenge: | Natural language is the de facto communication medium for LLM-based agents, but it presents a fundamental constraint . natural language downsampling limits the depth and nuance of information that can be transmitted . et al.: inter-agent latent space communication is a promising paradigm for solving complex tasks . |
| Approach: | They propose a paradigm that leverages the last hidden states of an LLM as a representation of its thought for direct communication. |
| Outcome: | The proposed paradigm outperforms fine-tuned chain-of-thought prompting and single-agent baselines even across heterogeneous models. |
MaDS: Long-Horizon GUI Automation via Synergizing Dual-Layer Memory and Multi-Round Debate (2026.acl-long)
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| Challenge: | Current methods struggle to distinguish targets in low Signal-to-Noise Ratio environments and lack sufficient pre-execution verification to prevent error accumulation. |
| Approach: | They propose a Memory-augmented Debate System to ensure precise grounding across diverse interfaces and handle irreversible errors in extended workflows. |
| Outcome: | The proposed system achieves a 90.23% task success rate on MaDS-Benchmark and strong performance on public benchmarks including AITW, AITZ, CAGUI, and GUIOdyssey. |
Decoupling Memories, Muting Neurons: Towards Practical Machine Unlearning for Large Language Models (2025.findings-acl)
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| Challenge: | Existing methods for MU degrade model utility, especially when accessing the original training data. |
| Approach: | They propose a method that eliminates the influence of unlearned data by modulating the outputs of merely 1% of the neurons in the feed-forward network modules within the Transformer blocks. |
| Outcome: | The proposed method eliminates the influence of unlearned data from Large Language Models by modulating the outputs of 1% of the neurons in the feed-forward network modules within the Transformer blocks, minimizing disruption to the model’s performance. |
AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling (2025.findings-acl)
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| Challenge: | In this paper, we introduce a suite of math models that excel in solving complex math problems. |
| Approach: | They propose a supervised fine-tuning process that achieves competitive performance across general domains, followed by targeted fine- tuning for the math domain using a carefully curated set of prompts and synthetically generated responses. |
| Outcome: | The proposed model outperforms Qwen2.5-Math-72B-Instruct, GPT-4o and Claude-3.5 Sonnet in the math domain. |
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. |
A Self-Evolving LLM Agent Framework for Role-Based Norm Compliance in Healthcare (2026.findings-acl)
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| Challenge: | Existing systems treat roles as static prompts and rely on one-shot safety filters . a self-evolving LLM agent is proposed that learns from role-based social experience . |
| Approach: | They propose a self-evolving LLM agent that learns from role-based social experience and explicitly models communicator-level individual traits informed by prior communication questionnaires and clinical literature. |
| Outcome: | The proposed agent learns from role-based social experience and models communicator-level individual traits informed by prior communication questionnaires and clinical literature. |
Who Can Withstand Chat-Audio Attacks? An Evaluation Benchmark for Large Audio-Language Models (2025.findings-acl)
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| Challenge: | Existing research focused on model-specific adversarial methods, but real-world applications demand a more generalizable approach to audio adversarials. |
| Approach: | They propose a Chat-Audio Attacks benchmark to evaluate LALMs' robustness . they propose standard evaluation, GPT-4o-based evaluation and human evaluation . |
| Outcome: | The proposed benchmark aims to explore the robustness of six state-of-the-art LALMs with voice interaction capabilities. |
METER: Evaluating Multi-Level Contextual Causal Reasoning in Large Language Models (2026.acl-long)
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| Challenge: | Existing benchmarks evaluate contextual causal reasoning in fragmented settings, failing to ensure context consistency or cover the full causal hierarchy. |
| Approach: | They use a unified context to benchmark large language models' contextual causal reasoning skills. |
| Outcome: | The proposed benchmarks show that LLMs are susceptible to distraction by irrelevant but factually correct information at lower level of causality. |
Rethinking Reasoning-Intensive Retrieval: Evaluating and Advancing Retrievers in Agentic Search Systems (2026.acl-long)
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| Challenge: | Existing evaluation benchmarks for retrievers are narrow and evaluate them in isolation . existing evaluation benchmarking frameworks focus on evaluating retrievers in isolation, obscuring their value in real-world applications. |
| Approach: | They propose an evaluation framework that evaluates retrievers in agentic search systems . they provide expert-annotated reasoning aspects, positive documents, a reference response and evaluation rubrics . |
| Outcome: | The proposed framework assesses retrievers in agentic search systems. |
Structure-aware Propagation Generation with Large Language Models for Fake News Detection (2025.findings-emnlp)
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| Challenge: | propagation-based methods for fake news detection often lack structural data . authors propose a structure-aware synthetic propagation enhanced detection framework . |
| Approach: | They propose a structure-aware synthetic propagation enhanced detection framework to capture real-world propagation. |
| Outcome: | The proposed framework captures structural dynamics from real propagation, while ignoring structural patterns. |
AwarenessBench: Assessing Cognitive Capabilities of Language Models (2026.acl-long)
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Xiaojian Li, Rongwu Xu, Tianyun Zhang, Yue Wang, Shuo Chen, Qiner Lyu, Briana Zhang, Peiran Yang, Kyle Xue Chen, Haoyuan Shi, Yu Wang, Wei Xu
| Challenge: | Language models exhibit increasingly consciousness-like behaviors, requiring a baseline to evaluate their cognitive abilities. |
| Approach: | They propose a benchmark to assess the cognitive abilities of language models (LMs) they compare 18 state-of-the-art LMs to human models in metacognition, self-awareness, social awareness and situational awareness . |
| Outcome: | Evaluating 18 state-of-the-art LMs, they find they consistently surpass baselines . but most models fall short in metacognition and self-awareness, the study finds . |
TernaryBERT: Distillation-aware Ultra-low Bit BERT (2020.emnlp-main)
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| Challenge: | Transformer-based pre-training models like BERT are computationally expensive and limited to resource-constrained devices. |
| Approach: | They propose a method which ternarizes the weights in a fine-tuned BERT model. |
| Outcome: | The proposed method outperforms the other methods on the GLUE and SQUAD benchmarks while being 14.9x smaller. |
Cooperative Denoising for Distantly Supervised Relation Extraction (C18-1)
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| Challenge: | Existing methods for distantly supervised relation extraction suffer from noisy labeling problem, which can severely degrade its performance. |
| Approach: | They propose a framework for distantly supervised relation extraction that leverages text corpus and knowledge graph and a cooperative module involving their mutual learning. |
| Outcome: | The proposed method reduces the noisy labels and achieves substantial improvement over the state-of-the-art methods. |
RiskLab: A Controlled Toolkit for Probing Emergent Risks in LLM-Based Multi-Agent Systems (2026.acl-demo)
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Yu Jiang, Wenjie Wang, Yue Huang, Yanbo Wang, Zhenhong Zhou, Xiuying Chen, Yang Liu, Pin-Yu Chen, Wei Wang, Xiangliang Zhang
| Challenge: | Recent advances in large language model (LLM) agents have accelerated deployment of multi-agent systems for complex tasks. |
| Approach: | They propose an open-source toolkit for instantiating, probing, and measuring emergent risks in LLM-based multi-agent systems under controlled conditions. |
| Outcome: | The proposed toolkit is based on a structured topology–environment–protocol–agent–task quintuple enabling reproducible studies of how communication structure, coordination mechanisms, and incentives shape system-level risks. |
Text Style Transfer Back-Translation (2023.acl-long)
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Daimeng Wei, Zhanglin Wu, Hengchao Shang, Zongyao Li, Minghan Wang, Jiaxin Guo, Xiaoyu Chen, Zhengzhe Yu, Hao Yang
| Challenge: | Current methods require large amount of bilingual training data, which is challenging and sometimes impossible task. |
| Approach: | They propose a method to modify the style of inputs by modifying the source side of BT data. |
| Outcome: | The proposed method significantly improves translation quality against popular BT benchmarks on high-resource and low-resourced language pairs. |
SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning (2022.coling-1)
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| Challenge: | e-commerce has become a research hotspot for review helpfulness prediction . a new approach to help predict helpfulness of multimodal product reviews is proposed . |
| Approach: | They propose a machine learning task to identify helpfulness of multimodal product reviews . they use a probe-based strategy to enforce high attention weights on regions of greater significance . |
| Outcome: | The proposed model achieves state-of-the-art performance with lower memory consumption on two benchmark datasets with three categories. |
UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction (2021.findings-acl)
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| Challenge: | Mainstream methods that ignore the diversity among keyphrases or weakly capture the relation between tasks implicitly ignore keyphrase diversity. |
| Approach: | They propose a novel end-to-end learning framework that jointly learns to extract and generate keyphrases by exploiting latent semantic relation between extraction and generation. |
| Outcome: | The proposed approach outperforms mainstream methods on a benchmarked document on keyphrase prediction. |
CoMAE: A Multi-factor Hierarchical Framework for Empathetic Response Generation (2021.findings-acl)
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| Challenge: | Existing methods for empathetic response generation ignore hierarchical relationships between different factors, leading to a weak ability of empathy modeling. |
| Approach: | They propose a multi-factor hierarchical framework for empathetic response generation which models the above three key factors in a hierarchically structured way. |
| Outcome: | The proposed model generates more empathetic responses than previous methods. |
MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization (2023.findings-emnlp)
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Yuyan Chen, Zhihao Wen, Ge Fan, Zhengyu Chen, Wei Wu, Dayiheng Liu, Zhixu Li, Bang Liu, Yanghua Xiao
| Challenge: | Existing research emphasizes the importance of adapting prompts to specific tasks, rather than specific LLMs. |
| Approach: | They propose a model-adaptive prompt optimizer method that optimizes original prompts for each LLM in downstream tasks. |
| Outcome: | The proposed method can optimize prompts for an LLM in downstream tasks. |
Stop When Enough: Adaptive Early-Stopping for Chain-of-Thought Reasoning (2026.acl-long)
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| Challenge: | Chain-of-Thought reasoning has driven recent gains of large language models (LLMs) on reasoning-intensive tasks by externalizing intermediate steps. |
| Approach: | They propose a training-free framework that adaptively determines when to stop reasoning to mitigate overthinking. |
| Outcome: | The proposed framework reduces token usage by 20-55% while maintaining or improving accuracy compared to standard CoT prompting. |
Experiential Co-Learning of Software-Developing Agents (2024.acl-long)
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Chen Qian, Yufan Dang, Jiahao Li, Wei Liu, Zihao Xie, YiFei Wang, Weize Chen, Cheng Yang, Xin Cong, Xiaoyin Che, Zhiyuan Liu, Maosong Sun
| Challenge: | Recent advances in large language models (LLMs) have brought significant changes to various domains, especially through autonomous agents. |
| Approach: | They propose a framework that lets agents learn shortcuts from their past tasks and use them for future task execution. |
| Outcome: | The proposed framework enables agents to tackle unseen software-developing tasks more effectively. |
A Novel Paradigm Boosting Translation Capabilities of Large Language Models (2024.findings-naacl)
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| Challenge: | Existing studies on LLMs focused on supervised fine-tuning but their effectiveness has been limited. |
| Approach: | They propose a paradigm consisting of three stages: Secondary Pre-training using extensive monolingual data, Continual Pre- training with interlinear text format documents, and Leveraging source-language consistent instruction for supervised fine-tuning. |
| Outcome: | The proposed approach surpasses previous work and achieves superior performance compared to models such as NLLB-54B(CITATION) and GPT3.5-text-davinci-003. |
An Empirical Study of Pre-trained Transformers for Arabic Information Extraction (2020.emnlp-main)
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| Challenge: | Multilingual pre-trained Transformers have been shown to enable effective cross-lingual zero-shot transfer, but their performance on Arabic information extraction tasks is not well studied. |
| Approach: | They pre-train a bilingual BERT that is designed specifically for Arabic NLP and English-to-Arabic zero-shot transfer learning. |
| Outcome: | The pre-trained model significantly outperforms mBERT, XLM-RoBERTa, and AraBERT in both the supervised and zero-shot transfer settings. |
Learning Cooperative Interactions for Multi-Overlap Aspect Sentiment Triplet Extraction (2022.findings-emnlp)
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| Challenge: | Existing methods for aspect sentiment triplet extraction focus on the single interactions between an aspect and an opinion. |
| Approach: | They propose a multi-overlap triplet extraction method which decodes the complex relations between multiple aspects and opinions by learning their cooperative interactions. |
| Outcome: | The proposed method outperforms baselines, especially multi-overlap triplets. |
XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation (2020.emnlp-main)
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Yaobo Liang, Nan Duan, Yeyun Gong, Ning Wu, Fenfei Guo, Weizhen Qi, Ming Gong, Linjun Shou, Daxin Jiang, Guihong Cao, Xiaodong Fan, Ruofei Zhang, Rahul Agrawal, Edward Cui, Sining Wei, Taroon Bharti, Ying Qiao, Jiun-Hung Chen, Winnie Wu, Shuguang Liu, Fan Yang, Daniel Campos, Rangan Majumder, Ming Zhou
| Challenge: | XGLUE provides a benchmark dataset to train large-scale cross-lingual pre-trained models . XCLUE provides 11 diversified tasks that cover both understanding and generation scenarios . |
| Approach: | They introduce a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora. |
| Outcome: | The proposed dataset is labeled in English and includes only natural language understanding tasks. |
Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets (N18-1)
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| Challenge: | Experimental results show that the proposed model consistently outperforms the traditional RNNSearch and the newly emerged state-of-the-art Transformer on English-German and Chinese-English translation tasks. |
| Approach: | They propose an approach for applying GANs to NMT by building a conditional sequence generative adversarial net with two adversarials. |
| Outcome: | The proposed model outperforms the existing RNNSearch and Transformer on English-German and Chinese-English translation tasks. |
Playing 20 Question Game with Policy-Based Reinforcement Learning (D18-1)
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| Challenge: | The 20 Questions (Q20) game encourages deductive reasoning and creativity. |
| Approach: | They propose a policy-based Reinforcement Learning method which learns optimal question selection . the method is robust to noisy answers and uses a reward network to estimate the more informative reward . |
| Outcome: | The proposed method outperforms an entropy-based engineering system and has competitive performance in noisy-free simulation environment. |
Fin-STAR: Structure-as-Semantics to Resolve Implicitness in Financial Retrieval (2026.findings-acl)
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Yu Zou, Yan Chen, Lida He, Qi Zhou, Xiaorui Zhou, Aixi Zhong, Yi Wang, Wei Li, Qingyu Wang, Jiatao Li, Wei Gong, Jialei Zeng, Jingmei Zhao, Ke Jiang, Qing Li
| Challenge: | Existing Retrieval-Augmented Generation systems treat structure as a physical navigational skeleton rather than intrinsic semantic knowledge. |
| Approach: | They propose a framework that redefining hierarchy as intrinsic semantics and uses snippets to enrich hierarchical lineage. |
| Outcome: | The proposed framework outperforms state-of-the-art hierarchical and graph-based benchmarks on FinTierQA Gold. |
KG-Adapter: Enabling Knowledge Graph Integration in Large Language Models through Parameter-Efficient Fine-Tuning (2024.findings-acl)
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| Challenge: | Large language models (LLMs) are criticized for lack of expertise and knowledge conflict . KG-Adapter is a parameter-level KG integration method for decoder-only LLMs . |
| Approach: | They propose a parameter-level KG integration method based on parameter-efficient fine-tuning . they use KG-Adapter to integrate knowledge graphs with LLMs and perform joint reasoning . |
| Outcome: | The proposed method outperforms the current state-of-the-art method on four datasets for two different tasks. |
KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment (2023.acl-long)
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| Challenge: | Recent legislation of the "right to be forgotten" has led to the interest in machine unlearning . MU can be used to forget specific training instances as if they have never existed . |
| Approach: | They propose a general unlearning framework called KGA to induce forgetfulness . they propose several unlearning evaluation metrics with pertinence . |
| Outcome: | The proposed framework improves on large-scale datasets and provides insight into unlearning for NLP tasks. |
Toward Consistent World Models with Multi-Token Prediction and Latent Semantic Enhancement (2026.acl-long)
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| Challenge: | Existing methods to learn internal world models rely on one-step supervision . however, standard MTP suffers from structural hallucinations . |
| Approach: | They propose a method which anchors predictions to ground-truth hidden state trajectories. |
| Outcome: | The proposed method bridges the gap between discrete tokens and continuous state representations, reducing structural hallucinations, and improving robustness to perturbations. |
Following Occam’s Razor: Dynamic Combination of Structured Knowledge for Multi-Hop Question Answering using LLMs (2025.findings-emnlp)
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| Challenge: | Multi-hop question answering is a challenging task that requires capturing information from multiple positions in multiple documents. |
| Approach: | They propose a framework for integrating text-based and triple-based paradigms that incorporates structured knowledge into large-scale question answering. |
| Outcome: | The proposed framework improves multi-hop question answering by incorporating structured knowledge into the models. |
Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data (2024.findings-acl)
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Yanda Li, Chi Zhang, Gang Yu, Wanqi Yang, Zhibin Wang, Bin Fu, Guosheng Lin, Chunhua Shen, Ling Chen, Yunchao Wei
| Challenge: | OpenAI's GPT-4 has demonstrated remarkable multimodal capabilities, but specific mechanics of GPT4 remain unknown. |
| Approach: | They propose a data collection methodology that synchronously synthesizes images and dialogues for visual instruction tuning. |
| Outcome: | The proposed method improves on ten commonly assessed models and provides greater flexibility compared to existing methods. |
League of LLMs: A Benchmark-Free Paradigm for Mutual Evaluation of Large Language Models (2026.acl-long)
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Qianhong Guo, Wei Xie, Xiaofang Cai, Enze Wang, Shuoyoucheng Ma, Xiaobing Sun, Tian Xia, Kai Chen, Xiaofeng Wang, Baosheng Wang
| Challenge: | Large language models (LLMs) have shown exceptional capabilities across a wide range of tasks, but reliable evaluation remains a challenge due to data contamination, opaque operation, and subjective preferences. |
| Approach: | They propose a benchmark-free evaluation paradigm that organizes multiple LLMs into a self-governed league for multi-round mutual evaluation. |
| Outcome: | Experiments on eight mainstream LLMs in mathematics and programming show that the proposed model can distinguish capabilities while maintaining high internal ranking stability. |
Enhancing Semantic Consistency of Large Language Models through Model Editing: An Interpretability-Oriented Approach (2024.findings-acl)
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| Challenge: | Large Language Models generate inconsistent and sometimes contradictory outputs when presented with a prompt that has equivalent semantics but is expressed differently from the original prompt. |
| Approach: | They propose to refine a Large Language Model (LLM) with prompt-output pairs with equivalent semantics to achieve semantic consistency. |
| Outcome: | The proposed method improves the semantic consistency and task performance of LLMs. |
When Personalization Tricks Detectors: The Feature-Inversion Trap in Machine-Generated Text Detection (2026.acl-long)
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Lang Gao, Xuhui Li, Chenxi Wang, Mingzhe Li, Wei Liu, Zirui Song, Jinghui Zhang, Rui Yan, Preslav Nakov, Xiuying Chen
| Challenge: | Personalized MGT detection remains largely underexplored due to personalization challenges . large language models (LLMs) can imitate personal writing styles, but they can generate fake news and misinformation. |
| Approach: | They propose a benchmark to evaluate detector robustness under personalization . they attribute this limitation to a feature-inversion trap that flips the effect in personalized contexts . |
| Outcome: | The proposed framework predicts detector robustness under personalization with an 85% correlation to actual results. |
PyramidCodec: Hierarchical Codec for Long-form Music Generation in Audio Domain (2024.findings-emnlp)
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| Challenge: | Existing approaches to generate long music are inefficient and lack of structured representation. |
| Approach: | They propose a hierarchical discrete representation of audio for long audio-domain music generation using residual vector quantization on different levels of features. |
| Outcome: | The proposed method achieves competitive performance in terms of reconstruction quality and token per second (TPS) the proposed method facilitates training a language model that can generate well-structured long-form music for up to 3 minutes. |
A Survey on Detection of LLMs-Generated Content (2024.findings-emnlp)
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Xianjun Yang, Liangming Pan, Xuandong Zhao, Haifeng Chen, Linda Petzold, William Yang Wang, Wei Cheng
| Challenge: | Recent advances in large language models have led to an increase in synthetic content generation . the ability to detect LLMs-generated content has become of paramount importance . |
| Approach: | They propose to provide a detailed overview of existing detection strategies and benchmarks, scrutinizing their differences and advocating for more adaptable and robust models to enhance detection accuracy. |
| Outcome: | The proposed model will be able to detect human-written content in real time. |
Think and Recall: Layer-Level Prompting for Lifelong Model Editing (2025.emnlp-main)
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| Challenge: | Existing methods for lifelong model editing suffer from limitations in usability, such as requiring additional training corpora or lacking support for reversible and detachable edits. |
| Approach: | They propose a plug-and-play method for knowledge retrieval and storage, i.e., Layer-Level Prompting, which enables seamless and efficient lifelong model editing. |
| Outcome: | The proposed method outperforms existing methods on question answering and hallucination benchmarks across different LLMs. |
Little Giants: Synthesizing High-Quality Embedding Data at Scale (2025.naacl-long)
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| Challenge: | Synthetic data generation is an increasingly popular way of training models without the need for large, manually labeled datasets. |
| Approach: | They propose a framework that aligns open-source small models to efficiently generate large-scale embedding data. |
| Outcome: | The proposed framework outperforms state-of-the-art embedding models by using only 1/10 of the GPT API calls. |
STORYTELLER: An Enhanced Plot-Planning Framework for Coherent and Cohesive Story Generation (2025.findings-acl)
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Jiaming Li, Yukun Chen, Ziqiang Liu, Minghuan Tan, Lei Zhang, Yunshui Li, Run Luo, Longze Chen, Jing Luo, Ahmadreza Argha, Hamid Alinejad-Rokny, Wei Zhou, Min Yang
| Challenge: | Existing methods for storytelling lack coherence and consistency, compromising the overall storytelling experience. |
| Approach: | They propose a novel approach that improves the coherence and consistency of automatically generated stories by managing plot nodes and enabling dynamic interactions between different parts of the story. |
| Outcome: | The proposed approach outperforms existing methods in 84.33% of the trials. |
LLM Factoscope: Uncovering LLMs’ Factual Discernment through Measuring Inner States (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) produce outputs that deviate from factual reality, especially in sensitive applications such as medical consultation and legal advice. |
| Approach: | They propose a Siamese network-based model that leverages LLMs’ inner states for factual detection. |
| Outcome: | The proposed model achieves over 96% accuracy on a custom-collected factual detection dataset. |
LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement (2025.acl-long)
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Boyi Kang, Xinfa Zhu, Zihan Zhang, Zhen Ye, Mingshuai Liu, Ziqian Wang, Yike Zhu, Guobin Ma, Jun Chen, Longshuai Xiao, Chao Weng, Wei Xue, Lei Xie
| Challenge: | Recent advances in language models have demonstrated strong capabilities in semantic understanding and contextual modeling. |
| Approach: | They propose a LLaMA-based language model that incentivizes generalization capabilities for speech enhancement. |
| Outcome: | The proposed language model outperforms prior task-specific discriminative and generative models in acoustic enhancement tasks. |
Breaking the Stage Barrier: A Novel Single-Stage Approach to Long Context Extension for Large Language Models (2025.coling-main)
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Haoran Lian, Junmin Chen, Wei Huang, Yizhe Xiong, Wenping Hu, Guiguang Ding, Hui Chen, Jianwei Niu, Zijia Lin, Fuzheng Zhang, Di Zhang
| Challenge: | Recent studies show that Large language models struggle with handling long token sequences due to limited training context size. |
| Approach: | They propose a single-stage continual pretraining method to equip LLMs with long context modeling capabilities. |
| Outcome: | The proposed method outperforms existing methods on 4 language modeling benchmarks. |
DeepSieve: Information Sieving via LLM-as-a-Knowledge-Router (2026.findings-eacl)
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Minghao Guo, Qingcheng Zeng, Xujiang Zhao, Yanchi Liu, Wenchao Yu, Mengnan Du, Haifeng Chen, Wei Cheng
| Challenge: | Existing RAG methods lack fine-grained control over query and source sides, resulting in noisy retrieval and shallow reasoning. |
| Approach: | They propose an agentic RAG framework that integrates information sieving via LLM-as-a-knowledge-router. |
| Outcome: | Experiments on multi-hop QA tasks across heterogeneous sources demonstrate improved reasoning depth, retrieval precision, and interpretability over conventional approaches. |
No One Fits All: From Fixed Prompting to Learned Routing in Multilingual LLMs (2026.findings-acl)
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| Challenge: | Existing studies show that translation-based prompting is not universally optimal for multilingual LLMs. |
| Approach: | They evaluate translation-based prompting across ten languages and four benchmarks . they propose a lightweight classifier that predicts whether native or translation- based prompts are optimal . |
| Outcome: | The proposed classifiers achieve statistically significant improvements over fixed prompting strategies across ten languages and four benchmarks. |
EMGLLM: Data-to-Text Alignment for Electromyogram Diagnosis Generation with Medical Numerical Data Encoding (2025.findings-acl)
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| Challenge: | Existing Large Language Models struggle to interpret EMG tables . EMGLLM is a data-to-text model for medical examination tables based on electrical signals . |
| Approach: | They propose a data-to-text model that aligns EMG data into word embeddings that reflect health degree. |
| Outcome: | The proposed model outperforms baseline models in understanding EMG tables and generating high-quality diagnoses. |
CloneMem: Benchmarking Long-Term Memory for AI Clones (2026.acl-long)
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| Challenge: | Existing memory benchmarks rely on user–agent conversational histories, which are temporally fragmented and insufficient for capturing continuous life trajectories. |
| Approach: | They propose a benchmark for evaluating long-term memory in AI Clone scenarios grounded in non-conversational digital traces, including diaries, social media posts, and emails, spanning one to three years. |
| Outcome: | Experiments show that existing memory benchmarks struggle in this setting, highlighting open challenges for life-grounded personalized AI. |
Targeted Exploration via Unified Entropy Control for Reinforcement Learning (2026.findings-acl)
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| Challenge: | Existing methods for group relative policy optimization suffer from entropy collapse . Existing exploration methods introduce additional bias or variance during exploration, making it difficult to maintain stability. |
| Approach: | They propose a framework that provides targeted mechanisms for exploration and stabilization. |
| Outcome: | The proposed framework expands search space on difficult prompts while preventing entropy growth uncontrollably. |
Modeling LLM Unlearning as an Asymmetric Two-Task Learning Problem (2026.acl-long)
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| Challenge: | Large language models (LLMs) are inherently dual-use and can be leveraged for both beneficial and harmful purposes. |
| Approach: | They propose a retention-prioritized gradient synthesis framework that decouples task-specific gradient extraction from conflict-aware combination. |
| Outcome: | The proposed method achieves tighter alignment on WMDP Bio and RWKU benchmarks. |
Recommend for a Reason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extraction (2021.findings-emnlp)
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| Challenge: | Existing review-based recommenders favor large and complex language encoders that can only learn latent and uninterpretable text representations. |
| Approach: | They propose a tightly coupled two-stage approach to extract latent user sentiments and item properties from reviews and an Attention-Property-aware Rating Estimator (APRE). |
| Outcome: | Extensive experiments on seven real-world Amazon review datasets show that the proposed approach extracts the latent user sentiments, item properties, and the complicated interactions between the two components. |
DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs (2025.emnlp-main)
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Minxuan Lv, Zhenpeng Su, Leiyu Pan, Yizhe Xiong, Zijia Lin, Hui Chen, Wei Zhou, Jungong Han, Guiguang Ding, Wenwu Ou, Di Zhang, Kun Gai, Songlin Hu
| Challenge: | Existing sparsification methods like pruning can lose model knowledge through parameter removal. |
| Approach: | They propose a novel approach that achieves sparsification by partitioning pre-trained FFN layers into computational blocks. |
| Outcome: | The proposed approach achieves superior performance across language modeling and downstream tasks under equivalent computational constraints. |
A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions (2026.acl-long)
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Zhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo, Chunchun Chen, Xing Wei, Yunhui Liu, Hongru Sun, Yuxing Zhang, Jun Xu, Yatao Bian, Ming Zhang, Wei Ye, Tieke He, Jie Yang, Guanjie Zheng, Zhonghai Wu, Bo Zhang, Lei Bai, Xiao Luo
| Challenge: | Existing research on reinforcement learning for LLMs under data scarcity has not been unified. |
| Approach: | They propose a top-up hierarchical framework built around three complementary perspectives: data-centric, training-centric and framework-centric. |
| Outcome: | The proposed framework provides a clear conceptual foundation for understanding the design space of data-efficient RL for large language models and to guide researchers working in this emerging area. |
VIEScore: Towards Explainable Metrics for Conditional Image Synthesis Evaluation (2024.acl-long)
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| Challenge: | Existing metrics for conditional image generation are opaque and lack explainability . evaluators of these metrics have limited ability to evaluate image synthesis tasks . |
| Approach: | They propose a Visual Instruction-guided Explainable metric for evaluating conditional image models. |
| Outcome: | The proposed model achieves a high Spearman correlation with human evaluations, but is weaker than GPT-4o and GPT-v in evaluating synthetic images. |
Uncertainty Quantification for In-Context Learning of Large Language Models (2024.naacl-long)
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Chen Ling, Xujiang Zhao, Xuchao Zhang, Wei Cheng, Yanchi Liu, Yiyou Sun, Mika Oishi, Takao Osaki, Katsushi Matsuda, Jie Ji, Guangji Bai, Liang Zhao, Haifeng Chen
| Challenge: | Existing studies on in-context learning have focused on quantifying the uncertainty associated with the model's response, but they neglect the complexity of the LLM and the uniqueness of in-constitut learning. |
| Approach: | They propose a method to quantify the uncertainty associated with in-context learning and propose corresponding estimation method to quantify both types of uncertainties. |
| Outcome: | The proposed method offers an unsupervised way to understand the prediction of in-context learning in a plug-and-play fashion. |
Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach (2024.emnlp-main)
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| Challenge: | Existing studies on susceptibility to misinformation rely on self-reported beliefs, which can be subject to bias, expensive to collect, and challenging to scale for downstream applications. |
| Approach: | They propose a computational approach to efficiently model users’ latent susceptibility levels by using demographic factors and political ideology as inputs. |
| Outcome: | The proposed model shows that political leanings and other psychological factors exhibit varying degrees of association with susceptibility to COVID-19 misinformation. |
Lightweight LLM Agent Memory with Small Language Models (2026.acl-long)
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Jiaquan Zhang, Chaoning Zhang, Shuxu Chen, Zhenzhen Huang, Pengcheng Zheng, Zhicheng Wang, Ping Guo, Fan Mo, Sung-Ho Bae, Jie Zou, Jiwei Wei, Yang Yang
| Challenge: | Existing external memory systems for LLMs have low online overhead but are unstable in accumulating latency over long interactions. |
| Approach: | They propose a lightweight memory system for better agent memory driven by Small Language Models . lightmem modularizes memory retrieval, writing, and long-term consolidation . they show consistent gains across model scales and high efficiency . |
| Outcome: | The proposed system improves agent memory but has low latency and low online overhead . it separates online processing from offline consolidation to enable efficient memory invocation . the proposed system achieves an average F1 improvement of 2.5 over A-MEM on LoCoMo . |
Train in Vain: Functionality-Preserving Poisoning to Prevent Unauthorized Use of Code Datasets (2026.findings-acl)
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Yuan Xiao, Jiaming Wang, Yuchen Chen, Wei Song, Jun Sun, Shiqing Ma, Yanzhou Mu, Juan Zhai, Chunrong Fang, Jin Song Dong, Zhenyu Chen
| Challenge: | Existing methods for dataset poisoning require full-dataset poison, which breaks code compilability. |
| Approach: | They propose a functionality-preserving poisoning approach that injects short, compilable weak-use fragments into executed code paths. |
| Outcome: | The proposed method contaminates 10% of the dataset while maintaining 100% compilability and functional correctness. |
LLMs Can Simulate Standardized Patients via Agent Coevolution (2025.acl-long)
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Zhuoyun Du, LujieZheng LujieZheng, Renjun Hu, Yuyang Xu, Xiawei Li, Ying Sun, Wei Chen, Jian Wu, Haolei Cai, Haochao Ying
| Challenge: | Training medical personnel using standardized patients (SPs) remains a complex challenge, necessitating extensive domain expertise and role-specific practice. |
| Approach: | They propose a simulated patient framework that allows patient agents to simulate diagnostic process through multi-turn dialogues. |
| Outcome: | The proposed framework improves over existing reasoning methods by more than 10% in requirement alignment and better human preference after evolving over 200 cases for 10 hours with excellent generalizability. |
Exploring Question Guidance and Answer Calibration for Visually Grounded Video Question Answering (2024.findings-emnlp)
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| Challenge: | Existing methods for videoQA lack temporal localization labels, leading to inaccurate localization. |
| Approach: | They propose a Question-Guided and Answer-Calibrated TRansformer which guides and calibrates localization using question and option texts without localization labels. |
| Outcome: | The proposed model achieves comparable accuracy to large-scale pretrained models and leads in localization aspects. |
Multi-granularity Textual Adversarial Attack with Behavior Cloning (2021.emnlp-main)
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| Challenge: | Existing adversarial attack models are vulnerable to adversarials crafted by human-imperceptible perturbations. |
| Approach: | They propose a multi-granularity adversarial attack model that generates high-quality adversarials with fewer queries to victim models. |
| Outcome: | The proposed model generates high-quality adversarial samples with fewer queries to victim models compared to baseline models . the proposed model also reduces query times for black-box models that only output labels without confidence scores . |
Charge-Based Prison Term Prediction with Deep Gating Network (D19-1)
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| Challenge: | Existing work merely predicts the total prison term, but in reality a defendant is often charged with multiple crimes. |
| Approach: | They propose a charge-based prison term prediction task that better fits real needs and makes it more accurate and interpretable. |
| Outcome: | The proposed method achieves state-of-the-art performance for charge-specific feature selection and aggregation. |
Learning to Disentangle Interleaved Conversational Threads with a Siamese Hierarchical Network and Similarity Ranking (N18-1)
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| Challenge: | Existing methods to disentangle interleaved conversations can lead to difficulties in following discussions and retrieving relevant information from simultaneous messages. |
| Approach: | They propose to leverage representation learning to separate intermingled messages into detached conversations by estimating conversation-level similarity between closely posted messages. |
| Outcome: | The proposed approach outperforms baselines in pairwise similarity estimation and conversation disentanglement. |
TREA: Tree-Structure Reasoning Schema for Conversational Recommendation (2023.acl-long)
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| Challenge: | Recent reasoning-based models cannot fully figure out complex causal relationships between mentioned entities with external knowledge. |
| Approach: | They propose a Tree structure Reasoning schEmA that constructs a multi-hierarchical scalable tree as the reasoning structure to clarify the causal relationships between mentioned entities. |
| Outcome: | Extensive experiments on two public CRS datasets show the proposed model works. |
ProMind-LLM: Proactive Mental Health Care via Causal Reasoning with Sensor Data (2025.findings-acl)
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| Challenge: | Existing methods for mental health risk assessment rely on subjective textual records . however, these uncertainties can cause inconsistent and unreliable predictions . |
| Approach: | They propose a method that integrates objective behavior data alongside subjective mental records for robust mental health risk assessment. |
| Outcome: | The proposed approach achieves significant improvements over general LLMs. |
MedFact: A Large-scale Chinese Dataset for Evidence-based Medical Fact-checking of LLM Responses (2025.emnlp-main)
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Tong Chen, Zimu Wang, Yiyi Miao, Haoran Luo, Sun Yuanfei, Wei Wang, Zhengyong Jiang, Procheta Sen, Jionglong Su
| Challenge: | Existing medical fact-checking datasets focus on human-generated content, leaving the verification of content generated by large language models (LLMs) relatively unexplored. |
| Approach: | They propose to use Chinese medical fact-checking datasets to verify LLM-generated medical content by combining in-context learning and fine-tuning. |
| Outcome: | The first evidence-based Chinese medical fact-checking dataset of LLM-generated medical content consists of 1,321 questions and 7,409 claims . |
HoPE: A Novel Positional Encoding Without Long-Term Decay for Enhanced Context Awareness and Extrapolation (2025.acl-long)
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| Challenge: | Existing positional encodings exhibit long-term decay, based on an entrenched and long-standing opinion that tokens farther away from the current position carry less relevant information. |
| Approach: | They propose a high-frequency rotary position encoding (HoPE) that replaces specific components in RoPE with position-independent ones, retaining only high- frequency signals. |
| Outcome: | The proposed method exhibits greater robustness to the out-of-distribution behavior in attention patterns during extrapolation. |
Multi-Agent Procedural Graph Extraction with Structural and Logical Refinement (2026.findings-eacl)
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Wangyang Ying, Yanchi Liu, Xujiang Zhao, Wei Cheng, Zhengzhang Chen, Wenchao Yu, Yanjie Fu, Haifeng Chen
| Challenge: | Recent advances in large language models (LLMs) show potential for graph extraction, but often yield ill-formed structures or misinterpret logical constructs such as gateways. |
| Approach: | They propose a framework that treats procedural graph extraction as a multi-round reasoning process with structural and logical refinement agents. |
| Outcome: | The proposed framework achieves significant improvements in structural correctness and logical consistency over strong baselines. |
TRANS-ZERO: Self-Play Incentivizes Large Language Models for Multilingual Translation Without Parallel Data (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have reshaped machine translation, but multilingual MT still relies heavily on parallel data for supervised fine-tuning. |
| Approach: | They propose a framework that leverages only monolingual data and the intrinsic multilingual knowledge of Large Language Models (LLMs). |
| Outcome: | The proposed framework matches models trained on large-scale parallel data and excels in non-English translation directions. |
Evaluating the Expressive Appropriateness of Speech in Rich Contexts (2026.acl-long)
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Tianrui Wang, Ziyang Ma, Yizhou Peng, Haoyu Wang, Zhikang Niu, Zikang Huang, Yihao Wu, Yi-Wen Chao, Yu Jiang, Yuheng Lu, Guanrou Yang, Xuanchen Li, Hexin Liu, Chunyu Qiang, Cheng Gong, Yifan Yang, Tianchi Liu, Junyu Wang, Nana Hou, Meng Ge, Fuming You, Yang Wei, Zhongqian Sun, Hu Haifeng, Xiaobao Wang, Eng Siong Chng, Xie Chen, Longbiao Wang, Jianwu Dang
| Challenge: | Existing methods for evaluating expressive speech focus on word accuracy, naturalness, signal quality, or emotional intensity at the utterance level. |
| Approach: | They propose a framework for Evaluating Expressive Appropriateness in speech that assesses whether a speech sample aligns with the underlying communicative intent implied by its discourse-level narrative context. |
| Outcome: | The proposed framework outperforms existing speech evaluation and analysis systems on a human-annotated test set. |
From Discrimination to Generation: Low-Resource Intent Detection with Language Model Instruction Tuning (2024.findings-acl)
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| Challenge: | Existing studies fine-tune discriminative models on specific defined intent classes, preventing them from being directly adopted to new intent domains. |
| Approach: | They propose to use a pre-trained generative intent model to detect new intents from different domains with no parameter updates. |
| Outcome: | The proposed model outperforms baselines that need further fine-tuning or domain-specific samples. |
A Hierarchical Interactive Network for Joint Span-based Aspect-Sentiment Analysis (2022.coling-1)
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| Challenge: | Existing methods for aspect-sentiment analysis ignore internal correlations between aspect extraction and sentiment classification. |
| Approach: | They propose a hierarchical interactive network to model two-way interactions between two tasks appropriately using shallow-level and deep-level inputs. |
| Outcome: | Extensive experiments on three real-world datasets demonstrate that the proposed model outperforms existing methods. |
Lost in the Context: Insufficient and Distracted Attention to Contexts in Preference Modeling (2025.acl-long)
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Shihan Dou, Jiayi Chen, Chenhao Huang, Feng Chen, Wei Chengzhi, Huiyuan Zheng, Shichun Liu, Yan Liu, Chenxiao Liu, Chao Xin, Lin Yan, Zongzhang Zhang, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing reward models concatenate contexts and responses, but they often ignore crucial segments of the context that are important for evaluating the response quality. |
| Approach: | They propose a reward model that evaluates the response quality based on a given context and assigns a rewards reward. |
| Outcome: | The proposed framework significantly improves preference modeling by increasing attention to relevant information within the context and achieves better generalizability. |
Be Cautious When Merging Unfamiliar LLMs: A Phishing Model Capable of Stealing Privacy (2025.findings-acl)
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| Challenge: | Model merging is a widespread technology in large language models that integrates multiple task-specific LLMs into a unified one. |
| Approach: | They propose a model merging approach that trains a phishing model capable of stealing privacy using a privacy phish instruction dataset. |
| Outcome: | The proposed model cloaking method mimics a specialized capability to conceal attack intent, luring users into merging the phishing model. |
CNEQ: Incorporating numbers into Knowledge Graph Reasoning (2024.findings-emnlp)
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| Challenge: | Complex query answering (CQA) is a task that addresses semantics of numerical entities. |
| Approach: | They propose a model that includes a Number-Entity Predictor and an Entity Filter . they use three widely-used Knowledge Graphs to perform reasoning over knowledge graphs . |
| Outcome: | The proposed model can predict entities and numerical values better than existing models . it compares or filters out entities that meet certain constraints on three widely-used Knowledge Graphs . |
Benchmarking for Domain-Specific LLMs: A Case Study on Academia and Beyond (2025.findings-emnlp)
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| Challenge: | Comp-Comp is an iterative benchmarking framework grounded in the principles of comprehensiveness and compactness. |
| Approach: | They propose a benchmark framework that incorporates the principle of comprehensiveness and compactness. |
| Outcome: | The proposed framework is domain-agnostic and adaptable to a wide range of specialized fields. |
Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs (D19-1)
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| Challenge: | Empirically, our model achieves state-of-the-art results on few-shot link prediction KG benchmarks. |
| Approach: | They propose a Meta Relational Learning framework to do few-shot link prediction in KGs by observing only a few associative triples. |
| Outcome: | The proposed model achieves state-of-the-art results on few-shot link prediction KG benchmarks. |
Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data (P19-1)
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| Challenge: | Existing methods to perform relation extraction are feature-based or kernel-based, but the results of our study show that they can improve the performance of a baseline model with more than 10% absolute increase in F1-score. |
| Approach: | They propose a multi-task architecture which jointly trains a model to perform relation identification with cross-entropy loss and relation classification with ranking loss. |
| Outcome: | The proposed model outperforms the state-of-the-art models on ACE 2005 Chinese and English corpus and significantly improves the performance of a baseline model with more than 10% increase in F1-score. |
Semi-Supervised Disfluency Detection (C18-1)
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| Challenge: | Detecting disfluency can be difficult because of the flexible nature of reparandum structure and the lack of a nested structure. |
| Approach: | They propose a semi-supervised approach which extracts hidden features from self-attention without any Recurrent Neural Network (RNN) or Convolutional Neural Net (CNN). |
| Outcome: | The proposed approach improves over baselines by using unlabelled data . identifying and removing non-fluent factors would help to improve spontaneous speech quality . |
SCALE: Synergized Collaboration of Asymmetric Language Translation Engines (2024.findings-acl)
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| Challenge: | In this paper, we introduce SCALE, a collaborative framework that connects a compact Specialized Translation Model (STM) and a general-purpose Large Language Model (LLM) as one unified translation engine. |
| Approach: | They propose a collaborative framework that connects a Specialized Translation Model (STM) and a general-purpose Large Language Model (LLM) as one unified translation engine. |
| Outcome: | The proposed framework outperforms both LLMs and supervised models in high-resource or challenging low-resourced settings. |
Doc-V*: Coarse-to-Fine Interactive Visual Reasoning for Multi-Page Document VQA (2026.acl-long)
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Yuanlei Zheng, Pei Fu, Hang Li, Ziyang Wang, Yuyi Zhang, Wenyu Ruan, Xiaojin Zhang, Zhongyu Wei, Zhenbo Luo, Jian Luan, Wei Chen, Xiang Bai
| Challenge: | Existing OCR-free approaches to document visual question answering are brittle and passive. |
| Approach: | They propose an OCR-free agentic framework that casts multi-page DocVQA as sequential evidence aggregation. |
| Outcome: | The proposed framework outperforms open-source and proprietary models in five benchmarks and improves out-of-domain performance by 47.9% over baseline. |
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%. |
ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering (2025.emnlp-main)
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| Challenge: | Chart question answering (CQA) is a multimodal task for evaluating the reasoning capabilities of vision-language models. |
| Approach: | They propose a chart question answering benchmark that incorporates multilingual contexts and supports open-domain textual outputs. |
| Outcome: | The proposed framework outperforms the previous three common CQA paradigms: instruction-following, OCR-enhanced, and chain-of-thought. |
Decide less, communicate more: On the construct validity of end-to-end fact-checking in medicine (2026.findings-acl)
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Sebastian Antony Joseph, Lily Chen, Barry Wei, Michael Mackert, Iain James Marshall, Paul Pu Liang, Ramez Kouzy, Byron C Wallace, Junyi Jessy Li
| Challenge: | Evidence-based medicine connects to every individual, yet the nature of it is highly technical . e-fact-checking systems that connect to medical decisions are largely unused . we examine how clinical experts verify real claims from social media . |
| Approach: | They propose that fact-checking should be approached as an interactive communication problem . they argue that social media and AI have made medical knowledge accessible . |
| Outcome: | The proposed method is based on the work of a clinical expert on social media . it reveals that the method is difficult to connect claims to clinical trials . |
Induct-Learn: Short Phrase Prompting with Instruction Induction (2024.emnlp-main)
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| Challenge: | Existing methods for generating instructions from demonstrations rely on large datasets or numerous examples, which is impractical and costly in real-world scenarios. |
| Approach: | They propose a task-level framework that induces pseudo instructions from a few demonstrations and a short phrase, adding a CoT process into existing demonstrations. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on two datasets and exhibits cross-model adaptability and lower cost. |
GA-S3: Comprehensive Social Network Simulation with Group Agents (2025.findings-acl)
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| Challenge: | Existing social network simulations focus on discrete events or system dynamics instead of elucidating underlying mechanisms or causal relationships. |
| Approach: | They propose a Social network simulation system that leverages newly designed Group Agents to make intelligent decisions regarding various online events. |
| Outcome: | The proposed system can make intelligent decisions regarding online events at a manageable cost. |
The Accuracy Paradox in RLHF: When Better Reward Models Don’t Yield Better Language Models (2024.emnlp-main)
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| Challenge: | Reinforcement Learning from Human Feedback (RLHF) significantly enhances Natural Language Processing by aligning language models with human expectations. |
| Approach: | They propose to integrate feedback from humans into RLHF to improve language models by capturing human-like preferences. |
| Outcome: | The proposed model outperforms models trained with moderately accurate reward models on relevance, factuality, and completeness tasks. |
MathAgent: Adversarial Evolution of Constraint Graphs for Mathematical Reasoning Data Synthesis (2026.findings-acl)
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| Challenge: | Current approaches to synthesising high-quality mathematical reasoning data without human priors suffer from mode collapse and limited logical complexity. |
| Approach: | They propose a hierarchical synthesis framework that formulates data synthesis as an unsupervised optimization problem over a constraint graph followed by semantic instantiation rather than a direct text generation task. |
| Outcome: | The proposed framework outperforms widely-used datasets on eight mathematical benchmarks. |
FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence (2024.acl-long)
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Sebastian Joseph, Lily Chen, Jan Trienes, Hannah Göke, Monika Coers, Wei Xu, Byron Wallace, Junyi Jessy Li
| Challenge: | FactPICO is a factuality benchmark for plain language summarization of medical texts describing randomized controlled trials . existing metrics for factual summarizing medical evidence are poorly correlated with expert judgments on the instance level. |
| Approach: | They propose a factuality benchmark for plain language summarization of medical texts . they assess factuality of critical elements of RCTs in those summaries . |
| Outcome: | The proposed benchmark assesses the factuality of medical summaries using LLMs . the summary summators are based on 345 plain language summaires with fine-grained evaluation . |
Large Language Models Can Be Contextual Privacy Protection Learners (2024.emnlp-main)
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Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Quanquan Gu, Haifeng Chen, Wei Wang, Wei Cheng
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable linguistic comprehension and generation capability, but when applied to specialized industries, they face challenges such as hallucination, insufficient domain knowledge, and failing to incorporate the latest domain knowledge. |
| Approach: | They propose a paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding inference-time data privacy. |
| Outcome: | The proposed model protects private data while enhancing the model's knowledge. |
MiLe Loss: a New Loss for Mitigating the Bias of Learning Difficulties in Generative Language Models (2024.findings-naacl)
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| Challenge: | Existing generative language models neglect an inherent challenge in text corpus during training, i.e., the imbalance between frequent tokens and infrequent ones. |
| Approach: | They propose a function to mitigate the imbalance between frequent and infrequent tokens . authors propose 'MiLe Loss' function to assess learning difficulty of tokens during training . |
| Outcome: | Experiments show that models with proposed model can improve on downstream benchmarks. |
Debiasing LLMs by Masking Unfairness-Driving Attention Heads (2026.findings-acl)
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Tingxu Han, Wei Song, Ziqi Ding, Ziming Li, Chunrong Fang, Yuekang Li, Dongfang Liu, Zhenyu Chen, Zhenting Wang
| Challenge: | Existing work probes when biased outputs appear, but gives little insight into the mechanisms that generate them, leaving existing mitigations largely fragile. |
| Approach: | They propose a lightweight debiasing framework that detects bias heads and selectively masks only those heads that activate under DA and CoT. |
| Outcome: | The proposed framework reduces unfairness by 391.9%- 534.5% in both one- and two-turn dialogues. |
Neural Natural Language Inference Models Enhanced with External Knowledge (P18-1)
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| Challenge: | Existing datasets that allow for complex models to be trained are limited . if data is not available, can machines learn all knowledge needed to perform natural language inference? |
| Approach: | They propose to enrich neural natural language inference models with external knowledge . they propose to use this knowledge to build NLI models to leverage it . |
| Outcome: | The proposed models improve on the SNLI and MultiNLI datasets. |
Bridging the Gap between Pre-Training and Fine-Tuning for Commonsense Generation (2023.findings-eacl)
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| Challenge: | Existing methods focusing on this task usually concatenate the concatened concepts words as the inputs of a pre-trained language model (PLM) however, in pre-training, the input is often corrupted sentences with correct word order. |
| Approach: | They propose a two-stage framework to improve the ability of pre-trained language models to deal with masked sentences with incorrect word order and a special token to make the input distribution more similar to the one used in pre-training. |
| Outcome: | The proposed method is able to generate a sentence containing all given concepts and correctly describe the relations between concepts. |
A Survey of Inductive Reasoning for Large Language Models (2026.acl-long)
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Kedi Chen, Dezhao Ruan, Yuhao Dan, Yaoting Wang, Siyu Yan, Xuecheng Wu, Yinqi Zhang, Qin Chen, Jie Zhou, Liang He, Biqing Qi, Linyang Li, Qipeng Guo, Xiaoming Shi, Wei Zhang
| Challenge: | Inductive reasoning is an important task for large language models (LLMs). |
| Approach: | They propose a survey of inductive reasoning for large language models . they categorize methods into three main areas: post-training enhancement, test-time exploration, and data augmentation. |
| Outcome: | The proposed method improves inductive reasoning in large language models. |
DistillCSE: Distilled Contrastive Learning for Sentence Embeddings (2023.findings-emnlp)
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| Challenge: | Existing approaches to sentence embeddings are based on contrastive learning (CL) . |
| Approach: | They propose a framework which performs contrastive learning under the self-training paradigm with knowledge distillation and propose 'Group-P shuffling strategy' and averaging logits from multiple teacher components. |
| Outcome: | The proposed framework outperforms many strong baseline methods and yields a new state-of-the-art performance. |
MixLLM: Dynamic Routing in Mixed Large Language Models (2025.naacl-long)
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Xinyuan Wang, Yanchi Liu, Wei Cheng, Xujiang Zhao, Zhengzhang Chen, Wenchao Yu, Yanjie Fu, Haifeng Chen
| Challenge: | Large Language Models (LLMs) exhibit potential artificial generic intelligence, however, their usage is costly with high response latency. |
| Approach: | They develop a dynamic contextual-bandit-based routing system for query-LLM assignment that leverages query tags to enhance query embeddings. |
| Outcome: | The proposed model maximizes response quality and minimizes cost and latency. |
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections (2022.emnlp-main)
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Chenliang Li, Haiyang Xu, Junfeng Tian, Wei Wang, Ming Yan, Bin Bi, Jiabo Ye, He Chen, Guohai Xu, Zheng Cao, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou, Luo Si
| Challenge: | Existing pre-trained vision-language models suffer from inefficiency and linguistic signal overwhelmed by long visual sequences in cross-modal alignment. |
| Approach: | They propose a vision-language foundation model with cross-modal skip-connections that can be pre-trained end-to-end on large-scale image-text pairs with both discriminative and generative objectives. |
| Outcome: | The proposed model achieves state-of-the-art results on a wide range of vision-language downstream tasks, including image captioning, image-text retrieval, visual grounding and visual question answering. |
LLMs as Collaborator: Demands-Guided Collaborative Retrieval-Augmented Generation for Commonsense Knowledge-Grounded Open-Domain Dialogue Systems (2024.findings-emnlp)
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| Challenge: | Existing methods to capture the unique knowledge demands of LLMs are unsatisfactory because of their overestimation and lack of knowledge. |
| Approach: | They propose a novel approach to capture the unique knowledge demands for each dialogue context using CoT and RAG methods. |
| Outcome: | The proposed model can capture the unique knowledge demands for each dialogue context and bring higher-quality responses. |
Supervised Gradual Machine Learning for Aspect-Term Sentiment Analysis (2023.tacl-1)
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| Challenge: | Recent work shows that Aspect-Term Sentiment Analysis (ATSA) can be performed by Gradual Machine Learning (GML) but the current unsupervised solution is limited by inaccurate knowledge conveyance. |
| Approach: | They propose a supervised approach which leverages binary polarity relations between instances to enable supervised knowledge conveyance. |
| Outcome: | The proposed approach outperforms pure DNN solutions on real benchmark data. |
SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check (2020.acl-main)
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| Challenge: | Existing methods to detect and correct spelling errors in Chinese take external input or just heuristic rules. |
| Approach: | They propose to incorporate phonological and visual similarity knowledge into Chinese language models by using a specialized graph convolutional network. |
| Outcome: | The proposed method outperforms existing models on three human-annotated datasets. |
ComRAG: Retrieval-Augmented Generation with Dynamic Vector Stores for Real-time Community Question Answering in Industry (2025.acl-industry)
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| Challenge: | Existing methods for Community Question Answering (CQA) focus on static knowledge, limiting their applicability to real-world scenarios. |
| Approach: | They propose a retrieval-augmented generation framework for real-time industrial CQA that integrates static knowledge with dynamic historical QA pairs via a centroid-based memory mechanism. |
| Outcome: | The proposed framework outperforms baselines on three industrial CQA datasets and achieves 25.9% improvement in vector similarity, reducing latency by 8.7%–23.3%, and lowering chunk growth from 20.23% to 2.06% over iterations. |
Word Form Matters: LLMs’ Semantic Reconstruction under Typoglycemia (2025.findings-acl)
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| Challenge: | Typoglycemia is a phenomenon where people can read words even when the middle letters of the words are scrambled. |
| Approach: | They propose a reliable metric to quantify the degree of semantic reconstruction and validate its effectiveness. |
| Outcome: | The proposed metric quantifies the degree of semantic reconstruction and validates its effectiveness. |
Unifying Cross-Lingual and Cross-Modal Modeling Towards Weakly Supervised Multilingual Vision-Language Pre-training (2023.acl-long)
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| Challenge: | Existing studies address the problem of translating English data into other languages, but they are limited in form and scale. |
| Approach: | They propose a framework to unify cross-lingual and cross-modal pre-training by using English data. |
| Outcome: | The proposed framework unifies cross-lingual and cross-modal pre-training on different data. |
SeMob: Semantic Synthesis for Dynamic Urban Mobility Prediction (2025.emnlp-main)
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| Challenge: | Existing spatiotemporal models struggle to interpret and adapt to abrupt changes caused by external events. |
| Approach: | They propose a LLM-powered semantic synthesis pipeline that extracts spatiotemporally related text from online texts and integrates it with spatio-temporal data. |
| Outcome: | The proposed framework achieves maximal reductions of 13.92% in MAE and 11.12% in RMSE compared to the spatiotemporal model. |
AutoSDT: Scaling Data-Driven Discovery Tasks Toward Open Co-Scientists (2025.emnlp-main)
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Yifei Li, Hanane Nour Moussa, Ziru Chen, Shijie Chen, Botao Yu, Mingyi Xue, Benjamin Burns, Tzu-Yao Chiu, Vishal Dey, Zitong Lu, Chen Wei, Qianheng Zhang, Tianyu Zhang, Song Gao, Xuhui Huang, Xia Ning, Nesreen K. Ahmed, Ali Payani, Huan Sun
| Challenge: | AutoSDT-5K is the only automatically collected and the largest open dataset for data-driven scientific discovery. |
| Approach: | They propose an automatic pipeline that collects high-quality coding tasks in real-world data-driven discovery workflows. |
| Outcome: | The proposed pipeline synthesizes accurate tasks and tasks from a dataset of 5,404 tasks covering four scientific disciplines and 756 Python packages. |
Combining the Best of Both Worlds: A Method for Hybrid NMT and LLM Translation (2025.findings-acl)
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Zhanglin Wu, Daimeng Wei, Xiaoyu Chen, Hengchao Shang, Jiaxin Guo, Zongyao Li, Yuanchang Luo, Jinlong Yang, Zhiqiang Rao, Hao Yang
| Challenge: | Large language models have advantages over neural machine translation systems, but they suffer from high computational costs and significant latency. |
| Approach: | They propose a scheduling policy that optimizes translation result while ensuring fast speed and as little LLM usage as possible. |
| Outcome: | The proposed model achieves optimal translation performance with less LLM usage on multilingual test sets. |
Following the Autoregressive Nature of LLM Embeddings via Compression and Alignment (2025.emnlp-main)
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Jingcheng Deng, Zhongtao Jiang, Liang Pang, Zihao Wei, Liwei Chen, Kun Xu, Yang Song, Huawei Shen, Xueqi Cheng
| Challenge: | Experimental results demonstrate that our method significantly outperforms traditional contrastive learning approaches when using the same amount of data. |
| Approach: | They propose a new contrastive learning method built on embedding conditional probability distributions that integrates two tasks: information compression and conditional distribution alignment. |
| Outcome: | The proposed method outperforms traditional contrastive learning approaches and achieves comparable performance to state-of-the-art models when using the same amount of data. |
GUI0: Self-Evolving Foundational GUI Agents in Super App Ecosystems (2026.acl-long)
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Xinyi Wang, Wei Dai, Kyle Qiao, Ke Wang, Peng Chen, Gang Cao, null Kangqin, Zhongpu Wang, Xiaode Zhang, Yanming Liu, Jihao Gu, Jingtao Xu, Gong Zhi
| Challenge: | Automated interaction with graphical user interfaces (GUIs) is central to general artificial intelligence, but remains challenging within Super App ecosystems. |
| Approach: | They propose a framework synergizing autonomous data synthesis with dual-agent co-evolution . GUI0 establishes a domain-aware foundation model via synthesized corpora and employs curriculum-driven reinforcement learning . |
| Outcome: | The proposed framework outperforms Gemini-2.5-Pro and Claude-4-Sonnet in the SuperAPP benchmark and has universal efficacy across base models. |
TabDSR: Decompose, Sanitize, and Reason for Complex Numerical Reasoning in Tabular Data (2025.findings-emnlp)
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| Challenge: | Large language models often underperform due to complex queries, noisy data, and limited numerical capabilities. |
| Approach: | They propose a framework that integrates seamlessly with mainstream LLMs to improve tabular reasoning. |
| Outcome: | The proposed framework outperforms existing methods in state-of-the-art analysis. |
Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction (D18-1)
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| Challenge: | Existing neural networks focus on instance representation, and subsampling fails to retain precise spatial relationships between higher-level parts. |
| Approach: | They propose a neural approach based on capsule networks with attention mechanisms to extract relational information from a capsule. |
| Outcome: | The proposed method improves the precision of the predicted relations with different benchmarks. |
mmE5: Improving Multimodal Multilingual Embeddings via High-quality Synthetic Data (2025.findings-acl)
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| Challenge: | Multimodal embedding models encode multimedia inputs into latent vector representations. |
| Approach: | They propose to synthesize multimodal multilingual data using a multimodal large language model . they identify three criteria for high-quality synthetic multimodal data . |
| Outcome: | The proposed model outperforms existing models on the MMEB Benchmark and the XTD benchmark. |
Privacy Checklist: Privacy Violation Detection Grounding on Contextual Integrity Theory (2025.naacl-long)
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| Challenge: | Existing privacy studies focus on sub-fields, but they focus on a few sub-domains. |
| Approach: | They propose to use the Health Insurance Portability and Accountability Act of 1996 as an example to develop a checklist that covers social identities, private attributes, and existing privacy regulations. |
| Outcome: | The proposed checklist covers social identities, private attributes, and existing privacy regulations. |
From Style to Story: A Curriculum Learning Approach for Imitative Novel Generation (2026.findings-acl)
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| Challenge: | Novels create rich, immersive worlds with intricate plots and distinct styles, captivating readers through complex storytelling. |
| Approach: | They propose a novel generation system that imitates novel elements by predicting plot developments and writing concrete details using vivid, expressive language. |
| Outcome: | The novel imitative novel generation system is trained through a curriculum learning paradigm, progressing from low-level stylistic mastery to high-level narrative coherence. |
The GaoYao Benchmark: A Comprehensive Framework for Evaluating Multilingual and Multicultural Abilities of Large Language Models (2026.acl-long)
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Yilun Liu, Chunguang Zhao, Mengyao Piao, Lingqi Miao, Shimin Tao, Minggui HE, Chenxin Liu, Zhang Li, null Mahongxia, Jiaxin Guo, Chen Liu, Liqun Deng, Jiansheng Wei, Xiaojun Meng, Fanyi Du, Daimeng Wei, Yanghua Xiao
| Challenge: | Existing multilingual evaluation benchmarks neglect cultural nuances and lack language coverage in subjective tasks. |
| Approach: | They propose a framework that categorizes evaluation tasks into three cultural layers and nine cognitive sub-layers. |
| Outcome: | The proposed framework surpasses prior coverage by up to 111% on 20+ LLMs. |
Chinese Metaphorical Relation Extraction: Dataset and Models (2023.findings-emnlp)
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| Challenge: | Metaphor identification is a core task in metaphor processing, which involves recognizing and analyzing metaphorical expressions in text. |
| Approach: | They propose a new formulation of metaphor identification as a relation extraction problem . they use a dataset to analyze metaphorical relations between two spans, a target and a source . |
| Outcome: | The proposed model can capture the properties of the target and source in Chinese sentences. |
DiffER: Diffusion Entity-Relation Modeling for Reversal Curse in Diffusion Large Language Models (2026.findings-acl)
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| Challenge: | Existing large language models exhibit unidirectional behavior when processing bidirectional relationships . authors propose a solution to alleviate the reversal curse in Diffusion LLMs . |
| Approach: | They propose a model that addresses the "reversal curse" of bidirectional behavior in large language models . they propose 'entity-aware training' and balanced data construction to alleviate asymmetry and missing relations . |
| Outcome: | The proposed model alleviates the "reversal curse" in Diffusion LLMs . the proposed model employs whole-entity masking to mitigate entity fragmentation . |
OpenUE: An Open Toolkit of Universal Extraction from Text (2020.emnlp-demos)
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Ningyu Zhang, Shumin Deng, Zhen Bi, Haiyang Yu, Jiacheng Yang, Mosha Chen, Fei Huang, Wei Zhang, Huajun Chen
| Challenge: | a large number of natural language processing tasks focus on token-level or sentence-level understandings. |
| Approach: | They propose an open-source and extensible toolkit for various extraction tasks . they deploy an online demo with restful APIs to support real-time extraction . |
| Outcome: | The proposed model can be used to extract information from text without training and deployment. |
Multi-Grained Knowledge Distillation for Named Entity Recognition (2021.naacl-main)
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| Challenge: | Pre-trained big models have delivered top performance in Seq2seq modeling, but their deployments in real-world applications are often hindered by excessive computations and memory demands. |
| Approach: | They propose a distillation scheme to efficiently transfer knowledge from big models to their cheaper counterparts. |
| Outcome: | The proposed scheme maximizes the assimilation of knowledge from the teacher model to the student model. |
OS Agents: A Survey on MLLM-based Agents for Computer, Phone and Browser Use (2025.acl-long)
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Xueyu Hu, Tao Xiong, Biao Yi, Zishu Wei, Ruixuan Xiao, Yurun Chen, Jiasheng Ye, Meiling Tao, Xiangxin Zhou, Ziyu Zhao, Yuhuai Li, Shengze Xu, Shenzhi Wang, Xinchen Xu, Shuofei Qiao, Zhaokai Wang, Kun Kuang, Tieyong Zeng, Liang Wang, Jiwei Li, Yuchen Eleanor Jiang, Wangchunshu Zhou, Guoyin Wang, Keting Yin, Zhou Zhao, Hongxia Yang, Fan Wu, Shengyu Zhang, Fei Wu
| Challenge: | a new generation of (M)LLMs is enabling the creation of superintelligent AI assistants . OS Agents can complete tasks autonomously and have the potential to significantly enhance the lives of billions of users worldwide. |
| Approach: | They propose to build OS Agents that operate within operating systems' GUIs and GUIs . they examine evaluation metrics and benchmarks to identify promising directions . |
| Outcome: | The proposed agents are based on operating systems (OS) and operating systems frameworks. |
Speculative Decoding: Exploiting Speculative Execution for Accelerating Seq2seq Generation (2023.findings-emnlp)
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| Challenge: | Experimental results show draft-then-verify paradigm can achieve around 5x speedup for the popular Transformer architectures with comparable generation quality to beam search decoding. |
| Approach: | They propose to use Spec-Drafter and Spec Verification to accelerate autoregressive (AR) decoding by combining a model optimized for efficient and accurate drafting and a reliable method for verifying the drafted tokens efficiently. |
| Outcome: | The proposed method achieves 5x speedup on seq2seq tasks with comparable generation quality to beam search decoding, refreshing the impression that draft-then-verify paradigm introduces only 1.4x2x speed up. |
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. |
A Multi-answer Multi-task Framework for Real-world Machine Reading Comprehension (D18-1)
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| Challenge: | Existing models of machine reading comprehension (MRC) are based on cloze style questions or crowdworkers given a short passage from well-edited sources. |
| Approach: | They propose a multi-answer multi-task framework that uses multiple reference answers for multiple questions. |
| Outcome: | The proposed model increases the ROUGE-L score on the DuReader dataset from 44.18, the previous state-of-the-art, to 51.09 . |
CompTab: A Comprehensive Benchmark for Real-World TableQA with Complex Reasoning and Irregular Tables (2026.acl-long)
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Zhen Yang, Wei Du, Jie Wang, Wenze Zhou, Xiangfeng Meng, Zhengyang Wang, Suping Sun, Ziwei Du, Haodong Zou, Jie Chen, Yongbin Liu, Shicheng Tan, Jiahao Ying, Shu Zhao
| Challenge: | Existing benchmarks focus on well-structured tables and fail to reflect irregular structures and complex reasoning commonly encountered in real-world scenarios. |
| Approach: | They propose a benchmark to evaluate TableQA under complex reasoning and irregular table conditions. |
| Outcome: | The proposed framework improves generalization and realism of large language models under complex and irregular table conditions. |
Dialect-SQL: An Adaptive Framework for Bridging the Dialect Gap in Text-to-SQL (2025.emnlp-main)
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| Challenge: | Existing Text-to-SQL research focuses on specific database systems, limiting adaptability to different dialects. |
| Approach: | They propose a framework that employs Object Relational Mapping (ORM) code as an intermediate language to bridge this gap. |
| Outcome: | The proposed framework outperforms existing methods that generate SQL queries directly. |
MMSciBench: Benchmarking Language Models on Chinese Multimodal Scientific Problems (2025.findings-acl)
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| Challenge: | Existing scientific benchmarks lack human-annotated difficulty levels and structured taxonomies of scientific concepts. |
| Approach: | They propose a benchmark for evaluating mathematical and physical reasoning through text-only and text-image formats with human-annotated difficulty levels and detailed explanations. |
| Outcome: | The proposed model achieves only 63.77% accuracy and struggles with visual reasoning tasks. |
False Sense of Security: Why Probing-based Malicious Input Detection Fails to Generalize (2026.findings-acl)
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| Challenge: | Recent work has leveraged probing-based approaches to study the separability of malicious and benign inputs in Large Language Models’ internal representations. |
| Approach: | They propose to use probing-based methods to study separability of malicious and benign inputs in LLMs' internal representations to detect harmful and benign content. |
| Outcome: | The proposed methods show that they learn superficial patterns rather than semantic harmfulness. |
Learn to Relax with Large Language Models: Solving Constraint Optimization Problems via Bidirectional Coevolution (2026.acl-long)
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| Challenge: | Large Language Model (LLM)-based optimization has shown promise for autonomous problem solving, but most approaches cast LLMs as passive constraint checkers rather than proactive strategy designers. |
| Approach: | They propose an end-to-end Automated Constraint Optimization method that tightly couples operations-research principles of constraint relaxation with LLM reasoning. |
| Outcome: | Extensive experiments on three challenging COP benchmarks validate AutoCO’s consistent effectiveness and superior performance, especially in hard regimes where current methods degrade. |
Uncertainty Quantification of Large Language Models through Multiple Uncertainty Sources (2026.findings-acl)
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| Challenge: | Existing methods for uncertainty quantification fail to capture multifaceted nature of natural language generation. |
| Approach: | They propose a multi-resource Uncertainty Quantification framework that integrates heterogeneous uncertainty signals into a unified measure. |
| Outcome: | The proposed framework outperforms existing methods on CoQA, NQ_Open, and HotpotQA. |
More is not always better? Enhancing Many-Shot In-Context Learning with Differentiated and Reweighting Objectives (2025.acl-long)
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Xiaoqing Zhang, Ang Lv, Yuhan Liu, Flood Sung, Wei Liu, Jian Luan, Shuo Shang, Xiuying Chen, Rui Yan
| Challenge: | Large language models excel at few-shot in-context learning but performance plateaus as ICL demonstrations increase from a few to many. |
| Approach: | They propose a novel optimization method that optimizes the negative log-likelihood objective and reweights the model to achieve many-shot performance. |
| Outcome: | The proposed method achieves significant performance improvements across a large-scale dataset. |
Document Hashing with Mixture-Prior Generative Models (D19-1)
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| Challenge: | Existing generative hashing methods only consider the use of simple priors, which limits them to further improve their performance. |
| Approach: | They propose to use Gaussian and Bernoulli priors to generate hashing codes . they propose to cast a Gausssian latent representation into binary code . |
| Outcome: | The proposed models outperform existing methods on a benchmark dataset using Gaussian and Bernoulli priors. |
FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models (2025.naacl-long)
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Xin Guo, Haotian Xia, Zhaowei Liu, Hanyang Cao, Zhi Yang, Zhiqiang Liu, Sizhe Wang, Jinyi Niu, Chuqi Wang, Yanhui Wang, Xiaolong Liang, Xiaoming Huang, Bing Zhu, Zhongyu Wei, Yun Chen, Weining Shen, Liwen Zhang
| Challenge: | Large language models have demonstrated outstanding performance in various natural language processing tasks, but their security capabilities in the financial domain have not been explored. |
| Approach: | They propose to use a benchmark to evaluate large language models' financial domain knowledge and practical abilities. |
| Outcome: | The proposed benchmark evaluates large language models' financial domain knowledge and practical abilities. |
TMATH A Dataset for Evaluating Large Language Models in Generating Educational Hints for Math Word Problems (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) are increasingly being applied in education, showing significant potential in personalized instruction, student feedback, and intelligent tutoring systems (ITSs). |
| Approach: | They propose a dataset specifically designed to evaluate LLMs’ ability to generate high-quality hints for Math Word Problems. |
| Outcome: | The proposed dataset shows that LLMs can generate more accurate and contextually appropriate educational hints for math word problems without offering direct answers. |
Detecting Customer Complaint Escalation with Recurrent Neural Networks and Manually-Engineered Features (N19-2)
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Wei Yang, Luchen Tan, Chunwei Lu, Anqi Cui, Han Li, Xi Chen, Kun Xiong, Muzi Wang, Ming Li, Jian Pei, Jimmy Lin
| Challenge: | e-commerce companies often have the option of escalating complaints by filing grievances with a government authority . this is detrimental to an ecommerce company, but this problem is challenging to solve by integrating recurrent neural networks with manually-engineered features. |
| Approach: | They propose a model that integrates recurrent neural networks with manually-engineered features to identify cases where the customer expresses such an intent. |
| Outcome: | The proposed model outperforms baseline models and provides better recall and triage for specialized agents. |
Frustratingly Easy Label Projection for Cross-lingual Transfer (2023.findings-acl)
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| Challenge: | Existing approaches to improve cross-lingual transfer performance are based on word alignment, but no empirical studies have evaluated their effectiveness or limitations. |
| Approach: | They propose a mark-then-translate method that integrates translation and projection by inserting special markers around the labeled spans in the original sentence. |
| Outcome: | The proposed method outperforms word alignment-based methods in 57 languages and three tasks. |
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. |
Accelerate Parallelizable Reasoning via Parallel Decoding within One Sequence (2025.emnlp-main)
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| Challenge: | Existing methods for parallelizable reasoning tasks are inefficient, says a new study . generating lengthy reasoning sequences is computationally expensive and time-consuming, says the study authors . |
| Approach: | They propose a method that decodes multiple tokens per forward pass using a tree-like attention mask . their method achieves nearly 100% speedup in decoding while basically maintaining the answer quality . |
| Outcome: | Experimental results show that the method achieves nearly 100% speedup in decoding while maintaining the answer quality. |
MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings (2026.acl-long)
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| Challenge: | Existing approaches to embed multimodal models face limitations such as suboptimal causal attention in VLMs and limited diversity in training objectives and data. |
| Approach: | They propose a framework for transforming pre-trained VLMs into bidirectional multimodal embedding models. |
| Outcome: | The proposed model improves performance across MMEB and ViDoRe-v2 benchmarks and exhibits strong scalability with both model size and training data on MMEF. |
Layer-Aware Task Arithmetic: Disentangling Task-Specific and Instruction-Following Knowledge (2025.findings-emnlp)
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Yan-Lun Chen, Yi-Ru Wei, Chia-Yi Hsu, Chia-Mu Yu, Chun-Ying Huang, Ying-Dar Lin, Yu-Sung Wu, Wei-Bin Lee
| Challenge: | Large language models (LLMs) demonstrate strong task-specific capabilities through fine-tuning, but merging multiple fine- tuned models often leads to degraded performance due to overlapping instruction-following components. |
| Approach: | They propose a layer-wise approach that assigns layer-specific weights to task vectors based on their alignment with instruction-following or task-specific components. |
| Outcome: | The proposed approach outperforms existing methods in learning and forgetting tasks while preserving overall model utility. |
KNSE: A Knowledge-aware Natural Language Inference Framework for Dialogue Symptom Status Recognition (2023.findings-acl)
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| Challenge: | Existing frameworks for symptom status recognition in doctor-patient dialogues are inadequate. |
| Approach: | They propose a framework for symptom status recognition that formalizes a natural language inference task . they generate knowledge about the symptom and a hypothesis about its status for each symptom . |
| Outcome: | The proposed framework outperforms baselines and has advantages in cross-disease and cross-symptom scenarios. |
Semi-supervised Fine-tuning for Large Language Models (2025.findings-naacl)
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| Challenge: | Existing LLMs require labeled data, which can be costly in real-world applications. |
| Approach: | They propose a framework that can fully exploit labeled and unlabeled data for LLM fine-tuning . they conducted experiments using GPT-4o-mini and Llama-3.1 on seven general or domain-specific datasets . |
| Outcome: | The proposed framework can fully exploit labeled and unlabeled data for LLM alignment from a propagate-and-select manner. |
SessionIntentBench: A Multi-task Inter-session Intention-shift Modeling Benchmark for E-commerce Customer Behavior Understanding (2026.findings-acl)
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Yuqi Yang, Weiqi Wang, Baixuan Xu, Wei Fan, Qing Zong, Chunkit Chan, Zheye Deng, Xin Liu, Yifan Gao, Changlong Yu, Chen Luo, Yang Li, Zheng Li, Qingyu Yin, Bing Yin, Yangqiu Song
| Challenge: | Existing models fail to capture and model customer intention effectively because of insufficient information exploitation and only apparent information like descriptions and titles are used. |
| Approach: | They propose to exploit existing session data to capture and model intention in E-commerce product purchase sessions using a multimodal benchmark. |
| Outcome: | The proposed framework can bridge the gap between intention understanding in simplified research cases like co-buy intention and more complex yet practical scenarios like session history. |
Automatic Term Name Generation for Gene Ontology: Task and Dataset (2020.findings-emnlp)
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Yanjian Zhang, Qin Chen, Yiteng Zhang, Zhongyu Wei, Yixu Gao, Jiajie Peng, Zengfeng Huang, Weijian Sun, Xuanjing Huang
| Challenge: | Gene Ontology (GO) terms are used to describe gene function in biology and bio-medicine. |
| Approach: | They propose a task to generate term names for GO and build a large-scale benchmark dataset. |
| Outcome: | The proposed model outperforms baselines by incorporating the relations between genes, words and terms for term name generation. |
Knowledge Association with Hyperbolic Knowledge Graph Embeddings (2020.emnlp-main)
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| Challenge: | Existing methods for knowledge graphs (KGs) depend on high embedding dimensions and hierarchical structures to achieve expressiveness. |
| Approach: | They propose a hyperbolic relational graph neural network for KG embedding and capture knowledge associations with a high-dimensional transformation. |
| Outcome: | Experiments on entity alignment and type inference show the proposed method is effective and efficient. |
Privacy Evaluation Benchmarks for NLP Models (2024.findings-emnlp)
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| Challenge: | Several kinds of privacy attacks are studied in depth, but they are non-systematic and lack a comprehensive understanding of the impact caused by the attacks. |
| Approach: | They propose a privacy attack and defense evaluation benchmark in the field of NLP . they propose an improved attack method and a chained framework for privacy attacks . |
| Outcome: | The proposed framework can be chained to achieve a higher-level attack objective. |
SampleMix: A Sample-wise Pre-training Data Mixing Strategy by Coordinating Data Quality and Diversity (2025.findings-emnlp)
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Xiangyu Xi, Deyang Kong, Jian Yang, Jiawei Yang, Zhengyu Chen, Wei Wang, Jingang Wang, Xunliang Cai, Shikun Zhang, Wei Ye
| Challenge: | Existing methods for pretraining data mixing for large language models neglect significant inter-domain overlaps and commonalities, failing to control the global diversity of the constructed training dataset. |
| Approach: | They propose a sample-wise data mixture approach that performs global cross-domain sampling by systematically evaluating the quality and diversity of each sample. |
| Outcome: | The proposed method exceeds existing domain-based methods in multiple downstream tasks and perplexity assessments. |
Continual Named Entity Recognition without Catastrophic Forgetting (2023.emnlp-main)
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| Challenge: | Named Entity Recognition (CNER) is a burgeoning area of research . a new paradigm has ushered NER into a non-entity type at the current step t . |
| Approach: | They propose a pooled feature distillation loss that skillfully navigates the trade-off between retaining knowledge of old entity types and acquiring new ones. |
| Outcome: | The proposed method outperforms state-of-the-art approaches on ten CNER settings using three datasets. |
TagRouter: Learning Route to LLMs through Tags for Open-Domain Text Generation Tasks (2025.findings-acl)
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| Challenge: | Existing models with limited performance and limited training can be difficult to use in large-scale applications. |
| Approach: | They propose a training-free model routing method that optimizes synergy among multiple LLMs for open-domain text generation tasks. |
| Outcome: | The proposed method outperforms 13 baseline models and reduces costs by 17.20%. |
Separate the Wheat from the Chaff: Winnowing Down Divergent Views in Retrieval Augmented Generation (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) lack robustness in knowledge-intensive tasks due to noisy or irrelevant retrieved data. |
| Approach: | They propose a multi-agent debate-based RAG framework that integrates external knowledge sources into large language models to improve their accuracy. |
| Outcome: | The proposed framework is unsupervised and leverages pretrained LLMs without fine-tuning, making it easily adaptable to various tasks. |
Ranking-Based Autoencoder for Extreme Multi-label Classification (N19-1)
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| Challenge: | Existing methods to solve label dependency and noisy labeling problems are limited . experimental results show the proposed method is competitive to state-of-the-art methods . |
| Approach: | They propose a deep learning XML method with word-vector-based self-attention followed by ranking-based AutoEncoder architecture to solve these problems. |
| Outcome: | The proposed method is competitive to state-of-the-art methods on benchmark datasets. |
On Task-personalized Multimodal Few-shot Learning for Visually-rich Document Entity Retrieval (2023.findings-emnlp)
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| Challenge: | Visually-rich document entity retrieval (VDER) is an important topic in industrial NLP applications. |
| Approach: | They propose a task-aware meta-learning framework to tackle the problem of visually-rich document entity retrieval (VDER) they adopt a hierarchical decoder and employ contrastive learning to achieve this goal. |
| Outcome: | The proposed framework significantly improves the robustness of popular meta-learning baselines. |
Memory-Augmented LLM-based Multi-Agent System for Automated Feature Generation on Tabular Data (2026.acl-long)
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| Challenge: | Existing methods for automated feature generation rely on predefined operator libraries and do not incorporate feature semantics, limiting their ability to produce high-quality features. |
| Approach: | They propose a Memory-Augmented LLM-based Multi-Agent System (MALMAS) that decomposes the generation process into agents with distinct responsibilities. |
| Outcome: | The proposed method extracts informative features from raw tabular data without manual intervention and is crucial for accurate, generalizable machine learning. |
EquiBench: Benchmarking Large Language Models’ Reasoning about Program Semantics via Equivalence Checking (2025.emnlp-main)
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Anjiang Wei, Jiannan Cao, Ran Li, Hongyu Chen, Yuhui Zhang, Ziheng Wang, Yuan Liu, Thiago S. F. X. Teixeira, Diyi Yang, Ke Wang, Alex Aiken
| Challenge: | EquiBench is a new benchmark to evaluate large language models' ability to reason about program semantics . Unlike natural language, code is executable. |
| Approach: | They propose a benchmark to evaluate large language models through equivalence checking . EquiBench consists of 2400 program pairs across four languages and six categories . |
| Outcome: | The proposed benchmark consists of 2400 program pairs across four languages and six categories. |
Making Pretrained Language Models Good Long-tailed Learners (2022.emnlp-main)
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| Challenge: | Prompt-tuning has shown appealing performance in few-shot classification . however, it is less promising in long-tailed classification due to long tail . |
| Approach: | They propose to use prompt-tuning to make pretrained language models at least good long-tailed learners by bridging the gap between prompt- and commonly used finetun. |
| Outcome: | The proposed method makes pretrained language models at least good long-tailed learners, bridging the gap between prompt-tuning and finetunation. |
On Synthetic Data for Back Translation (2022.naacl-main)
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| Challenge: | Existing studies on back translation (BT) focus on beam search or random sampling . a new method to generate synthetic data with a backward model is proposed to improve BT performance. |
| Approach: | They propose a method to generate synthetic data to trade off quality and importance factors . back translation (BT) is one of the most significant technologies in NMT research fields . |
| Outcome: | The proposed method outperforms the baseline methods on WMT14 DE-EN, EN-DE, and RU-EN benchmark tasks. |
A Progressive Framework for Role-Aware Rumor Resolution (2022.coling-1)
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| Challenge: | Existing methods for rumor resolution ignore intrinsic propagation mechanisms of rumors and present poor adaptive ability when unprecedented news emerges. |
| Approach: | They propose to identify triggering posts and exploit their characteristics to facilitate rumor verification. |
| Outcome: | The proposed model and scheme exploits rumor diffusion patterns and linguistic features to facilitate verification. |
Dual Class Knowledge Propagation Network for Multi-label Few-shot Intent Detection (2023.acl-long)
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| Challenge: | Existing studies on multi-label intent detection are confused by the identical representation of the utterance with multiple labels and overlook the intrinsic intra-class and inter-class relations. |
| Approach: | They propose a dual class knowledge propagation network to learn well-separated representations for utterances with multiple intents. |
| Outcome: | The proposed method outperforms baselines on two multi-label intent datasets by a large margin. |
MMEvalPro: Calibrating Multimodal Benchmarks Towards Trustworthy and Efficient Evaluation (2025.naacl-long)
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Jinsheng Huang, Liang Chen, Taian Guo, Fu Zeng, Yusheng Zhao, Bohan Wu, Ye Yuan, Haozhe Zhao, Zhihui Guo, Yichi Zhang, Jingyang Yuan, Wei Ju, Luchen Liu, Tianyu Liu, Baobao Chang, Ming Zhang
| Challenge: | Large Multimodal Models (LMMs) exhibit impressive cross-modal understanding and reasoning abilities, but many benchmarks suffer from systematic biases. |
| Approach: | They propose a benchmark to avoid Type-I errors by creating one perception question and one knowledge anchor question through a meticulous annotation process. |
| Outcome: | The proposed benchmark avoids Type-I errors while maintaining reliability of MCQ evaluations. |
OpenRLHF: A Ray-based Easy-to-use, Scalable and High-performance RLHF Framework (2025.emnlp-demos)
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Jian Hu, Xibin Wu, Wei Shen, Jason Klein Liu, Weixun Wang, Songlin Jiang, Haoran Wang, Hao Chen, Bin Chen, Wenkai Fang, null Xianyu, Yu Cao, Haotian Xu, Yiming Liu
| Challenge: | Existing RLHF frameworks face inference bottlenecks and complexity barriers restricting their accessibility for newcomers. |
| Approach: | They propose an open-source RLHF framework that can be used to train large language models. |
| Outcome: | The proposed framework achieves superior training efficiency with speedups ranging from 1.22 to 1.68 across different model sizes compared to state-of-the-art frameworks, while requiring significantly fewer lines of code for implementation. |
Privacy-Preserving Reasoning with Knowledge-Distilled Parametric Retrieval Augmented Generation (2026.findings-acl)
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| Challenge: | Existing RAG systems require uploading local documents to the cloud, resulting in inference latency and poor generalization on out-of-distribution (OOD) inputs. |
| Approach: | They propose a generalizable knowledge-distilled parametric RAG model aligned with standard RAG in document structure and parameter activation. |
| Outcome: | The proposed model outperforms baselines in accuracy and generalizes well on out-of-distribution (OOD) data. |
Prompt-based Zero-shot Text Classification with Conceptual Knowledge (2023.acl-srw)
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| Challenge: | Existing approaches to pre-training language models rely on verbalizers to translate the predicted vocabulary to task-specific labels. |
| Approach: | They propose a framework that incorporates conceptual knowledge for text classification in the extreme zero-shot setting. |
| Outcome: | The proposed framework outperforms prompt-based approaches on four widely-used datasets for sentiment analysis and topic detection on the same experimental settings. |
Conformal Feedback Alignment: Quantifying Answer-Level Reliability for Robust LLM Alignment (2026.findings-eacl)
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| Challenge: | Existing uncertainty-aware approaches weight preferences, but ignore reliability of the answers being compared. |
| Approach: | They propose a framework that grounds preference weighting in Conformal Predictions to address this problem. |
| Outcome: | The proposed framework improves alignment robustness and data efficiency across different datasets. |
An Exploration of Mamba for Speech Self-Supervised Models (2026.acl-long)
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Tzu-Quan Lin, Heng-Cheng Kuo, Tzu-Chieh Wei, Hsi-Chun Cheng, Chun Wei Chen, Hsien-Fu Hsiao, Yu Tsao, Hung-yi Lee
| Challenge: | Mamba-based SSL models are promising for long-sequence modeling, speech unit extraction, and speech self-supervised learning. |
| Approach: | They propose to use Mamba-based HuBERT models as an alternative to Transformer-based SSL architectures. |
| Outcome: | The proposed models outperform Transformer-based models in language modeling tasks while showing superior performance on streaming ASR. |
Gen-SQL: Efficient Text-to-SQL By Bridging Natural Language Question And Database Schema With Pseudo-Schema (2025.coling-main)
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| Challenge: | Recent studies have shifted paradigms and leveraged Large Language Models (LLMs) to tackle the challenging task of Text-to-SQL. |
| Approach: | They propose a framework that leverages large language models to generate SQL queries . they exploit prior knowledge from the LLM to enhance embedding-based retriever . |
| Outcome: | The proposed method improves embedding-based retriever and reduces cost. |
Structured Optimal Brain Pruning for Large Language Models (2024.emnlp-main)
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| Challenge: | Existing pruning methods for Large Language Models rely on unstructured pruning or require special hardware to accelerate computation. |
| Approach: | They propose a retraining-free structured pruning method called SoBP . they evaluate the effectiveness of SoBP across 14 models from 3 LLM families . |
| Outcome: | The proposed method outperforms current state-of-the-art pruning methods on 8 datasets. |
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. |
Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction (2020.coling-main)
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| Challenge: | Distant Supervision (DS) generates large-scale annotated data but has wrong labels that result in incorrect evaluation scores during testing. |
| Approach: | They build a dataset using DS-generated data as training data and hire annotators to label test data. |
| Outcome: | The proposed dataset NYTH has a much larger test set and performs more accurate and consistent evaluation. |
Memory-augmented Query Reconstruction for LLM-based Knowledge Graph Reasoning (2025.findings-acl)
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| Challenge: | Existing methods that confuse tool utilization with knowledge reasoning harm readability and give rise to tool invocation hallucinations. |
| Approach: | They propose to decouple LLM from tool invocation tasks by establishing a memory module with explicit descriptions of query statements and a query memory module to facilitate the KGQA process. |
| Outcome: | The proposed method achieves state-of-the-art on WebQSP and CWQ benchmarks. |
Self-Adaptive Sampling for Accurate Video Question Answering on Image Text Models (2024.findings-naacl)
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| Challenge: | Image–text models (ITMs) are the prevalent architecture to solve video question–answering tasks, which requires only a few input frames to save huge computational cost compared to video–language models. |
| Approach: | They propose a sampling method based on question–frame correlation that is efficient for the few-frame situations. |
| Outcome: | The proposed method can boost the performance of image–text pretrained models and have a wide application scenario in terms of model architectures and dataset types. |
FuseSearch: Learning Adaptive Parallel Execution for Efficient Code Localization (2026.findings-acl)
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Ke Xu, Siyang Xiao, Ming Liang, Yichen Yu, Zhixiang Wang, Jingxuan Xu, Dajun Chen, Wei Jiang, Yong Li
| Challenge: | Existing parallel code localization agents suffer from a 34.9% redundant tool invocation rate . specialized localization agent that operate as dedicated search components is needed to achieve high localization accuracy. |
| Approach: | They propose a parallel code localization system that reframes parallel code execution as a quality–efficiency co-optimization problem. |
| Outcome: | The proposed method matches SOTA performance while being 93.6% faster. |
Look Again, Think Slowly: Enhancing Visual Reflection in Vision-Language Models (2025.emnlp-main)
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| Challenge: | Recent advances in text-only "slow thinking" reasoning have prompted efforts to transfer this capability to vision-language models (VLMs). |
| Approach: | They propose a VRM Reflection-V which enhances visual reflection based on reasoning data for cold-start and reward design for reinforcement learning. |
| Outcome: | The proposed model improves visual reflection for cold-start and reward design for reinforcement learning (RL) it maintains a stronger and more consistent reliance on visual information during visual reasoning, indicating effective enhancement in visual reflection capabilities. |