Papers by Chao Wang
DisCo_Speech: Controllable Zero-Shot Speech Generation with A Disentangled Speech Codec (2026.acl-long)
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Tao Li, Wenshuo Ge, Zhichao Wang, Zihao Cui, Yong Ma, Yingying Gao, Chao Deng, Shilei Zhang, Junlan Feng
| Challenge: | DisCo-Speech is a zero-shot controllable text-to-speech framework . standard codecs entangle timbre and prosody, which hinders independent control in continuation-based LMs. |
| Approach: | They propose a disentangled speech codec and an LM-based generator to solve this problem . they propose fusion and reconstruction that merges content and prosody into unified tokens . |
| Outcome: | DisCo-Speech achieves competitive voice cloning and superior zero-shot prosody control. |
Benchmarking the Detection of LLMs-Generated Modern Chinese Poetry (2025.findings-emnlp)
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| Challenge: | Detecting AI-generated poetry is difficult due to distinctive characteristics of modern Chinese poetry. |
| Approach: | They propose a benchmark for detecting AI-generated modern Chinese poetry . they use a high-quality dataset and systematic performance assessments . |
| Outcome: | The proposed benchmark is based on a high-quality dataset of 800 poems written by six professional poets and 41,600 poems generated by four mainstream LLMs. |
Explicit Utilization of General Knowledge in Machine Reading Comprehension (P19-1)
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| Challenge: | Existing MRC models are unable to integrate general knowledge with human knowledge. |
| Approach: | They propose a data enrichment method which uses WordNet to extract inter-word semantic connections as general knowledge from each given passage-question pair. |
| Outcome: | The proposed model outperforms state-of-the-art models and is robust to noise. |
Doc2SoarGraph: Discrete Reasoning over Visually-Rich Table-Text Documents via Semantic-Oriented Hierarchical Graphs (2024.lrec-main)
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| Challenge: | Existing work on document visual question answering fails to capture the differences and correlations between elements of a document and associated questions. |
| Approach: | They propose a document-visual question-answering challenge that exploits element-level semantics and employs hierarchical Graph structures to capture differences and correlations between elements. |
| Outcome: | The proposed model surpasses the state-of-the-art method and large language model in terms of Exact Match (EM) metric, demonstrating exceptional effectiveness. |
StarFlow: Generating Structured Workflow Outputs From Sketch Images (2026.eacl-long)
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Patrice Bechard, Chao Wang, Amirhossein Abaskohi, Juan A. Rodriguez, Christopher Pal, David Vazquez, Spandana Gella, Sai Rajeswar, Perouz Taslakian
| Challenge: | Despite being widely used, building workflows can be complex, often requiring manual configuration through low-code platforms or visual programming tools. |
| Approach: | They propose a framework for generating structured workflow outputs from sketches using vision-language models to automate the process. |
| Outcome: | The proposed framework outperforms large vision-language models in the task of generating structured workflow outputs from sketches and diagrams. |
Towards Alleviating the Object Bias in Prompt Tuning-based Factual Knowledge Extraction (2023.findings-acl)
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| Challenge: | Existing methods to optimize prompts for factual knowledge extraction are undesirable object bias. |
| Approach: | They propose a prompt tuning method that reduces object bias and improves factual knowledge extraction. |
| Outcome: | The proposed method reduces object bias and improves accuracy of factual knowledge extraction. |
CTSM: Combining Trait and State Emotions for Empathetic Response Model (2024.lrec-main)
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| Challenge: | Empathetic response generation attempts to empower dialogue systems to perceive speakers’ emotions and generate empathetic responses accordingly. |
| Approach: | They propose to combine trait and state emotions for Empathetic Response Model to enable dialogue systems to perceive speakers' emotions and generate empathetic responses accordingly. |
| Outcome: | The proposed model outperforms state-of-the-art models and generates more empathetic responses. |
Beyond Token Length: Step Pruner for Efficient and Accurate Reasoning in Large Language Models (2026.findings-acl)
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| Challenge: | Existing reinforcement learning methods for large reasoning models suffer from excessive verbosity, known as "overthinking." Existing models penalize generated tokens to promote conciseness, but these methods encounter two challenges: they may develop hacking behavior in later stages of training by discarding reasoning steps. |
| Approach: | They propose a framework that steers large reasoning models toward more efficient reasoning . they prioritize correctness while imposing penalties for redundant steps . |
| Outcome: | The proposed framework reduces token usage by 69.7% on AIME24. |
Spec-VLA: Speculative Decoding for Vision-Language-Action Models with Relaxed Acceptance (2025.emnlp-main)
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| Challenge: | Visual Language Models (VLMs) have significant parameter size and autoregressive (AR) decoding nature impose considerable computational demands on VLA models. |
| Approach: | They propose a framework to relax acceptance utilizing the relative distances represented by the action tokens of the VLA model. |
| Outcome: | Empirical results show that the proposed framework improves the speed of the prediction task by 44%. |
RepoDistill: Distilling Repository Knowledge through Compression-Aware Budget Allocation and Policy Optimization (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have strong performance on code translation tasks, but they struggle with repository-level scenarios where context is extensive and interdependent. |
| Approach: | They propose a framework that integrates retrieval with learning budget allocation for fine-grained context compression. |
| Outcome: | The proposed framework outperforms baselines on SWE-QA, CoderEval, and LongCodeU. |
GL-GAN: Perceiving and Integrating Global and Local Styles for Handwritten Text Generation with Mamba (2025.coling-main)
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| Challenge: | Existing models lack the ability to perceive and integrate handwriting styles, which affects the realism of the synthesized samples. |
| Approach: | They propose a Hybrid Style Encoder that captures global and local styles and integrates them into a Dynamic Feature Enhancement Module (DFEM). |
| Outcome: | The proposed model outperforms state-of-the-art models on two widely used handwriting datasets and can provide training data for handwritten text recognition and signature verification. |
In-context Contrastive Learning for Event Causality Identification (2024.emnlp-main)
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| Challenge: | Recent prompt learning-based approaches have shown promising improvements on the ECI task . however, they are subject to the delicate design of multiple prompts and positive correlations between the main task and derivate tasks. |
| Approach: | They propose an event causality identification model that uses contrastive learning to enhance both positive and negative demonstrations. |
| Outcome: | The proposed model improves on the event-related causality identification task . it uses contrastive learning to enhance both positive and negative demonstrations . |
Log-FGAER: Logic-Guided Fine-Grained Address Entity Recognition from Multi-Turn Spoken Dialogue (2023.emnlp-main)
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| Challenge: | Existing name entity recognition methods combine pre-trained language models with supervised models such as BiLSTM/LSTM-CRF to perform poorly in a spoken dialogue context. |
| Approach: | They propose a logic-guided fine-grained address recognition method that softly applies the logic rule to improve the accuracy of FGAER. |
| Outcome: | The proposed method improves fine-grained address entity recognition from multi-turn spoken dialogues. |
An Optimizable Suffix Is Worth A Thousand Templates: Efficient Black-box Jailbreaking without Affirmative Phrases via LLM as Optimizer (2025.findings-naacl)
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| Challenge: | Existing jailbreaking methods generate harmful and unethical content when subjected to jailbreaking attacks. |
| Approach: | They propose a black-box jailbreaking method with optimizable suffixes that translate jailbreaking objectives into natural language instructions. |
| Outcome: | The proposed method outperforms existing methods by 2.4 times in the ASR of three open-source LLMs and GPT-3.5-Turbo. |
CoCo: Coherence-Enhanced Machine-Generated Text Detection Under Low Resource With Contrastive Learning (2023.emnlp-main)
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| Challenge: | Recent proposed methods fail to consider the linguistic structure of texts and lack the ability to handle the low-resource problem. |
| Approach: | They propose a coherence-based contrastive learning model named CoCo to detect MGTs under low-resource scenario. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two datasets and two self-constructed datasets. |
PLATO-Ad: A Unified Advertisement Text Generation Framework with Multi-Task Prompt Learning (2022.emnlp-industry)
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Zeyang Lei, Chao Zhang, Xinchao Xu, Wenquan Wu, Zheng-yu Niu, Hua Wu, Haifeng Wang, Yi Yang, Shuanglong Li
| Challenge: | Online advertisement text generation models have achieved remarkable success in generating high-quality text ads, but some challenges remain, such as low-resource scenarios and training efficiency for multiple ad tasks. |
| Approach: | They propose a unified text ad generation framework with multi-task prompt learning to tackle low-resource ade generation problem and a multi-step prompt learning mechanism to efficiently solve multiple aed generation tasks. |
| Outcome: | The proposed framework outperforms the state-of-the-art on offline and online metrics. |
Make-A-Voice: Revisiting Voice Large Language Models as Scalable Multilingual and Multitask Learners (2024.acl-long)
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Rongjie Huang, Chunlei Zhang, Yongqi Wang, Dongchao Yang, Jinchuan Tian, Zhenhui Ye, Luping Liu, Zehan Wang, Ziyue Jiang, Xuankai Chang, Jiatong Shi, Chao Weng, Zhou Zhao, Dong Yu
| Challenge: | Large language models (LLMs) have been used for general-purpose interfaces across multiple tasks and languages. |
| Approach: | They propose to use large language models as a general-purpose interface across multiple tasks and languages. |
| Outcome: | The proposed model performs better on 200K hours of 6-language data for voice generation applications. |
Rehearsal-free Continual Language Learning via Efficient Parameter Isolation (2023.acl-long)
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Zhicheng Wang, Yufang Liu, Tao Ji, Xiaoling Wang, Yuanbin Wu, Congcong Jiang, Ye Chao, Zhencong Han, Ling Wang, Xu Shao, Wenqiu Zeng
| Challenge: | Existing methods for learning continual tasks do not cache history data, which makes the problem more challenging. |
| Approach: | They propose a method that allocates a small portion of private parameters and learns them with a shared pre-trained model. |
| Outcome: | The proposed method is comparable to existing methods and comparable to those using historical data. |
BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers (2024.emnlp-main)
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Ran Xu, Wenqi Shi, Yue Yu, Yuchen Zhuang, Yanqiao Zhu, May Dongmei Wang, Joyce Ho, Chao Zhang, Carl Yang
| Challenge: | Developing effective biomedical retrieval models is important for excelling at knowledge-intensive biomedically tasks but still challenging due to the lack of sufficient publicly annotated biomedic data and computational resources. |
| Approach: | They propose a series of dense retrievers for enhancing biomedical retrieval via unsupervised pre-training on large biomedically corpora, followed by instruction fine-tuning on a combination of labeled datasets and synthetic pairs. |
| Outcome: | Experiments on 5 biomedical tasks across 11 datasets confirm the performance of the retrieval model on various biomedically demanding tasks. |
Exploring Cross-Lingual Transfer Learning with Unsupervised Machine Translation (2021.findings-acl)
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| Challenge: | a new CLTL model is proposed to facilitate cross-linguistic transfer learning between distant languages . a key to CLTL is to learn a shared representation space for the given source-target language pair. |
| Approach: | They propose a new CLTL model that integrates machine translation with MT . they use an unannotated data technique to make use of the model's pre-training and fine-tuning . |
| Outcome: | The proposed model achieves better CLTL performance than the baseline model without more annotated data. |
Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training (2025.naacl-long)
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Yuchen Zhuang, Jingfeng Yang, Haoming Jiang, Xin Liu, Kewei Cheng, Sanket Lokegaonkar, Yifan Gao, Qing Ping, Tianyi Liu, Binxuan Huang, Zheng Li, Zhengyang Wang, Pei Chen, Ruijie Wang, Rongzhi Zhang, Nasser Zalmout, Priyanka Nigam, Bing Yin, Chao Zhang
| Challenge: | Existing LLMs often rely on complex prompting or extensive fine-tuning to introduce new capabilities while preserving strong generalizability. |
| Approach: | They propose a large-scale pre-training corpus to enhance LLM agents' capabilities . they use 103B agent-specific data encompassing 76,537 APIs . |
| Outcome: | The proposed training corpus outperforms open-source LLMs and commercial LLM agents on three agent benchmarks. |
Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework (2021.findings-emnlp)
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| Challenge: | Named entity recognition (NER) is a language understanding task that requires large amounts of in-domain labeled data to perform well. |
| Approach: | They propose a framework which learns from natural language supervision and enables the identification of never-seen entity classes without using in-domain labeled data. |
| Outcome: | The proposed method brings 10%, 23% and 26% improvements over baselines in few-shot learning, domain transfer and zero-shot settings respectively. |
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. |
LLM×MapReduce-V3: Enabling Interactive In-Depth Survey Generation through a MCP-Driven Hierarchically Modular Agent System (2025.emnlp-demos)
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Yu Chao, Siyu Lin, Xiaorong Wang, Zhu Zhang, Zihan Zhou, Haoyu Wang, Shuo Wang, Jie Zhou, Zhiyuan Liu, Maosong Sun
| Challenge: | Generating high-quality long-form survey articles poses significant challenges to AI Agent systems. |
| Approach: | They propose a hierarchically modular agent system for long-form survey generation . they use atomic models to implement skeleton initialization, digest construction, and skelet refinement . human evaluations demonstrate system surpasses representative baselines . |
| Outcome: | The proposed system surpasses representative baselines in both content depth and length, highlighting the strength of MCP-based modular planning. |
Improving Back-Translation with Uncertainty-based Confidence Estimation (D19-1)
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| Challenge: | Despite the success of low-resource neural machine translation, there is a data scarcity problem in many languages . large-scale, high-quality, and widecoverage bilingual corpora do not exist for most language pairs . |
| Approach: | They propose to quantify confidence of NMT models based on model uncertainty . they propose to use uncertainty-based confidence measures to improve back-translation . |
| Outcome: | The proposed model outperforms conventional statistical machine translation (SMT) on Chinese-English and English-German translation tasks. |
An Effective Pronunciation Assessment Approach Leveraging Hierarchical Transformers and Pre-training Strategies (2024.acl-long)
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Bi-Cheng Yan, Jiun-Ting Li, Yi-Cheng Wang, Hsin Wei Wang, Tien-Hong Lo, Yung-Chang Hsu, Wei-Cheng Chao, Berlin Chen
| Challenge: | Existing attempts to quantify a second language learner’s pronunciation proficiency in a target language often sideline the hierarchy of linguistic units and relatedness among the pronunciation aspects. |
| Approach: | They propose a hierarchical automatic pronunciation assessment method that models the intrinsic structures of an utterance while considering the relatedness among the pronunciation aspects. |
| Outcome: | The proposed method can be used to quantify a second language learner’s pronunciation proficiency in a target language by providing fine-grained feedback with multiple pronunciation aspect scores at various linguistic levels. |
Bayesian Example Selection Improves In-Context Learning for Speech, Text and Visual Modalities (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) can adapt to new tasks easily and efficiently in a training-free manner. |
| Approach: | They propose to use eBayesian in-context example selection method to extend the inference probability conditioned on in-constitut examples based on Bayes’ theorem to select in-strategy examples . Experimental results show the efficacy and robustness of their method on various models, tasks and modalities. |
| Outcome: | The proposed method is based on the eBayesian in-context example selection approach. |
Train Once, and Decode As You Like (2020.coling-main)
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| Challenge: | Existing approaches to machine translation support autoregressive, semi-autoregressive and refinement-based non-auto-regressives. |
| Approach: | They propose a unified approach for supporting different generation manners of machine translation including autoregressive, semi-autoregressive and refinement-based non-auto-regressives. |
| Outcome: | The proposed approach achieves better or competitive translation performance compared with strong baseline models in all the settings. |
Knowledge-Selective Pretraining for Attribute Value Extraction (2023.findings-emnlp)
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Hui Liu, Qingyu Yin, Zhengyang Wang, Chenwei Zhang, Haoming Jiang, Yifan Gao, Zheng Li, Xian Li, Chao Zhang, Bing Yin, William Wang, Xiaodan Zhu
| Challenge: | Existing methods for AVE are limited on rare attributes due to poor generalization ability. |
| Approach: | They propose to leverage pretraining and transfer learning to address weaknesses in existing methods. |
| Outcome: | The proposed method achieves new state-of-the-art performance without pretraining on rare attributes with limited training resources. |
Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attacks (2024.acl-long)
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| Challenge: | Existing studies on this topic focus on the robustness of specific detectors or particular attack methods. |
| Approach: | They stress test the detectors’ robustness to malicious attacks under realistic scenarios using LLMs and metric-based detectors. |
| Outcome: | The proposed methods are based on a set of LLM-based models and their performance is compared under different budget levels. |
TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance (2021.acl-long)
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Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, Tat-Seng Chua
| Challenge: | Existing QA systems focus on unstructured text, structured knowledge base, or semi-structured tables. |
| Approach: | They propose a large-scale question answering model based on financial reports . numerical reasoning is usually required to infer the answer . |
| Outcome: | The proposed model achieves 58.0% inF1, an 11.1% increase over the baseline model, but still lags behind the best human model. |
WorkForceAgent-R1: Incentivizing Reasoning Capability in LLM-based Web Agents via Reinforcement Learning (2026.findings-eacl)
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| Challenge: | Existing web agents relying on supervised fine-tuning struggle with generalization and robustness due to insufficient reasoning capabilities when handling the inherently dynamic nature of web interactions. |
| Approach: | They propose a large language model-empowered web agent that trains using a rule-based reinforcement learning framework to enhance single-step reasoning and planning for business-oriented web navigation tasks. |
| Outcome: | The proposed agent outperforms baseline LLM-based agents on the WorkArena benchmark by 10.26–16.59%. |
Med-SRAF: A Multi-Agent Framework for Medical Reasoning via Semantic Routing and Agentic Fusion (2026.findings-acl)
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| Challenge: | Existing RAG methods suffer from a two-part problem: semantic drift and concatenation fallacy . et al.: rapid development of Large Language Models has led to a paradigm shift in artificial intelligence . |
| Approach: | They propose a multi-agent retrieval augmentation framework guided by medical domain knowledge to address these challenges. |
| Outcome: | The proposed framework outperforms existing general RAG baselines on five widely used medical benchmarks. |
LongSpec: Long-Context Lossless Speculative Decoding with Efficient Drafting and Verification (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) can process extremely long contexts, requiring efficient inference over extended inputs. |
| Approach: | They propose a model that uses a constant-sized key-value cache to train long-context models. |
| Outcome: | Experimental results show that LongSpec achieves 3.26x speedup over strong Flash Attention baselines and 2.34x wall clock time on four math reasoning tasks. |
Calibrated Speculative Decoding: Frequency-Guided Candidate Selection for Efficient Inference (2026.acl-long)
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| Challenge: | Speculative decoding (SD) is a powerful and efficient way to accelerate autoregressive generation. |
| Approach: | They propose a training-free framework that recovers valid tokens discarded by standard verification . they use online correction memory and Semantic Consistency Gating to analyze rejections . |
| Outcome: | The proposed framework outperforms existing methods and achieves peak throughput speedup of 2.33x. |
SQL-ASTRA: Alleviating Sparse Feedback in Agentic SQL via Column-Set Matching and Trajectory Aggregation (2026.findings-acl)
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| Challenge: | Agentic SQL is a framework for multiturn agent learning, but it is limited to single-turn paradigms. |
| Approach: | They propose a framework that provides a universal two-tiered reward mechanism for credit assignment . they propose 'Aggregated Trajectory Reward' to resolve multi-turn credit assignment. |
| Outcome: | The proposed framework outperforms SOTA Arctic-Text2SQL-R1-7B on BIRD and Spider 2.0 using identical models. |
Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models (2026.acl-long)
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Chao Xue, Yao Wang, Mengqiao Liu, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Chenyao Lu, Lei Jiang, Yu Lu, Haibo Shi, Shuang Liang, Minlong Peng, Flora D. Salim
| Challenge: | Incomplete learning is widespread and heterogeneous in large language models . authors identify five recurrent sources of incomplete learning: missing prerequisite knowledge, conflicts between SFT supervision and pre-training knowledge, internal inconsistencies within SFT data, left-side forgetting during sequential fine-tuning, and insufficient optimization for rare or complex patterns. |
| Approach: | They propose a diagnostic-first framework that maps incomplete learning to causes . they identify five recurrent sources of incomplete learning: missing prerequisite knowledge, conflicts between supervision and pre-training knowledge, internal inconsistencies, left-side forgetting during sequential fine-tuning, and insufficient optimization for rare or complex patterns. |
| Outcome: | The proposed framework maps incomplete learning to causes using observable training and inference signals. |
LRQuant: Learnable and Robust Post-Training Quantization for Large Language Models (2024.acl-long)
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| Challenge: | Existing methods for post-training quantization (PTQ) are limited by the complexity of the quantization parameter and performance degradations when tested on unseen datasets. |
| Approach: | They propose a learnable smooth-based PTQ framework that allows for rapid adaptation during testing. |
| Outcome: | The proposed framework improves performance on unseen datasets and reduces memory constraints. |
HiGen: Hierarchy-Aware Sequence Generation for Hierarchical Text Classification (2024.eacl-long)
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| Challenge: | Hierarchical text classification is a complex subtask under multi-label text classification . the relevance of document sections can vary based on the hierarchy level, necessitating a dynamic document representation. |
| Approach: | They propose a text-generation-based framework that uses language models to encode dynamic text representations. |
| Outcome: | The proposed framework surpasses existing methods while handling data and mitigating class imbalance. |
An Emotional Comfort Framework for Improving User Satisfaction in E-Commerce Customer Service Chatbots (2021.naacl-industry)
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| Challenge: | E-commerce has grown rapidly over the last several years, and chatbots for intelligent customer service are simultaneously drawing attention. |
| Approach: | They propose a framework to obtain proper answer to customers’ emotional questions using emotion classification model and text matching. |
| Outcome: | The proposed framework is very promising on real online systems. |
Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking (2021.findings-acl)
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| Challenge: | Chinese Spell Checking (CSC) aims to detect and correct erroneous characters for usergenerated text in Chinese. |
| Approach: | They propose a Chinese spell checker that leverages multimodal Chinese characters' information to predict the correct output. |
| Outcome: | The proposed model outperforms strong baselines on the SIGHAN benchmarks by a large margin. |
TAG: Gradient Attack on Transformer-based Language Models (2021.findings-emnlp)
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Jieren Deng, Yijue Wang, Ji Li, Chenghong Wang, Chao Shang, Hang Liu, Sanguthevar Rajasekaran, Caiwen Ding
| Challenge: | Recent studies show that publicly shared gradients in the training process can reveal the private training data to a third-party. |
| Approach: | They propose a gradient attack algorithm to reconstruct the local training data using GLUE benchmarks. |
| Outcome: | The proposed algorithm achieves 1.5x recover rate and 2.5x ROUGE-2 over previous methods without the need of ground truth label. |
Similarizing the Influence of Words with Contrastive Learning to Defend Word-level Adversarial Text Attack (2023.findings-acl)
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| Challenge: | Neural language models are vulnerable to word-level adversarial text attacks . previous word-based search methods assume important words influence prediction . |
| Approach: | They propose a method for similarizing the influence of words with contrast learning that encourages model to learn sentence representations in which words of varying importance have a more uniform influence on prediction. |
| Outcome: | The proposed method is compatible with various training methods and improves model robustness against various adversarial attacks. |
Assessing the Ability of Self-Attention Networks to Learn Word Order (P19-1)
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| Challenge: | Existing studies have attributed SAN to being weak at learning positional information for sequence modeling due to lack of recurrence structure. |
| Approach: | They propose a word reordering detection task to quantify how well word order information is learned by SAN and RNN. |
| Outcome: | The proposed task quantifies how well word order information learned by SAN and RNN is learned. |
Balanced Joint Adversarial Training for Robust Intent Detection and Slot Filling (2020.coling-main)
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| Challenge: | Existing joint models for intent detection and slot filling show insufficient robustness . however, some small changes of inputs can fool the models to produce wrong predictions . |
| Approach: | They propose a joint adversarial training model that generates adversarials to attack the joint model and trains the model to defend against the adversarial examples. |
| Outcome: | The proposed model achieves significantly higher scores and improves robustness on two datasets. |
Universally Empowering Zeroth-Order Optimization via Adaptive Layer-wise Sampling (2026.findings-acl)
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| Challenge: | Existing methods for fine-tuning Large Language Models are slow and lack of performance. |
| Approach: | They propose a Zeroth-Order optimization framework that uses forward passes to fine-tune Large Language Models. |
| Outcome: | The proposed framework achieves 1.7 to 3.0 wall-clock acceleration on LLaMA and OPT models. |
RRAtention: Dynamic Block Sparse Attention via Per-Head Round-Robin Shifts for Long-Context Inference (2026.acl-long)
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Siran Liu, Guoxia Wang, Sa Wang, Jinle Zeng, Haoyang Xie, Siyu Lou, Jiabin Yang, Dianhai Yu, Haifeng Wang, Chao Yang
| Challenge: | Existing approaches to dynamic sparse attention require preprocessing, lack global evaluation, violate query independence, or incur high computational overhead. |
| Approach: | They propose a dynamic sparse attention method that achieves all desirable properties through a head **r**ound-**r**obin (RR) sampling strategy. |
| Outcome: | Experiments on natural language understanding and multimodal video comprehension show that the proposed method achieves 2.4 speedup at 128K context length outperforming existing methods. |
Agri-CM3: A Chinese Massive Multi-modal, Multi-level Benchmark for Agricultural Understanding and Reasoning (2025.acl-long)
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| Challenge: | Existing benchmarks lack comprehensive evaluations, particularly in multi-level reasoning, making it difficult to identify model limitations. |
| Approach: | They propose to use Agri-CM3 to assess multi-level reasoning in agricultural management by integrating multiple data modalities. |
| Outcome: | The Agri-CM3 benchmark includes 3,939 images and 15,901 multi-level multiple-choice questions with detailed explanations. |
Inference-Time Language Model Alignment via Integrated Value Guidance (2024.findings-emnlp)
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| Challenge: | Large language models are fine-tuned to align with human preferences, but tuning large models is computationally intensive and complex. |
| Approach: | They propose a method that uses implicit and explicit value functions to guide language model decoding at token and chunk-level respectively. |
| Outcome: | The proposed method outperforms traditional methods and circumvents the complexities of fine-tuning. |
Bit-by-Bit: Progressive QAT Strategy with Outlier Channel Splitting for Stable Low-Bit LLMs (2026.acl-long)
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Binxing Xu, Hao Gu, Lujun Li, Hao Wang, Bei Liu, Jiacheng Liu, Qiyuan Zhu, Xintong Yang, Chao Li, Sirui Han, Yike Guo
| Challenge: | Existing approaches to training LLMs at ultra-low precisions suffer from convergence instability and substantial training costs. |
| Approach: | They propose a progressive QAT framework with outlier channel splitting to address these issues . they use nested structure of integer quantization grids to enable a "train once, deploy any precision" paradigm . |
| Outcome: | The proposed framework outperforms baselines on both Llama2/3 and W2A16, with an 11 speedup over BF16. |
Poller: Are LLMs Suitable for Evaluating Poetry Understanding Task? (2026.findings-acl)
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| Challenge: | Traditional methods for poetry evaluation are expensive and unsuitable for large-scale data. |
| Approach: | They propose a method leveraging Large Language Models to evaluate poetry understanding tasks using Large Language models. |
| Outcome: | The proposed method reduces the evaluation error between LLMs and humans by adopting the poet's perspective. |
Attribution-Based Analysis and Optimization of Modular Agentic Workflows (2026.findings-acl)
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Yingxuan Yang, Bo Huang, Siyuan Qi, Chao Feng, Haoyi Hu, Yuxuan Zhu, Jinbo Hu, Haoran Zhao, Ziyi He, Xiao Liu, ZongYu Wang, Muning Wen, Lin Qiu, Xuezhi Cao, Xunliang Cai, Yong Yu, Weinan Zhang
| Challenge: | Large Language Models (LLMs) have driven the rise of agentic workflows . yet, how can we attribute performance gains to individual upgrades and their interactions? |
| Approach: | They propose a game-theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values. |
| Outcome: | The proposed framework provides interaction-aware attribution and recommendation for model allocation under a fixed workflow structure. |
Investigating and Scaling up Code-Switching for Multilingual Language Model Pre-Training (2025.findings-acl)
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Zhijun Wang, Jiahuan Li, Hao Zhou, Rongxiang Weng, Jingang Wang, Xin Huang, Xue Han, Junlan Feng, Chao Deng, Shujian Huang
| Challenge: | Large language models (LLMs) exhibit remarkable multilingual capabilities despite the extreme language imbalance in the pre-training data. |
| Approach: | They investigate the existence of code-switching in the pre-training corpus and categorize it into four types within two quadrants. |
| Outcome: | The proposed approach improves performance across benchmarks and representation space. |
HyperIDP: Customizing Temporal Hypergraph Neural Networks for Multi-Scale Information Diffusion Prediction (2025.coling-main)
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| Challenge: | Existing studies on information diffusion prediction have focused on both macroscopic and microscopic scales. |
| Approach: | They propose a hypergraph-based model that manages both macroscopic and microscopic diffusion predictions. |
| Outcome: | The proposed model outperforms baseline models on both macroscopic and microscopic tasks. |
Enhancing Neural Topic Model with Multi-Level Supervisions from Seed Words (2023.findings-acl)
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| Challenge: | Existing topic seed words are difficult to incorporate into topic models due to the semantic diversity of natural language. |
| Approach: | They propose a neural topic model enhanced with supervisions from seed words on word and document levels. |
| Outcome: | The proposed model outperforms the state-of-the-art seeded topic models in terms of topic quality and classification accuracy. |
DecompileBench: A Comprehensive Benchmark for Evaluating Decompilers in Real-World Scenarios (2025.findings-acl)
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| Challenge: | Existing approaches focus on syntactic correctness through synthetic micro-benchmarks or subjective human ratings, despite semantic fidelity and usability. |
| Approach: | They propose a framework that enables effective evaluation of decompilers in reverse engineering workflows . they compare six industrial-strength decompils and six recent LLM-powered approaches . |
| Outcome: | The proposed framework outperforms commercial tools in code understandability despite lower functionality correctness . it shows that it can transform human-centric reverse engineering workflows . |
Improving Consistency for Text Summarization with Energy Functions (2023.findings-emnlp)
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Qi Zeng, Qingyu Yin, Zheng Li, Yifan Gao, Sreyashi Nag, Zhengyang Wang, Bing Yin, Heng Ji, Chao Zhang
| Challenge: | Current abstractive summarization models generate inconsistent content due to the inherently noisy dataset and the discrepancy between maximum likelihood estimation based training objectives and consistency measurements. |
| Approach: | They propose a new consistency taxonomy that categorizes inconsistent content into faithfulness, factuality, and self-supportiveness. |
| Outcome: | Experiments on XSUM and CNN/DM datasets show that EnergySum mitigates the trade-off between accuracy and consistency. |
RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM-Based Table Analysis (2025.findings-acl)
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Pengzuo Wu, Yuhang Yang, Guangcheng Zhu, Chao Ye, Hong Gu, Xu Lu, Ruixuan Xiao, Bowen Bao, Yijing He, Liangyu Zha, Wentao Ye, Junbo Zhao, Haobo Wang
| Challenge: | Existing benchmarks for large language models focus on simple, flat table structures. |
| Approach: | They propose a benchmark to evaluate the performance of both Large Language Models and Multimodal LLMs across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG. |
| Outcome: | The proposed benchmark evaluates the performance of LLMs and Multimodal LLM models across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG. |
Training Language Model to Critique for Better Refinement (2025.findings-acl)
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Tianshu Yu, Chao Xiang, Mingchuan Yang, Pei Ke, Bosi Wen, Cunxiang Wang, Jiale Cheng, Li Zhang, Xinyu Mu, Chuxiong Sun, Minlie Huang
| Challenge: | Large language models (LLMs) have remarkable evaluation and critique capabilities, providing insightful feedback and identifying flaws in various tasks. |
| Approach: | They propose a framework to train critic models using refinement signals to generate feedback loops where critiques guide the model in refining its responses. |
| Outcome: | The proposed framework outperforms traditional methods and open-source models in terms of critique quality and refinement outcomes. |
Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal Uncertainty (2026.findings-acl)
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Chao Xue, Yao Wang, Mengqiao Liu, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Chenyao Lu, Lei Jiang, Yu Lu, Haibo Shi, Shuang Liang, Minlong Peng, Flora D. Salim
| Challenge: | Existing approaches to generating reward models rely on voting-based mechanisms to evaluate CoT outputs. |
| Approach: | They propose an efficient generative reward modeling framework grounded in model-internal uncertainty. |
| Outcome: | The proposed framework reduces inference cost while improving answer accuracy. |
MultiPL-MoE: Multi-Programming-Lingual Extension of Large Language Models through Hybrid Mixture-of-Experts (2025.findings-emnlp)
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| Challenge: | MultiPL is a special case of multiple natural languages and requires limited computational resources to generate multilingual code. |
| Approach: | They propose to extend LLMs by combining two paired experts to optimize expert selection at token and segment levels. |
| Outcome: | The proposed extension improves the performance of the base LLMs while retaining the most popular ones using limited computational resources. |
Speech-Text Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment (2023.acl-long)
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Tianshu Yu, Haoyu Gao, Ting-En Lin, Min Yang, Yuchuan Wu, Wentao Ma, Chao Wang, Fei Huang, Yongbin Li
| Challenge: | Existing speech-text pre-training methods are limited to one or two specific tasks, despite their success in speech-language processing tasks. |
| Approach: | They propose a temporal position prediction task to capture the speech-text alignment . they use a textual dialog pre-training task to generalize a response selection task . |
| Outcome: | The proposed model is superior in learning speech-text alignment and multi-turn dialog context. |
ImpRIF: Stronger Implicit Reasoning Leads to Better Complex Instruction Following (2026.acl-long)
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| Challenge: | Experiments show that enhancing implicit reasoning capabilities can significantly improve complex instruction following in large language models. |
| Approach: | They propose a method to enhance LLMs’ understanding of implicit reasoning instructions by formalizing such instructions as verifiable reasoning graphs and fine-tuning with graph reasoning. |
| Outcome: | The proposed method outperforms existing models on five complex instruction following benchmarks and will be open-sourced in the near future. |
More Than Catastrophic Forgetting: Integrating General Capabilities For Domain-Specific LLMs (2024.emnlp-main)
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Chengyuan Liu, Yangyang Kang, Shihang Wang, Lizhi Qing, Fubang Zhao, Chao Wu, Changlong Sun, Kun Kuang, Fei Wu
| Challenge: | a recent study shows that performance on general tasks decreases after Large Language Models are fine-tuned on domain-specific tasks. |
| Approach: | They propose a general capability integration approach to integrate general capabilities and domain knowledge within a single instance. |
| Outcome: | The proposed method improves performance on domain-specific tasks by integrating general capabilities and domain knowledge. |
Probing Semantic Alignment, Lexical Invariance, and Syntactic Influence in LLM Metaphor Processing (2026.acl-long)
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| Challenge: | Large language models (LLMs) achieve strong performance on metaphor detection and interpretation tasks, yet it remains unclear what such success actually reveals about metaphor processing. |
| Approach: | They propose to probing semantic attribute alignment, lexical invariance, and syntactic sensitivity to examine the limits of behavioral evidence for metaphor processing. |
| Outcome: | The proposed model can exhibit semantic drift relative to reference attributes, stable lexical anchors persist across contextual conditions, potentially supporting conventional metaphors while biasing novel metaphors requiring contextual integration. |
Data Diversity Matters for Robust Instruction Tuning (2024.findings-emnlp)
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Alexander Bukharin, Shiyang Li, Zhengyang Wang, Jingfeng Yang, Bing Yin, Xian Li, Chao Zhang, Tuo Zhao, Haoming Jiang
| Challenge: | Recent studies have shown that by curating high quality and diverse instruction tuning datasets, we can significantly improve instruction-following capabilities. |
| Approach: | They propose an algorithm to control diversity and quality of instruction tuning datasets and validate it. |
| Outcome: | The proposed algorithm significantly improves worst and average case performance on large scale instruction tuning datasets. |
ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora (2021.emnlp-main)
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| Challenge: | Existing methods for pretraining cross-lingual models are limited in their size due to the limited amount of parallel corpora. |
| Approach: | They propose a method that encourages the model to align multiple languages with monolingual corpora to overcome the constraint of the parallel corpus size. |
| Outcome: | The proposed method outperforms existing cross-lingual models and delivers new state-of-the-art results in various cross-linguistic downstream tasks. |
What is the Best Way for ChatGPT to Translate Poetry? (2024.acl-long)
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| Challenge: | Despite promising results, our analysis reveals persistent issues in the translations generated by ChatGPT that warrant attention. |
| Approach: | They propose an Explanation-Assisted Poetry Machine Translation method which leverages monolingual poetry explanation as a guiding information for the translation process. |
| Outcome: | The proposed method outperforms traditional translation methods of ChatGPT and the existing online systems in English-Chinese poetry translation. |
A Speaker-Aware Co-Attention Framework for Medical Dialogue Information Extraction (2022.emnlp-main)
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Yuan Xia, Zhenhui Shi, Jingbo Zhou, Jiayu Xu, Chao Lu, Yehui Yang, Lei Wang, Haifeng Huang, Xia Zhang, Junwei Liu
| Challenge: | With the development of medical digitization, the extraction and structuring of electronic medical records (EMRs) have become challenging but fundamental tasks. |
| Approach: | They propose a speaker-aware dialogue encoder with multi-task learning which takes the speaker's identity into account and a co-attention fusion network to aggregate the utterance information. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on the public medical dialogue extraction datasets to demonstrate its superiority. |
Deploying Multi-task Online Server with Large Language Model (2025.coling-industry)
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| Challenge: | In the industry, numerous natural language processing tasks are deployed online . traditional approaches tackle each task separately by its own network and pipeline . |
| Approach: | They propose a three-stage multi-task learning framework for large language models . it involves task filtering, fine-tuning on high-resource tasks, and finally fine- tuning on all tasks . |
| Outcome: | The proposed framework reduces up to 90% of overhead while reducing latency and resource usage. |
Assessing Logical Puzzle Solving in Large Language Models: Insights from a Minesweeper Case Study (2024.naacl-long)
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| Challenge: | Large Language Models (LLMs) have shown remarkable proficiency in language understanding and have been successfully applied to a variety of real-world tasks through task-specific fine-tuning or prompt engineering. |
| Approach: | They propose a task that challenges LLMs to identify the locations of mines based on numerical clues provided by adjacent cells. |
| Outcome: | The proposed task requires an understanding of each cell’s state, discerning spatial relationships between clues and mines, and strategizing actions based on logical deductions drawn from the arrangement of the cells. |
Can ChatGPT Really Understand Modern Chinese Poetry? (2026.findings-eacl)
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| Challenge: | Recent studies have focused on poetry generation and translation, but their scope has been limited to evaluation and analysis of experimental results without addressing fundamental issues of comprehension. |
| Approach: | They propose a framework for evaluating ChatGPT's understanding of modern poetry . they evaluated the interpretations of unpublished modern Chinese poems by different poets . |
| Outcome: | The proposed framework is based on the evaluation of unpublished poems by poets and shows that its interpretations align with the original poets’ intents in over 73% of the cases. |
ARL2: Aligning Retrievers with Black-box Large Language Models via Self-guided Adaptive Relevance Labeling (2024.acl-long)
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| Challenge: | Existing retrievers are misaligned with large language models due to separate training processes and inherent black-box nature of LLMs. |
| Approach: | They propose a retriever learning technique that harnesses LLMs as labelers to annotate and score adaptive relevance evidence. |
| Outcome: | Extensive experiments show that ARL2 improves accuracy and reduces the cost of API calls. |
Virtual Compiler Is All You Need For Assembly Code Search (2024.acl-long)
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| Challenge: | Using a large dataset, we find that assembly code search is a significant task for reverse engineers. |
| Approach: | They propose to train a Large Language Model (LLM) to emulate a general compiler. |
| Outcome: | The proposed model surpasses the baseline by 26%. |
GuoFeng: A Benchmark for Zero Pronoun Recovery and Translation (2022.emnlp-main)
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Mingzhou Xu, Longyue Wang, Derek F. Wong, Hongye Liu, Linfeng Song, Lidia S. Chao, Shuming Shi, Zhaopeng Tu
| Challenge: | ZPs are often omitted when they can be pragmatically or grammatically inferred from intraand inter-sentential contexts. |
| Approach: | They propose a benchmark testset for target evaluation on Chinese-English ZP translation. |
| Outcome: | The proposed testset covers five genres and identifies current challenges for evaluation. |
Everyone is unique: Towards Behaviorally Heterogeneous Negotiation Dialogue Systems for Debt Collection (2026.acl-long)
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| Challenge: | Existing models that assume users to be static, rational agents with fixed preferences fail to capture rich behavioral heterogeneity in real-world debt collection scenarios. |
| Approach: | They propose a public persona-enriched debt collection benchmark that highlights behavioral heterogeneity in negotiation. |
| Outcome: | The proposed benchmark outperforms existing models in realistic scenarios using 16 state-of-the-art LLMs. |
On the Copying Behaviors of Pre-Training for Neural Machine Translation (2021.findings-acl)
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| Challenge: | Existing studies show that initializing NMT models with pre-trained language models (LM) can speed up the model training and boost the model performance. |
| Approach: | They propose a method to control copying behaviors in NMT models by initializing them with pre-trained language models (LM) they propose to use a metric called copy ratio to control the copying behavior in decoding. |
| Outcome: | The proposed method improves translation performance by controlling copying behaviors for pre-training based models. |
Guided by the Plan: Enhancing Faithful Autoregressive Text-to-Audio Generation with Guided Decoding (2026.eacl-long)
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| Challenge: | Autoregressive (AR) models excel at generating temporally coherent audio by producing tokens sequentially, yet they often falter in faithfully following complex textual prompts. |
| Approach: | They propose a lightweight auxiliary model trained with a GAE-inspired objective to predict final instruction-following quality from partial generations. |
| Outcome: | The proposed model achieves 10 points improvement in CLAP score over baseline AR models while maintaining computational parity with best-of-N decoding. |
Revisiting Pre-trained Language Models and their Evaluation for Arabic Natural Language Processing (2022.emnlp-main)
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Abbas Ghaddar, Yimeng Wu, Sunyam Bagga, Ahmad Rashid, Khalil Bibi, Mehdi Rezagholizadeh, Chao Xing, Yasheng Wang, Xinyu Duan, Zhefeng Wang, Baoxing Huai, Xin Jiang, Qun Liu, Phillippe Langlais
| Challenge: | Existing pre-trained language models are not well-explored and are not reproducible in the literature. |
| Approach: | They propose to improve existing Arabic language pre-trained language models using a more methodical approach. |
| Outcome: | The proposed models outperform existing models on ALUE, a leaderboard-powered benchmark for Arabic NLU and NLG tasks. |
Geoparsing: Diagram Parsing for Plane and Solid Geometry with a Unified Formal Language (2026.findings-acl)
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Peijie Wang, Ming-Liang Zhang, Jun Cao, Chao Deng, Dekang Ran, Pi Bu, Hongda Sun, Xuan Zhang, Yingyao Wang, Jun Song, Bo Zheng, Fei Yin, Cheng-Lin Liu
| Challenge: | Recent advances in Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities across various vision reasoning tasks. |
| Approach: | They propose a unified formal language that integrates plane and solid geometry, comprehensively covering geometric structures and semantic relations. |
| Outcome: | The proposed language achieves state-of-the-art parsing performance and significantly boosts MLLMs’ capabilities for downstream geometry reasoning tasks. |
M3AV: A Multimodal, Multigenre, and Multipurpose Audio-Visual Academic Lecture Dataset (2024.acl-long)
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Zhe Chen, Heyang Liu, Wenyi Yu, Guangzhi Sun, Hongcheng Liu, Ji Wu, Chao Zhang, Yu Wang, Yanfeng Wang
| Challenge: | Publishing open-source academic video recordings is an emerging approach to sharing knowledge online. |
| Approach: | They propose a multimodal, multigenre, and multipurpose audio-visual academic lecture dataset with human annotations for multimodal content recognition and understanding tasks. |
| Outcome: | The proposed dataset can be used for multiple audio-visual recognition and understanding tasks. |
On the Complementarity between Pre-Training and Back-Translation for Neural Machine Translation (2021.findings-emnlp)
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| Challenge: | Experimental results show that PT and BT are nicely complementary to each other. |
| Approach: | They introduce two probing tasks for PT and BT respectively and investigate their complementarity. |
| Outcome: | The proposed methods establish state-of-the-art on the WMT16 English-Romanian and English-Russian benchmarks. |
TransLLM: A Unified Multi-Task Large Language Model for Urban Transportation via Learnable Prompting (2026.acl-long)
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| Challenge: | Existing models lack generalization capabilities and lack structured spatiotemporal data. |
| Approach: | They propose a unified multi-task framework that synergizes spatiotemporal encoding with LLM reasoning through learnable prompt composition. |
| Outcome: | The proposed framework outperforms baseline models on seven datasets and three tasks on supervised and zero-shot settings with excellent generalization and robustness. |
PSC: Extending Context Window of Large Language Models via Phase Shift Calibration (2024.emnlp-main)
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| Challenge: | Large-scale language models (LLMs) have shown impressive results across a variety of tasks. |
| Approach: | They propose a module for calibrating the frequencies predefined by existing methods . they conducted extensive experiments across multiple models and tasks . |
| Outcome: | The proposed method reduces perplexity as the context window size is varied from 16k to 32k and up to 64k. |
Self-Generated Critiques Boost Reward Modeling for Language Models (2025.naacl-long)
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Yue Yu, Zhengxing Chen, Aston Zhang, Liang Tan, Chenguang Zhu, Richard Yuanzhe Pang, Yundi Qian, Xuewei Wang, Suchin Gururangan, Chao Zhang, Melanie Kambadur, Dhruv Mahajan, Rui Hou
| Challenge: | Existing reward models produce scalar scores and struggle to incorporate critiques in a natural language format. |
| Approach: | They propose a framework that predicts critiques and rewards using self-generated critiques without extra supervision. |
| Outcome: | The proposed framework improves reward modeling accuracy by 3.7%-7.3% compared to standard reward models and LLM judges. |
GenDis: Generative-Discriminative Dual-View Co-Training for Generalized Category Discovery (2026.acl-long)
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| Challenge: | Existing methods rely on one-hot discriminative supervision, leading to overfitting on seen classes and poor generalization to unseen ones. |
| Approach: | They propose a Generative–Discriminative Dual-View Co-Training framework that unifies discriminative classification and semantic label generation within an LLM. |
| Outcome: | The proposed framework outperforms existing methods on five benchmarks on the generalized category discovery (GCD) task. |
EDSD: Entropy-Driven Design for Faster Speculative Decoding (2026.acl-long)
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| Challenge: | Existing methods for speculative decoding incur substantial training overhead to mitigate information misalignment between autoregressive draft model training and decoding. |
| Approach: | They propose an Entropy-Driven Speculative Decoding framework that uses entropy as a unified, interpretable signal for both draft model training and architectural design. |
| Outcome: | Experiments on seven large language models show that EDSD improves training efficiency by 24.8% and increases acceptance length by 4.0% compared to state-of-the-art methods. |
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. |
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%. |
Causal Document-Grounded Dialogue Pre-training (2023.emnlp-main)
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Yingxiu Zhao, Bowen Yu, Bowen Li, Haiyang Yu, Jinyang Li, Chao Wang, Fei Huang, Yongbin Li, Nevin Zhang
| Challenge: | Existing methods for document-grounded dialogue (DocGD) rely on general pre-trained language models without a tailored pre-training approach that explicitly captures causal relationships. |
| Approach: | They propose a causally-complete dataset construction strategy for developing million-scale DocGD pre-training corpora and a perturbation-based strategy to capture causality. |
| Outcome: | The proposed strategy yields significant and consistent improvements in fully-supervised, low-resource, few-shot, and zero-shot settings. |
Aligning to Constraints for Data-Efficient Language Model Customization (2025.findings-naacl)
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Fei Wang, Chao Shang, Shuai Wang, Sarthak Jain, Qiang Ning, Bonan Min, Vittorio Castelli, Yassine Benajiba, Dan Roth
| Challenge: | General-purpose language models (LMs) are aligned to diverse user intents, but fall short when it comes to specific applications. |
| Approach: | They propose a framework that uses constraints to automatically produce supervision signals for user alignment with constraints. |
| Outcome: | The proposed framework can produce supervision signals for user alignment with constraints. |
PLaD: Preference-based Large Language Model Distillation with Pseudo-Preference Pairs (2024.findings-acl)
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Rongzhi Zhang, Jiaming Shen, Tianqi Liu, Haorui Wang, Zhen Qin, Feng Han, Jialu Liu, Simon Baumgartner, Michael Bendersky, Chao Zhang
| Challenge: | Knowledge distillation (KD) is a technique for transferring expertise from large teacher models to compact student models with reduced memory footprints and inference costs. |
| Approach: | They propose to transfer knowledge from large teacher models to compact student models by exploiting teacher-student capacity discrepancies to generate pseudo-preference pairs where teacher outputs are preferred over student outputs. |
| Outcome: | The proposed framework exploits teacher-student capacity discrepancy to generate pseudo-preference pairs where teacher outputs are preferred over student outputs. |
Retrieve-Plan-Generation: An Iterative Planning and Answering Framework for Knowledge-Intensive LLM Generation (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) often produce factual errors due to limited internal knowledge. |
| Approach: | They propose a retrieval-augmented generation framework that generates plan tokens to guide subsequent generation. |
| Outcome: | The proposed framework improves the accuracy of large language models with external knowledge sources. |
Language Model Based Text-to-Audio Generation: Anti-Causally Aligned Collaborative Residual Transformers (2025.emnlp-main)
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| Challenge: | Autoregressive language models excel in text-to-audio generation, but lag behind diffusion models by a non-trivial margin. |
| Approach: | They propose a framework that integrates multiple isolated transformers with causal conditioning and anti-causal alignment via reinforcement learning. |
| Outcome: | The proposed framework outperforms existing LM-based and diffusion-based systems in audio synthesis. |
MDC-Bench: A Multidisciplinary Causal Benchmark Based on Causal Structures for Evaluating Large Language Models (2026.findings-acl)
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Peng Wang, Yuxiong Yan, Xiao Ding, Kai Xiong, Bibo Cai, Chao Peng, Yutai Hou, Dandan Tu, Bing Qin, Ting Liu
| Challenge: | Existing causal datasets focus on the commonsense domain, but LLMs perform poorly when answering complex questions. |
| Approach: | They propose a multidisciplinary causal evaluation benchmark to assess LLMs' knowledge and skills. |
| Outcome: | The proposed model improves in domain specialization, structural diversity, and task complexity. |
Boosting Data Utilization for Multilingual Dense Retrieval (2025.emnlp-main)
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| Challenge: | Existing studies focus on fine-tuning multilingual dense retrieval models, but data scarcity for low-resource languages makes it difficult to align representations in a shared vector space. |
| Approach: | They propose to obtain high-quality hard negative samples and effective mini-batch data to boost data utilization for multilingual dense retrieval by obtaining high-quality negative samples. |
| Outcome: | The proposed method outperforms existing baselines on a multilingual retrieval benchmark, MIRACL, with 16 languages. |
Adapting LLM Agents with Universal Communication Feedback (2025.findings-naacl)
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| Challenge: | Recent advances in large language models (LLMs) have demonstrated potential for LLM agents. |
| Approach: | They propose a universal buffer and iterative pipeline to store feedback and itersative pipelines to enable LLM agents to explore and update their policy in an environment. |
| Outcome: | The proposed approach outperforms supervised instruction fine-tuning baselines on four datasets. |
To Code or not to Code? Adaptive Tool Integration for Math Language Models via Expectation-Maximization (2025.findings-acl)
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| Challenge: | Existing tools that integrate chain-of-thought reasoning and code execution lack metacognitive awareness to integrate tools. |
| Approach: | They propose a framework that synergizes structured exploration with off-policy RL optimization to create a cycle between metacognitive tool-use decisions and evolving capabilities. |
| Outcome: | The proposed framework improves over 11% on MATH500 and 9.4% on AIME without o1-like CoT. |
Planning Beyond Text: Graph-based Reasoning for Complex Narrative Generation (2026.findings-acl)
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| Challenge: | Existing methods for long-form complex narrative generation struggle to maintain global narrative coherence and logical consistency. |
| Approach: | They propose a framework that performs narrative planning on structural graph representations instead of direct sequential text representations. |
| Outcome: | The proposed model outperforms representative baselines across diverse scenarios. |
METNet: A Mutual Enhanced Transformation Network for Aspect-based Sentiment Analysis (2020.coling-main)
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| Challenge: | Existing methods for learning complex sentences with multiple aspects are ill-equipped to learn complex sentences . |
| Approach: | They propose a mutual enhanced transformation network for the ABSA task . it improves representation learning of the aspect with contextual semantic features . |
| Outcome: | The proposed model improves representation learning of the aspect with contextual semantic features, giving the aspect more abundant information. |
MiniRAG: A Lightweight RAG system with Small Language Models (2026.acl-long)
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| Challenge: | Existing RAG frameworks rely on Large Language Models (LLMs) for all stages of the process, resulting in high computational costs and resource demands. |
| Approach: | They propose a semantic-aware heterogeneous graph indexing mechanism that combines text chunks and named entities in a unified structure and a lightweight topology-enhanced retrieval approach that leverages graph structures for efficient knowledge discovery without requiring advanced language capabilities. |
| Outcome: | The proposed system achieves comparable performance to LLM-based methods while requiring only 25% of the storage space. |
Correcting Chinese Spelling Errors with Phonetic Pre-training (2021.findings-acl)
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Ruiqing Zhang, Chao Pang, Chuanqiang Zhang, Shuohuan Wang, Zhongjun He, Yu Sun, Hua Wu, Haifeng Wang
| Challenge: | Existing methods for Chinese spelling correction only use pre-trained language model or incorporate phonological information as external knowledge. |
| Approach: | They propose a phonetic Chinese spelling correction model that integrates phonetic features into language model by leveraging pre-training and fine-tuning methods. |
| Outcome: | The proposed model outperforms existing methods on SIGHAN datasets and improves on other datasets. |
Can Reasoning Path still be Effective as Input? Bridging Post-Reasoning to Chain-of-Thought Compression (2026.acl-long)
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Chengzhengxu Li, Xiaoming Liu, Zhaohan Zhang, Shengchao Liu, Guoxin Ma, Yu Lan, Cong Wang, Chao Shen
| Challenge: | Existing work on reducing CoT generation in reasoning impairs the necessary information for deriving the correct answer. |
| Approach: | They propose a reasoning paradigm that takes CoT as a part of context to simplify the reasoning task for Large Language Models (LLMs). |
| Outcome: | The proposed framework reduces the generation length of LLMs, but its effectiveness hinges on the efficiency and reliability of the contextual CoT generation. |
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. |
Harnessing Pre-Trained Neural Networks with Rules for Formality Style Transfer (D19-1)
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| Challenge: | Existing studies normalize informal sentences with rules, but they introduce noise if we use them in a naive way. |
| Approach: | They propose to harness rules into a state-of-the-art neural network that is typically pretrained on massive corpora. |
| Outcome: | The proposed method can be used to generate a state-of-the-art on a small dataset. |
Exploring Compositional Image Retrieval with Hybrid Compositional Learning and Heuristic Negative Mining (2022.findings-emnlp)
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| Challenge: | Existing CIR models are pre-trained on uni-modal data, resulting in unimodal data. |
| Approach: | They propose a CIR model HyCoLe-HNM with CLIP as the backbone . they use a gated fusion mechanism from a question answering model to perform compositional learning . |
| Outcome: | The proposed model achieves state-of-the-art performance on three CIR datasets . it borrows a gated fusion mechanism from a question answering model to perform compositional fusion . |
On the Representation Geometry of LoRA Model Merging (2026.findings-acl)
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| Challenge: | Existing methods for low-rank Adaptation (LoRA) fine-tuning focus on globally shared structure . combining SVD with CUR improves performance of LoRA model merging . |
| Approach: | They propose a training-free method that combines SVD and CUR decomposition to improve LoRA merging performance. |
| Outcome: | The proposed procedure improves on vision and language benchmarks. |
DRIFT: Transferring Reasoning Priors for Efficient MLLM Fine-Tuning (2026.findings-acl)
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Chao Huang, Zeliang Zhang, Jiang Liu, Ximeng Sun, Jialian Wu, Xiaodong Yu, Ze Wang, Chenliang Xu, Emad Barsoum, Zicheng Liu
| Challenge: | Multimodal large language models (MLLMs) have made rapid progress in perception and alignment, but their reasoning ability often lags behind strong text-only LLMs. |
| Approach: | They propose a method that transfers reasoning knowledge in the gradient space while preserving multimodal alignment. |
| Outcome: | Experiments on multimodal reasoning benchmarks show that DRIFT outperforms naive merging and standard SFT. |
LightReasoner: Can Small Language Models Teach Large Language Models Reasoning? (2026.acl-long)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable progress in reasoning, but are resource-intensive and require large curated datasets. |
| Approach: | They propose a framework that leverages the behavioral divergence between a stronger expert model and a weaker amateur model. |
| Outcome: | The proposed framework improves accuracy by up to 28.1% while reducing time consumption by 90% and tuning token usage by 99%. |
Formality Style Transfer with Shared Latent Space (2020.coling-main)
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| Challenge: | Existing approaches for formality style transfer use neural networks for sentence generation, but the dataset for formal style transfer is considerably smaller than translation corpora. |
| Approach: | They propose a new approach for formality style transfer using shared latent space and two auxiliary losses. |
| Outcome: | The proposed approach outperforms baselines in various settings, especially when limited data is available. |
PARSE: An Efficient Search Method for Black-box Adversarial Text Attacks (2022.coling-1)
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| Challenge: | Neural networks are vulnerable to adversarial examples, i.e., under a black-box scenario. |
| Approach: | They propose a word-level search algorithm that searches for subareas under dynamic search space following the subarea importance. |
| Outcome: | The proposed algorithm can achieve comparable success rates to complex search methods while saving numerous queries and time. |
PairRE: Knowledge Graph Embeddings via Paired Relation Vectors (2021.acl-long)
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| Challenge: | Existing knowledge graph embedding methods fail to solve two major problems at the same time, leading to unsatisfactory results. |
| Approach: | They propose a model with paired vectors for each relation representation that can be adaptively adjusted to fit for different complex relations. |
| Outcome: | Experiments on two knowledge graph datasets show the proposed model can handle complex relations and encode relation patterns. |
PEDNet: A Persona Enhanced Dual Alternating Learning Network for Conversational Response Generation (2020.coling-main)
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| Challenge: | Existing persona-based dialogue models generate personalized responses using predefined persona information, but they lack personality. |
| Approach: | They propose a persona-based dual Alternating Learning Network that generates personalized responses using predefined persona information. |
| Outcome: | The proposed method produces more personalized responses than baseline methods. |
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. |
Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuning (2024.findings-acl)
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Zhiyang Xu, Chao Feng, Rulin Shao, Trevor Ashby, Ying Shen, Di Jin, Yu Cheng, Qifan Wang, Lifu Huang
| Challenge: | Recent vision-language models (VLMs) have shown impressive capabilities as general visual assistants, but there are two challenges to their performance: (1) lacking task diversity in pretraining and visual instruction tuning; (2) annotation error and bias in GPT-4 synthesized instruction tuning data. |
| Approach: | They propose a two-stage instruction tuning framework that fine tunes VLMs firstly and further tuned on GPT-4 synthesized data. |
| Outcome: | The proposed framework outperforms the traditional single-stage visual instruction tuning framework and achieves state-of-the-art performance across a wide range of multi-modal evaluation benchmarks. |
Does DetectGPT Fully Utilize Perturbation? Bridging Selective Perturbation to Fine-tuned Contrastive Learning Detector would be Better (2024.acl-long)
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Shengchao Liu, Xiaoming Liu, Yichen Wang, Zehua Cheng, Chengzhengxu Li, Zhaohan Zhang, Yu Lan, Chao Shen
| Challenge: | Existing methods to detect MGT from human-written texts are inadequate . existing methods are fine-tuned and zero-shot metric-based, but they can be more accurate. |
| Approach: | They propose a novel fine-tuned detector that can detect MGT from human-written texts by contrastive learning on selective perturbation. |
| Outcome: | The proposed method outperforms the state-of-the-art by 1.20% on four public datasets. |
An Iterative Associative Memory Model for Empathetic Response Generation (2024.acl-long)
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| Challenge: | Existing methods for empathetic response generation ignore the associated words between dialogue utterances. |
| Approach: | They propose an iterative associative memory model to capture associated words between dialogue utterances and situations, dialogue history, and a memory module for storing associated words. |
| Outcome: | The proposed model captures key words between dialogue utterances and situations, dialogue history, and a memory module, thereby accurately and nuancedly comprehending the utterables. |
Dynamic Collaboration of Multi-Language Models based on Minimal Complete Semantic Units (2025.emnlp-main)
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| Challenge: | Existing methods to enhance reasoning capabilities of language models are expensive and often lack the ability to perform complex reasoning tasks. |
| Approach: | They propose a token-level multi-model collaboration strategy to enhance reasoning capabilities in language models by selecting the optimal tokens from the next token distributions. |
| Outcome: | The proposed method is superior to existing methods and will be released soon. |
Multimodal and Multi-view Models for Emotion Recognition (P19-1)
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| Challenge: | combining lexical and acoustic information results in more robust and accurate models . combining both modalities may be a bottleneck in a deployment pipeline due to computational complexity or privacy constraints . |
| Approach: | They propose to combine acoustic and lexical information to provide a deployable acustic model . they use multimodal models and two attention mechanisms to assess the benefits of lexicals . |
| Outcome: | The proposed model outperforms the state-of-the-art on the USC-IEMOCAP dataset . it significantly surpasses models that have been exclusively trained with acoustic features . |
D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat (2022.emnlp-main)
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| Challenge: | Existing human-machine dialogue systems are not able to provide diagnostic information for depression diagnosis due to stigma associated with mental illness. |
| Approach: | They propose to construct a Chinese Dialogue Dataset for depression-diagnosis-oriented chat based on clinical depression diagnostic criteria. |
| Outcome: | The proposed system can be used to diagnose depression using a Chinese Dialogue Dataset. |
LongDocURL: a Comprehensive Multimodal Long Document Benchmark Integrating Understanding, Reasoning, and Locating (2025.acl-long)
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Chao Deng, Jiale Yuan, Pi Bu, Peijie Wang, Zhong-Zhi Li, Jian Xu, Xiao-Hui Li, Yuan Gao, Jun Song, Bo Zheng, Cheng-Lin Liu
| Challenge: | Existing document understanding benchmarks only handle a small number of pages . existing models are limited to handling only a limited number of documents . |
| Approach: | They propose a long document understanding benchmark that integrates three primary tasks and 20 sub-tasks based on different primary tasks. |
| Outcome: | The proposed model outperforms existing benchmarks on open-source and closed-source models . the model outpersforms other models on more than 33,000 pages of documents . |
Improving Time Sensitivity for Question Answering over Temporal Knowledge Graphs (2022.acl-long)
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| Challenge: | Temporal knowledge graphs record entity relations and when they occur in time . previous work fails to address time-related challenges such as time-order issues . paper proposes time-sensitive question answering framework to address these problems . |
| Approach: | They propose a time-sensitive question answering framework that uses temporal KGs to answer natural language questions. |
| Outcome: | The proposed framework outperforms the state-of-the-art on a new benchmark for question answering over temporal knowledge graphs. |
ERNIE-Code: Beyond English-Centric Cross-lingual Pretraining for Programming Languages (2023.findings-acl)
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| Challenge: | ERNIE-Code is a unified pre-trained language model for 116 NLs and 6 PLs. |
| Approach: | They propose a unified pre-trained language model for 116 NLs and 6 PLs . they employ span-corruption language modeling that learns patterns from monolingual NL or PL . |
| Outcome: | The proposed model outperforms previous multilingual models for NL or NL across end tasks. |
MessToClean: Evidence-Grounded Structure-Preserving Reconstruction for Real-World Degraded Exam Paper Images (2026.acl-long)
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Jiayi Tuo, Cheng Tang, Zihan Wang, Chenyue Zhou, Yao Li, Yanbiao Ma, Chao Wang, Wei Dai, Mingxuan Wang, Shitong Qin, Ziwei Zhao
| Challenge: | Existing Multimodal Large Language Models (MLLMs) fail under RDEI, leading to disrupted structure and evidence-unsupported hallucinations. |
| Approach: | They propose a backbone-agnostic, evidence-driven pipeline that treats off-the-shelf MLLMs as interchangeable components to improve stem consistency and figure consistency. |
| Outcome: | The proposed pipeline improves stem consistency by 1.01-3.18%, figure consistency by 0.50-49.16%, and refusal F1 by 1.06-10.88% across question types. |
Bridging Internal Consistency and External Alignment: A Causal and Dynamic Interpretability Framework for LLM Generation (2026.acl-long)
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| Challenge: | Existing interpretability methods focus on internal and external aspects of the model . existing explanations often focus on surface correlations or static dependencies . |
| Approach: | They propose a causal and dynamic interpretability framework for Large Language Models . they characterize backdoor-adjusted causal effects of generated prefix and prompt . |
| Outcome: | The proposed framework provides a unified causal view of internal consistency and external alignment in LLM generation dynamics. |
Convolutional Self-Attention Networks (N19-1)
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| Challenge: | Existing models of self-attention networks lack the ability to capture dependencies regardless of distance and can be enhanced with multi-head attention. |
| Approach: | They propose a convolutional self-attention network which can be enhanced by multi-head attention by allowing the model to attend to information from different representation subspaces. |
| Outcome: | The proposed model outperforms existing models on improving locality of SANs on different language pairs and model settings. |
Neural Machine Translation with Decoding History Enhanced Attention (C18-1)
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| Challenge: | Neural machine translation with source-side attention has been criticized for its poor memory performance. |
| Approach: | They propose to use a Decoding History Enhanced Attention mechanism to render NMT models better at selecting both source-side and target-side information. |
| Outcome: | The proposed model improves by 0:9 BLEU on Chinese-English translation and the state-of-the-art on a larger task. |
AXIS: Efficient Human-Agent-Computer Interaction with API-First LLM-Based Agents (2025.acl-long)
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Junting Lu, Zhiyang Zhang, Fangkai Yang, Jue Zhang, Lu Wang, Chao Du, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
| Challenge: | Multimodal large language models (MLLMs) have enabled LLM-based agents to directly interact with application user interfaces (UIs), enhancing agents’ performance in complex tasks. |
| Approach: | They propose a novel agent framework that prioritizes actions through application programming interfaces over UI actions and facilitates the creation and expansion of APIs through automated exploration of applications. |
| Outcome: | The proposed framework reduces task completion time by 65%-70% and cognitive workload by 38%-53% while maintaining accuracy of 97%-98% compared to humans. |
Effective Demonstration Annotation for In-Context Learning via Language Model-Based Determinantal Point Process (2024.emnlp-main)
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| Challenge: | Existing studies on large-scale labeled support sets are not feasible in practical scenarios. |
| Approach: | They introduce a language model-based determinant point process that considers uncertainty and diversity of unlabeled instances for optimal selection. |
| Outcome: | The proposed method can effectively select canonical examples on 9 NLU and 2 Generation datasets. |
TEPrompt: Task Enlightenment Prompt Learning for Implicit Discourse Relation Recognition (2023.findings-acl)
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| Challenge: | Existing prompt learning models for IDRR use multiple-prompt decisions from three different yet much similar connective prediction templates. |
| Approach: | They propose to fuse three related tasks to fuse the learned features of auxiliary tasks to create a prompt learning model that can be used to boost the main task. |
| Outcome: | The proposed model outperforms the ConnPrompt in the training phase and in the testing phase. |
What Would Happen Next? Predicting Consequences from An Event Causality Graph (2024.findings-emnlp)
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| Challenge: | Existing script event prediction task forcasts the subsequent event based on an event script chain, but the evolution of historical events is more complicated in real world scenarios. |
| Approach: | They propose a Causality Graph Event Prediction task that forecasts consequential event based on an Event Causity Graph (ECG). |
| Outcome: | The proposed model outperforms the advanced competitors for the CGEP task. |
Learning Deep Transformer Models for Machine Translation (P19-1)
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| Challenge: | Neural machine translation models have advanced the previous state-of-the-art by learning mappings between sequences via neural networks and attention mechanisms. |
| Approach: | They propose to use layer normalization to pass the combination of previous layers to the next layer to improve the model. |
| Outcome: | The proposed model outperforms the shallow Transformer-Big/Base baseline model on English-German and Chinese-English tasks by 0.4-2.4 BLEU points. |
QualiSpeech: A Speech Quality Assessment Dataset with Natural Language Reasoning and Descriptions (2025.acl-long)
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Siyin Wang, Wenyi Yu, Xianzhao Chen, Xiaohai Tian, Jun Zhang, Lu Lu, Yu Tsao, Junichi Yamagishi, Yuxuan Wang, Chao Zhang
| Challenge: | Existing datasets lack comprehensive annotations for speech quality assessment . existing methods lack detailed annotations, resulting in inaccurate evaluations. |
| Approach: | They propose a low-level speech quality assessment dataset incorporating natural language descriptions and a Benchmark to evaluate low- level speech understanding capabilities of auditory large language models. |
| Outcome: | The proposed model can be used to evaluate the low-level speech understanding capabilities of auditory large language models. |
LongTableBench: Benchmarking Long-Context Table Reasoning across Real-World Formats and Domains (2025.findings-emnlp)
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Liyao Li, Jiaming Tian, Hao Chen, Wentao Ye, Chao Ye, Haobo Wang, Ningtao Wang, Xing Fu, Gang Chen, Junbo Zhao
| Challenge: | Evaluating 52 LLMs reveals that only the strongest models maintain robust performance under increasing context lengths and format diversity. |
| Approach: | They propose a benchmark for evaluating long-context reasoning over semi-structured tables across diverse formats, tasks, and domains. |
| Outcome: | The proposed model outperforms compression-based approaches on tasks requiring semantic integration. |
MoEC: A Memory-Routed Mixture-of-Experts Controller for Adaptive Minecraft Control (2026.acl-long)
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| Challenge: | Existing systems rely on a monolithic policy to execute subgoals across varying contexts, causing inconsistent outcomes and scaling only partially mitigates. |
| Approach: | They propose a memory-routed mixtureof-experts controller for Adaptive Minecraft Control that routes via a subgoal-indexed expert memory and regulates capacity through failure-triggered expert growth and redundancy-aware consolidation. |
| Outcome: | The proposed controller shows significant gains in adaptability, robustness, and execution consistency over strong baselines. |
At Your Own PACE: A Causal Framework for Evaluating EQ in LLMs (2026.findings-acl)
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| Challenge: | Emotional Quotient (EQ) has emerged as a competency for seamless human-AI integration. |
| Approach: | They propose a framework for a closed-loop EQ evaluation using a PACE taxonomy to define four dimensions of LLM EQ. |
| Outcome: | The proposed framework achieves high alignment of 89.31% with human preferences while maintaining robust consistency of 83.6%. |
Rethinking Word-level Adversarial Attack: The Trade-off between Efficiency, Effectiveness, and Imperceptibility (2024.lrec-main)
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| Challenge: | Neural language models have demonstrated impressive performance but remain vulnerable to word-level adversarial attacks. |
| Approach: | They propose two standardized search spaces to address the problem of word-level adversarial attacks. |
| Outcome: | The proposed search spaces improve performance and trade-offs in different scenarios. |
LLM×MapReduce: Simplified Long-Sequence Processing using Large Language Models (2025.acl-long)
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Zihan Zhou, Chong Li, Xinyi Chen, Shuo Wang, Yu Chao, Zhili Li, Haoyu Wang, Qi Shi, Zhixing Tan, Xu Han, Xiaodong Shi, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing studies have focused on extending the context length of large language models (LLMs) due to their quadratic computational complexity and a lack of high-quality long training examples, most LLMs are trained with a limited window size. |
| Approach: | They propose a training-free framework that enables large language models to effectively process long texts using a divide-and-conquer strategy for comprehensive document understanding. |
| Outcome: | The proposed framework outperforms open-source and commercial long-context LLMs and is compatible with several models. |
The Invisible Hand: Unveiling Provider Bias in Large Language Models for Code Generation (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have emerged as the new recommendation engines, surpassing traditional methods in both capability and scope, particularly in code generation. |
| Approach: | They propose to use a dataset to investigate a new type of bias in Large Language Models for code generation, provider bias, to determine whether the model favors specific providers. |
| Outcome: | The proposed model favors services from Google and Amazon, but without explicit directives, and can modify input code to incorporate their preferred providers without user requests. |
StablePT : Towards Stable Prompting for Few-shot Learning via Input Separation (2024.findings-emnlp)
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| Challenge: | Existing studies on prompt tuning have shown that language models can be effective few-shot learners with prompting. |
| Approach: | They propose to treat the hard prompt and soft prompt as separate inputs to mitigate noise brought by prompt initialization. |
| Outcome: | Experimental results show that the proposed method outperforms state-of-the-art methods by 6.97% in accuracy and reduces the standard deviation by 1.92 on average. |
The Lower The Simpler: Simplifying Hierarchical Recurrent Models (N19-1)
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| Challenge: | Using a simplified version of GRU, we replace the GRUs at the middle layers of hierarchical recurrent models with Fixed-size Ordinally-Forgetting Encoding (FOFE). |
| Approach: | They propose to make the lower layers simpler than the upper ones to simplify two typical hierarchical recurrent models, namely Hierarchical Recurrent Encoder-Decoder (HRED) and R-NET, whose basic building block is GRU. |
| Outcome: | The proposed models contain less trainable parameters, consume less training time, and achieve slightly better performance than baseline models. |
Aria-UI: Visual Grounding for GUI Instructions (2025.findings-acl)
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| Challenge: | Using a multimodal model, GUI agents can ground from language instructions to target elements . relying on HTML or AXTree inputs is a challenge for GUI agents . |
| Approach: | They propose a large multimodal model specifically designed for GUI grounding that adopts a pure vision approach instead of auxiliary inputs. |
| Outcome: | The proposed model outperforms vision-only and AXTree-reliant models on offline and online agents. |
The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking (2022.findings-acl)
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Yinghui Li, Qingyu Zhou, Yangning Li, Zhongli Li, Ruiyang Liu, Rongyi Sun, Zizhen Wang, Chao Li, Yunbo Cao, Hai-Tao Zheng
| Challenge: | Chinese Spell Checking (CSC) aims to detect and correct spelling errors, which are caused by the phonological or visual similarity. |
| Approach: | They propose an Error-driven COntrastive Probability Optimization framework to refine the knowledge representations of pre-trained language models to avoid predicting common characters. |
| Outcome: | Extensive experiments and detailed analyses on SIGHAN datasets demonstrate that ECOPO is simple yet effective. |