Papers by Liu Tao
CE-RM: A Pointwise Generative Reward Model Optimized via Two-Stage Rollout and Unified Criteria (2026.findings-acl)
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| Challenge: | Existing studies have shown that rule-based evaluation methods are ineffective for open-ended natural language generation. |
| Approach: | They propose a pointwise generative reward model with a dedicated two-stage rollout method and unified query-based criteria that can be trained with 5.7K high-quality data. |
| Outcome: | The proposed model achieves superior performance on diverse reward model benchmarks, especially in Best-of-N scenarios, and delivers more effective improvements in downstream RL practice. |
TeleMelody: Lyric-to-Melody Generation with a Template-Based Two-Stage Method (2022.emnlp-main)
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Zeqian Ju, Peiling Lu, Xu Tan, Rui Wang, Chen Zhang, Songruoyao Wu, Kejun Zhang, Xiang-Yang Li, Tao Qin, Tie-Yan Liu
| Challenge: | a new lyric-to-melody generation system bridges the gap between lyrics and melodies . previous generation systems lack paired data and lack of control on generated melodie. |
| Approach: | They develop a lyric-to-melody generation system with music template to bridge the gap between lyrics and melodies. |
| Outcome: | The proposed system bridges the gap between lyrics and melodies by using music template. |
CODERL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment (2026.acl-long)
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Xue Jiang, Yihong Dong, Mengyang Liu, Deng Hongyi, Tian Wang, Yongding Tao, Zhi Jin, Wenpin Jiao, Ge Li
| Challenge: | Large Language Models excel at code generation by learning from vast code corpora, but a fundamental semantic gap remains between training on textual patterns and the goal of functional correctness . reinforcement learning with verifiable rewards (RLVR) approaches are inefficient for establishing a well-aligned connection between the textual representation of code and its execution semantics. |
| Approach: | They propose a novel approach that integrates execution semantics alignment into the RLVR training pipeline for code generation. |
| Outcome: | The proposed model outperforms baseline training and RLVR and shows strong applicability across RL and LLMs. |
Sentence Matching with Syntax- and Semantics-Aware BERT (2020.coling-main)
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| Challenge: | Sentence matching aims to determine the special relationship between two sentences. |
| Approach: | They propose to integrate syntactic and semantic information into BERT with sentence matching by using an implicit integration method that is less sensitive to the output structure information. |
| Outcome: | The proposed method achieves state-of-the-art or competitive performance on several sentence matching datasets. |
GLoCIM: Global-view Long Chain Interest Modeling for news recommendation (2025.coling-main)
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| Challenge: | Recent efforts to extract local subgraph information from click graphs have hindered collaboratively utilizing global click graph information. |
| Approach: | They propose a global-view long chain interests model that models a click graph with neighbor interest to enhance news recommendation. |
| Outcome: | The proposed method surpasses baseline methods on two real-world datasets. |
Augmenting Multi-Turn Text-to-SQL Datasets with Self-Play (2022.findings-emnlp)
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| Challenge: | Numerous architectures and pretraining methods have been proposed for context-dependent text-to-SQL, but the size of the datasets used has been limited due to the high cost of annotating multi-turn dialogue and SQL pairs. |
| Approach: | They propose to augment training datasets using self-play which leverages contextual information to synthesize new interactions to adapt the model to new databases. |
| Outcome: | The proposed model improves accuracy on SParC and CoSQL, two widely used cross-domain text-to-SQl datasets. |
Visual Pivoting Unsupervised Multimodal Machine Translation in Low-Resource Distant Language Pairs (2024.findings-emnlp)
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| Challenge: | Existing studies show that neural MT achieves much worse translation quality than statistical MT with a small number of corpora. |
| Approach: | They propose a visual pivoting method for alignment between distant language pairs . they first construct a dataset and then apply it to pre-training and fine-tuning . |
| Outcome: | The proposed method outperforms baselines on DLPs and close language pairs. |
Zero-shot Sharpness-Aware Quantization for Pre-trained Language Models (2023.emnlp-main)
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| Challenge: | Existing zero-shot quantization methods are based on overfitting problem in adversarial learning process, leading to sub-optimal performance. |
| Approach: | They propose a zero-shot sharpness-aware quantization framework for the quantization of various PLMs by optimizing a minimax problem. |
| Outcome: | The proposed framework can achieve significant performance gains on discriminative and generative PLMs. |
Lil: Less is Less When Applying Post-Training Sparse-Attention Algorithms in Long-Decode Stage (2026.findings-acl)
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Junhao Hu, Fangze Li, Mingtao Xu, Feifan Meng, Shiju Zhao, Tiancheng Hu, Ting Peng, Anmin Liu, Wenrui Huang, Chenxu Liu, Ziyue Hua, Tao Xie
| Challenge: | Prior work typically decomposes inference into prefill and decode stages, with the decode stage dominating total latency. |
| Approach: | They propose an algorithm that detects threshold where information loss exceeds information gain during sparse decoding to reduce token consumption by up to 90% and a marginal accuracy degradation of less than 2%. |
| Outcome: | The proposed algorithm reduces token consumption by 90% with a marginal accuracy degradation of less than 2% across reasoning-intensive benchmarks. |
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)
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Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin
| Challenge: | a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities . |
| Approach: | They present a comparative analysis to identify and distinguish LLM activities from human activities. |
| Outcome: | The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities. |
Revisiting Knowledge Distillation for Autoregressive Language Models (2024.acl-long)
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| Challenge: | Autoregressive language models (LMs) are expensive and memory intensive, preventing the development of industrial applications. |
| Approach: | They propose an adaptive teaching approach to improve the KD of autoregressive language models by distilling knowledge into a small student model. |
| Outcome: | The proposed method can achieve consistent and significant performance gains across all model types and sizes. |
FOLIO: Natural Language Reasoning with First-Order Logic (2024.emnlp-main)
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Simeng Han, Hailey Schoelkopf, Yilun Zhao, Zhenting Qi, Martin Riddell, Wenfei Zhou, James Coady, David Peng, Yujie Qiao, Luke Benson, Lucy Sun, Alexander Wardle-Solano, Hannah Szabó, Ekaterina Zubova, Matthew Burtell, Jonathan Fan, Yixin Liu, Brian Wong, Malcolm Sailor, Ansong Ni, Linyong Nan, Jungo Kasai, Tao Yu, Rui Zhang, Alexander Fabbri, Wojciech Kryscinski, Semih Yavuz, Ye Liu, Xi Lin, Shafiq Joty, Yingbo Zhou, Caiming Xiong, Rex Ying, Arman Cohan, Dragomir Radev
| Challenge: | Existing benchmarks for logical reasoning in large language models lack language naturalness or limited complexity. |
| Approach: | They propose to use first-order logic annotations to evaluate logical reasoning capabilities of large language models. |
| Outcome: | The proposed dataset evaluates the FOL reasoning ability of supervised fine-tuning on medium-sized language models. |
Llama SLayer 8B: Shallow Layers Hold the Key to Knowledge Injection (2024.findings-emnlp)
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| Challenge: | Existing methods to augment pre-trained large language models require extensive computational efforts and massive data volumes, challenging the widespread accessibility of LLM research. |
| Approach: | They propose a post-pretraining strategy of selectively enhancing shallow layers while pruning less effective deep ones to augment pretrained large language models. |
| Outcome: | The proposed approach improves performance on the corpus of code & math and a legal corpus and is widely applicable. |
AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments (2026.acl-long)
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Zhiheng Xi, Dingwen Yang, Jiaqi Liu, Jixuan Huang, Honglin Guo, Baodai Huang, Tinggang Chen, Qi Zhang, Zhonghang Lu, Chenyu Liu, Jiajun Sun, Jiazheng Zhang, Dingwei Zhu, Xin Guo, Junzhe Wang, Zhihao Zhang, Yuming Yang, Junjie Ye, Minghe Gao, Dongrui Liu, Jiaming Ji, Guohao Li, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing benchmarks evaluate agents in simplified, idealized settings, relying on pre-packaged tool interfaces, overlooking critical steps, and assume inputs are clean and fully specified. |
| Approach: | They propose a framework that evaluates language agents in simplified, idealized settings . they show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2 . |
| Outcome: | Experiments on 15 proprietary and open-source models show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2 . |
LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style Plugin (2024.acl-long)
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Shihan Dou, Enyu Zhou, Yan Liu, Songyang Gao, Wei Shen, Limao Xiong, Yuhao Zhou, Xiao Wang, Zhiheng Xi, Xiaoran Fan, Shiliang Pu, Jiang Zhu, Rui Zheng, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Experimental results show that, as the instruction data increases, LoRAMoE can significantly improve the ability to process downstream tasks, while maintaining the world knowledge stored in the LLM. |
| Approach: | They propose a framework that introduces several low-rank adapters and integrates them by using a router network to freeze the backbone model and force a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks. |
| Outcome: | The proposed framework freezes the backbone model and forces a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks, to alleviate world knowledge forgetting. |
MemoPhishAgent: Memory-Augmented Multi-Modal LLM Agent for Phishing URL Detection (2026.acl-industry)
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| Challenge: | Traditional phishing website detection relies on static heuristics or reference lists, which lag behind rapidly evolving attacks. |
| Approach: | They propose a memory-augmented multi-modal LLM agent that leverages episodic memories to guide decisions on recurring and novel threats. |
| Outcome: | The proposed agent outperforms state-of-the-art phishing detection tools on two public datasets and improves recall by 20%. |
SimulSpeech: End-to-End Simultaneous Speech to Text Translation (2020.acl-main)
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| Challenge: | SimulSpeech is an end-to-end simultaneous speech to text translation system . conventional approaches to simultaneous speech translation divide the translation process into two stages . |
| Approach: | They develop an end-to-end simultaneous speech to text translation system which translates speech in source language to text in target language concurrently. |
| Outcome: | The proposed system achieves reasonable BLEU scores and lower delay compared to full-sentence translation model. |
Self-Powered LLM Modality Expansion for Large Speech-Text Models (2024.emnlp-main)
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| Challenge: | Large language models exhibit remarkable performance across diverse tasks . however, these methods require significant resource demands and tend to overfit specific tasks. |
| Approach: | They propose a self-powered LSM that leverages augmented automatic speech recognition data generated by the model itself for more effective instruction tuning. |
| Outcome: | The proposed model mitigates speech anchor bias and improves the fusion of speech and text modalities in large language models. |
Progressive Multi-Granularity Training for Non-Autoregressive Translation (2021.findings-acl)
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| Challenge: | Non-autoregressive translation models are weak at learning high-mode knowledge, argues a new study . despite the improved learning difficulty, there are still complicated word orders and structures in the synthetic sentences, making the NAT performance sub-optimal. |
| Approach: | They propose to train non-autoregressive translation models to learn fine-grained lower-mode knowledge . they break down sentence-level examples into three types and increase granularities . |
| Outcome: | The proposed method improves phrase translation accuracy and model reordering ability against strong NAT baselines. |
How Do Answer Tokens Read Reasoning Traces? Self-Reading Patterns in Thinking LLMs for Quantitative Reasoning (2026.findings-acl)
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| Challenge: | Prior work on activation steering has focused on shaping reasoning traces, but it remains unclear how answer tokens actually read and integrate the reasoning to produce reliable outcomes. |
| Approach: | They propose a training-free steering method that uses self-reading quality scores to guide inference toward benign self-readiness and away from uncertain and disorganized reading. |
| Outcome: | The proposed method yields consistent accuracy gains in the reasoning traces generated by thinking LLMs. |
TexSmart: A System for Enhanced Natural Language Understanding (2021.acl-demo)
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Lemao Liu, Haisong Zhang, Haiyun Jiang, Yangming Li, Enbo Zhao, Kun Xu, Linfeng Song, Suncong Zheng, Botong Zhou, Dick Zhu, Xiao Feng, Tao Chen, Tao Yang, Dong Yu, Feng Zhang, ZhanHui Kang, Shuming Shi
| Challenge: | TexSmart supports fine-grained named entity recognition (NER) Large-scale fine-granular entity types are expected to provide richer semantic information for downstream NLP applications. |
| Approach: | They introduce TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities. |
| Outcome: | The proposed system supports fine-grained named entity recognition (NER) and enhanced semantic analysis functions. |
LoopCoder: Scaling Code Intelligence via Looped Language Models (2026.findings-acl)
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Jian Yang, Wei Zhang, Shuyue Guo, Yizhi LI, Linzheng Chai, Zhengmao Ye, Shukai Liu, Yuyang Song, Jiajun Wu, Che Liu, Tianyu Zheng, Siwei Wu, Leo L, Xudong Ma, Chuan Hao, Ran Tao, Yan Xing, Jianzhou Wang, Mingjie Tang, Aishan Liu, Zhoujun Li, Xianglong Liu, Weifeng Lv, Bryan Dai
| Challenge: | Large language models have mastered syntax-level code generation, but complex algorithmic reasoning remains a challenge. |
| Approach: | They propose a recurrent inductive bias that aligns with the recursive nature of programming logic. |
| Outcome: | The proposed model achieves comparable performance to standard dense models with more parameters. |
RIFT: Repurposing Negative Samples via Reward-Informed Fine-Tuning (2026.findings-acl)
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| Challenge: | Reward Informed Fine-Tuning (RIFT) is an effective and robust alternative to expensive expert data for LLM alignment. |
| Approach: | They propose a reward-informed fine-tuning framework that utilizes all self-generated samples to learn from both positive and negative trajectories. |
| Outcome: | The proposed framework outperforms both RFT and Supervised Fine-Tuning (SFT) on mathematical benchmarks. |
LiteVL: Efficient Video-Language Learning with Enhanced Spatial-Temporal Modeling (2022.emnlp-main)
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| Challenge: | Recent large-scale video-language pre-trained models have shown appealing performance on downstream tasks. |
| Approach: | They propose a video-text model that adapts a pre-trained image-language model into a text-based model without heavy pre-training. |
| Outcome: | The proposed model outperforms existing models on video-text retrieval and video question answering tasks without heavy pre-training. |
Dynamic Parallel Tree Search for Efficient LLM Reasoning (2025.acl-long)
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Yifu Ding, Wentao Jiang, Shunyu Liu, Yongcheng Jing, Jinyang Guo, Yingjie Wang, Jing Zhang, Zengmao Wang, Ziwei Liu, Bo Du, Xianglong Liu, Dacheng Tao
| Challenge: | Recent methods focus on search accuracy while overlooking computational efficiency. |
| Approach: | They propose a parallelism framework that dynamically optimizes reasoning path in inference. |
| Outcome: | The proposed framework improves efficiency by 2-4 on average while maintaining or even surpassing existing reasoning algorithms in accuracy. |
PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction (2021.acl-long)
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| Challenge: | Chinese spelling correction (CSC) is a task to detect and correct spelling errors in texts. |
| Approach: | They propose a Pre-trained masked Language model with Misspelled knowledgE (PLOME) which jointly learns how to understand language and correct spelling errors. |
| Outcome: | The proposed model outperforms state-of-the-art methods on widely used benchmarks and achieves superior performance against existing models. |
Rethinking Task-Specific Knowledge Distillation: Contextualized Corpus as Better Textbook (2022.emnlp-main)
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| Challenge: | Existing methods for knowledge distillation use a two-stage paradigm: general distillation with a task-agnostic general corpus and task-specific distillation using augmented task- specific corpus. |
| Approach: | They propose a contextualized corpus that contextualizes task corpus with large-scale general corpus through relevance-based text retrieval to improve student learning. |
| Outcome: | The proposed model improves on the GLUE benchmark and shows that it is better than generalized corpus and augmented task-specific corpus. |
Revisiting Over-Smoothness in Text to Speech (2022.acl-long)
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| Challenge: | Non-autoregressive text to speech models ignore correlation in time and frequency domains, causing blurry results. |
| Approach: | They revisit the problem of over-smoothness in non-autoregressive text to speech models . they use methods that reduce complexity of data distributions and improve modeling methods . |
| Outcome: | The proposed models achieve better voice quality and faster inference speed than autoregressive models. |
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. |
Data Interpreter: An LLM Agent for Data Science (2025.findings-acl)
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Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Robert Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Yongxin Ni, Zhibin Gou, Zongze Xu, Yuyu Luo, Chenglin Wu
| Challenge: | Large Language Models (LLMs) excel in various domains but face challenges when applied to data science workflows due to their complex, multi-stage nature. |
| Approach: | They propose a hierarchical graph-based agent that represents complexity and a progressive strategy for step-by-step verification, refinement, and consistent context management. |
| Outcome: | The proposed agent surpasses state-of-the-art baselines on the MATH dataset and performs better on InfiAgent-DABench. |
Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models (2026.findings-acl)
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Hengyuan Zhang, Zhihao Zhang, Ercong Nie, Mingyang Wang, Zunhai Su, Yiwei Wang, Qianli Wang, Shuzhou Yuan, Xufeng Duan, Qibo Xue, Zeping Yu, Chenming Shang, Xiao Liang, Jing Xiong, Hui Shen, Chaofan Tao, Zhengwu Liu, Senjie Jin, Zhiheng Xi, Dongdong Zhang, Sophia Ananiadou, Tao Gui, Ruobing Xie, Hayden Kwok-Hay So, Hinrich Schuetze, Xuanjing Huang, Qi Zhang, Ngai Wong
| Challenge: | Existing literature on mechanistic interpretation (MI) treats it as an observational science, leaving practical applications underexplored. |
| Approach: | They propose a survey structured around the pipeline to identify and improve MI models. |
| Outcome: | The proposed framework enables tangible improvements in Alignment, Capability, and Efficiency. |
EpiCoDe: Boosting Model Performance Beyond Training with Extrapolation and Contrastive Decoding (2025.findings-acl)
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| Challenge: | Existing methods to enhance performance of Large language models are limited due to the cost of training data and privacy concerns. |
| Approach: | They propose a method that enhances a finetuned model with its inferior version and adopts contrastive decoding to reduce predicted errors. |
| Outcome: | The proposed method outperforms existing methods in data-scarcity scenarios across three domains and shows that it is more robust and robust. |
DB-LLM: Accurate Dual-Binarization for Efficient LLMs (2024.findings-acl)
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Hong Chen, Chengtao Lv, Liang Ding, Haotong Qin, Xiabin Zhou, Yifu Ding, Xuebo Liu, Min Zhang, Jinyang Guo, Xianglong Liu, Dacheng Tao
| Challenge: | Existing methods for ultra-low bit quantization cause severe accuracy drops . a novel Dual-Binarization method is proposed for efficient Large Language Models . |
| Approach: | They propose a Dual-Binarization method that takes 2-bit-width and binarization into account . they propose DB-LLM, which uses a 2-bit binarized weighted model to represent weights efficiently . |
| Outcome: | The proposed method surpasses the current State-of-the-Art in ultra-low bit quantization and achieves 20% reduction in computational consumption compared to the SOTA method under the same bit-width. |
DARM: Distribution-Aware Reward Modeling by Alleviating Biases from Low Preference-Context Dependency Data (2026.acl-long)
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Shaofan Liu, Guoqiang Zhang, Shihan Dou, Huiyuan Zheng, Yiming Zhou, Junjie Ye, Shaowen Wang, Shichun Liu, Jiazheng Zhang, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing methods for training reward models are vulnerable to context neglect and degraded accuracy. |
| Approach: | They propose distribution-aware reward modeling that augments the RM objective with a conditional mutual information regularizer that maximizes context and the predicted reward conditioned on the response. |
| Outcome: | The proposed model improves performance in RLHF and improves accuracy in other settings. |
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. |
AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling (2024.acl-long)
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Jun Zhan, Junqi Dai, Jiasheng Ye, Yunhua Zhou, Dong Zhang, Zhigeng Liu, Xin Zhang, Ruibin Yuan, Ge Zhang, Linyang Li, Hang Yan, Jie Fu, Tao Gui, Tianxiang Sun, Yu-Gang Jiang, Xipeng Qiu
| Challenge: | Existing language models that use discrete representations for unified processing of various modalities are limited to text generation and do not include multimodal output. |
| Approach: | They propose a multimodal language model that utilizes discrete representations for unified processing of various modalities. |
| Outcome: | The proposed model can be trained stably without any alterations to existing models or training paradigms. |
Improving Chinese Grammatical Error Detection via Data augmentation by Conditional Error Generation (2022.findings-acl)
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| Challenge: | Chinese Grammatical Error Detection is a non-automatic method to detect grammatical errors in texts. |
| Approach: | They propose a Conditional Non-Autoregressive Error Generation model for Chinese grammatical errors that uses a masking and prediction method to generate a context-dependent error. |
| Outcome: | The proposed method achieves better performance than all compared data augmentation methods on the CGED-2018 and CGAD-2020 benchmarks. |
Two-Stage Regularization-Based Structured Pruning for LLMs (2026.acl-long)
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Mingkuan Feng, Jinyang Wu, Siyuan Liu, Shuai Zhang, Hongjian Fang, Ruihan Jin, Feihu Che, Pengpeng Shao, Zhengqi Wen, Jianhua Tao
| Challenge: | Structural pruning is a promising solution for large language models . prior structured pruning methods remove unimportant parameters based on certain metrics . |
| Approach: | They propose a structural pruning method that iteratively learns the weights of transformer layers by adding their l1-norm to the loss function. |
| Outcome: | The proposed pruning method outperforms strong layer-wise pruning methods without requiring retraining. |
TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer Capabilities (2024.emnlp-main)
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Ming Zhang, Caishuang Huang, Yilong Wu, Shichun Liu, Huiyuan Zheng, Yurui Dong, Yujiong Shen, Shihan Dou, Jun Zhao, Junjie Ye, Qi Zhang, Tao Gui, Xuanjing Huang
| Challenge: | Current datasets cater to user-led systems and are limited to predefined specific scenarios and slots. |
| Approach: | They propose to use a Chinese dialogue dataset to train a model that authentically simulates human-computer dialogues in 30 popular life service scenarios. |
| Outcome: | The proposed model achieves a joint accuracy of 75.09% in out-of-domain evaluations . it also achieves notable abilities in slot filling and questioning . |
Active Domain Knowledge Acquisition with 100-Dollar Budget: Enhancing LLMs via Cost-Efficient, Expert-Involved Interaction in Sensitive Domains (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have demonstrated an impressive level of general knowledge, but often struggle in highly specialized domains due to the lack of expert knowledge. |
| Approach: | They propose a framework to actively engage domain experts within a fixed budget to enhance domain-specific LLMs. |
| Outcome: | The proposed framework improves LLMs in highly specialized domains while adhering to budget constraints. |
Structured Pruning for Efficient Generative Pre-trained Language Models (2023.findings-acl)
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| Challenge: | Large-scale generative Pre-trained Language Models (PLMs) are limited in their deployment in real-world applications. |
| Approach: | They propose to prune the feed-forward networks of generative pre-trained language models to smaller widths without designing extra operators. |
| Outcome: | The proposed method achieves 1.51x/6.96x inference speedup on GPU/CPU with 67% size reduction. |
SINCon: Mitigate LLM-Generated Malicious Message Injection Attack for Rumor Detection (2025.acl-long)
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| Challenge: | Existing methods define important nodes as important and target them for attacks if the model treats nodes’ predictive influence more uniformly . Existing approaches target high predictive influence nodes but are vulnerable to malicious message injection attacks. |
| Approach: | They propose a defense mechanism that encourages the model to learn graph representations where nodes with varying importance have a more uniform influence on predictions. |
| Outcome: | Extensive experiments on the Twitter and Weibo datasets show that similarizing the predictive Influence of nodes with Contrastive Learning significantly enhances resistance against LLM-driven message injection attacks. |
ADAM: Dense Retrieval Distillation with Adaptive Dark Examples (2024.findings-acl)
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| Challenge: | Existing methods to retrieve data from multiple encoders are too trivial for the teacher to distinguish, preventing the teacher from transferring abundant dark knowledge to the student. |
| Approach: | They propose a knowledge distillation framework that can better transfer the dark knowledge held in the teacher with adaptive dark examples. |
| Outcome: | The proposed framework can better transfer the dark knowledge held in the teacher with adaptive dark examples. |
BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical Modeling Problem Solving (2025.acl-long)
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Teng Wang, Wing Yin Yu, Zhenqi He, Zehua Liu, HaileiGong HaileiGong, Han Wu, Xiongwei Han, Wei Shi, Ruifeng She, Fangzhou Zhu, Tao Zhong
| Challenge: | Existing datasets in operations research domain lack detailed annotations of the modeling process, focusing only on objective values. |
| Approach: | They propose an annotation-based tree-of-thought tree-based reasoning algorithm that integrates reinforcement learning into a tree- of-though. |
| Outcome: | The proposed algorithm outperforms state-of-the-art methods on StructuredOR, NL4OPT, and MAMO-ComplexLP datasets. |
AgentV-RL: Scaling Reward Modeling with Agentic Verifier (2026.findings-acl)
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Jiazheng Zhang, Ziche Fu, Zhiheng Xi, Wenqing Jing, Mingxu Chai, Wei He, Guoqiang Zhang, Chenghao Fan, Chenxin An, Wenxiang Chen, Zhicheng Liu, Haojie Pan, Dingwei Zhu, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing approaches to improve LLM reasoning are limited in complex domains and lack external grounding makes verifiers unreliable on computation-intensive tasks. |
| Approach: | They propose a framework that transforms reward modeling into a multi-turn, tool-augmented deliberative process. |
| Outcome: | The proposed framework surpasses state-of-the-art ORMs by 25.2% under parallel and sequential TTS. |
DeepGuard: Secure Code Generation via Multi-Layer Semantic Aggregation (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) can replicate insecure patterns from training data. |
| Approach: | They propose a framework that leverages distributed security-relevant cues by aggregating representations from multiple upper layers via an attention-based module. |
| Outcome: | Experiments show that the framework improves the secure-and-correct generation rate by 11.9% over baselines. |
Efficient Sequence Learning with Group Recurrent Networks (N18-1)
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| Challenge: | Recurrent neural networks have achieved state-of-the-art results in many artificial intelligence tasks, such as language modeling, neural machine translation and speech recognition. |
| Approach: | They propose an efficient architecture to improve the efficiency of such RNN model training by adopting the group strategy for recurrent layers while exploiting the representation rearrangement strategy between layers as well as time steps. |
| Outcome: | The proposed architecture achieves comparable or better accuracy compared with baselines, with a much smaller number of parameters and at a lower computational cost. |
Graph Reasoning Paradigm: Structured and Symbolic Reasoning with Topology-Aware Reinforcement Learning for Large Language Models (2026.acl-long)
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Runxuan Liu, Xianhao Ou, Xinyan Ma, Jiyuan Wang, Jiafeng Liang, Jiaqi Li, Tao He, Zheng Chu, Rongchuan Mu, Zekun Wang, Baoxin Wang, Dayong Wu, Ming Liu, Shijin Wang, Guoping Hu, Bing Qin
| Challenge: | Existing methods for long chain-of-thought (LCoT) are coarse-grained, reward hacking, and poor generalization. |
| Approach: | They propose a Long Chain-of-Thought (LCoT) model that integrates reinforcement learning with verifiable rewards with a process-aware verification approach. |
| Outcome: | The proposed model improves reasoning and code generation tasks while reducing the cost of training and performance bottlenecks. |
Revisiting Token Dropping Strategy in Efficient BERT Pretraining (2023.acl-long)
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| Challenge: | Token dropping is a recently-proposed strategy to speed up the pretraining of masked language models, such as BERT. |
| Approach: | They propose a semantic-consistent learning method to improve token dropping by skipping the computation of a subset of input tokens at several middle layers. |
| Outcome: | The proposed method achieves consistent and significant performance gains across all tasks and model sizes. |
JointCoder: Exploring Automated ICD Coding on Real-World Chinese EHRs with a Multi-Agent Framework (2026.acl-demo)
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| Challenge: | Existing automated ICD coding systems face several fundamental challenges due to the limited availability of publicly available Chinese ICD datasets. |
| Approach: | They propose to use a Chinese ICD coding dataset and a multi-agent framework to reformulate ICD as a joint disease-procedure coding task. |
| Outcome: | The proposed system outperforms state-of-the-art methods on real-world Chinese ICD coding datasets and 1.7B-parameter models. |
Flooding-X: Improving BERT’s Resistance to Adversarial Attacks via Loss-Restricted Fine-Tuning (2022.acl-long)
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Qin Liu, Rui Zheng, Bao Rong, Jingyi Liu, ZhiHua Liu, Zhanzhan Cheng, Liang Qiao, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing approaches to generating adversarial perturbations scale up the cost of training computational complexity by the number of gradient steps it takes to obtain the adversarials. |
| Approach: | They propose a flood method which aims at better generalization and a criterion to bring hyper-parameter-dependent flooding into effect with a narrowed-down search space by measuring how the gradient steps taken within one epoch affect the loss of each batch. |
| Outcome: | The proposed method improves BERT’s resistance to textual adversarial attacks by a large margin and achieves state-of-the-art robust accuracy on various text classification and GLUE tasks. |
“I’ve Decided to Leak”: Probing Internals Behind Prompt Leakage Intents (2025.emnlp-main)
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Jianshuo Dong, Yutong Zhang, Liu Yan, Zhenyu Zhong, Tao Wei, Ke Xu, Minlie Huang, Chao Zhang, Han Qiu
| Challenge: | Large language models (LLMs) exhibit prompt leakage vulnerabilities, raising intellectual property and confidentiality concerns. |
| Approach: | They use probing techniques to capture LLMs’ intent-related internal representations and show that they internalize prompt leakage intents in their hidden states before generating tokens. |
| Outcome: | The proposed probes achieve 90%+ AUROC across all tested models, even when applied to new system prompts and attacks. |
Reasoning-Augmented Conversation for Multi-Turn Jailbreak Attacks on Large Language Models (2025.findings-emnlp)
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Zonghao Ying, Deyue Zhang, Zonglei Jing, Yisong Xiao, Quanchen Zou, Aishan Liu, Siyuan Liang, Xiangzheng Zhang, Xianglong Liu, Dacheng Tao
| Challenge: | Existing methods for implementing multi-turn jailbreaks struggle to balance semantic coherence with attack effectiveness, resulting in benign semantic drift or ineffective detection evasion. |
| Approach: | They propose a framework that reformulates harmful queries into benign reasoning tasks and leverages LLMs’ strong reasoning capabilities to compromise safety alignment. |
| Outcome: | The proposed framework achieves state-of-the-art attack effectiveness in complex conversational scenarios, with average ASRs increasing by up to 96%. |
Unsupervised Pivot Translation for Distant Languages (P19-1)
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| Challenge: | Unsupervised neural machine translation (NMT) is a popular method for transferring information between languages. |
| Approach: | They propose an unsupervised pivot translation method which translates a language to a distant language through multiple hops. |
| Outcome: | The proposed method improves translation on 20 languages and 294 distant languages on 20 different languages and language pairs. |
Compression of Generative Pre-trained Language Models via Quantization (2022.acl-long)
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| Challenge: | Existing methods to compress generative pre-trained language models fail on generative tasks due to homogeneous word embeddings and limited memory. |
| Approach: | They propose a token-level contrastive distillation method to learn distinguishable word embeddings and a module-wise dynamic scaling method to make quantizers adaptive to different modules. |
| Outcome: | The proposed method outperforms the state-of-the-art compression methods on generative PLMs by a clear margin. |
Listen, Watch, and Learn to Feel: Retrieval-Augmented Emotion Reasoning for Compound Emotion Generation (2025.findings-acl)
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| Challenge: | Existing methods to assess human emotion are limited by the subjective nature of emotion perception, limiting the robustness of existing models. |
| Approach: | They propose a plug-and-play module that enhances MLLMs’ ability to tackle compound and context-rich emotion tasks. |
| Outcome: | The proposed framework improves MLLMs' ability to tackle compound and context-rich emotion tasks and the Compound Emotion QA dataset shows it performs well across both benchmarks and evaluation frameworks. |
DeReA: Improving Idiom Translation with Detect-Retrieve-Arbitrate Reasoning (2026.acl-long)
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| Challenge: | Existing approaches to idiom translation are limited by the constraints of static parametric memory and retrieval noise . idiomatic expressions are non-compositional units where figurative meanings diverge from literal interpretations . |
| Approach: | They propose a detect-retrieve-arbitrate framework that detects idiomatic spans by reasoning over semantic conflicts between literal and contextual meanings. |
| Outcome: | The proposed framework improves GPT-5-mini and Emerging Slang datasets on various model scales. |
OctoBench: Benchmarking Scaffold-Aware Instruction Following in Repository-Grounded Agentic Coding (2026.acl-long)
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Deming Ding, Shichun Liu, Enhui Yang, Jiahang Lin, Ziying Chen, Shihan Dou, Honglin Guo, Weiyu Cheng, Pengyu Zhao, Chengjun Xiao, Qunhong Zeng, Qi Zhang, Xuanjing Huang, Qidi Xu, Tao Gui
| Challenge: | coding scaffolds that follow heterogeneous instructions remain under-examined in software engineering . coding models are capable software agents, but their ability to follow constraints remains under-explored . |
| Approach: | They introduce OctoBench, which benchmarks scaffold-aware instruction following in agentic coding. |
| Outcome: | The proposed benchmark aims to accelerate the development of more scaffold-aware agents. |
Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement (2023.findings-emnlp)
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Zhiheng Xi, Senjie Jin, Yuhao Zhou, Rui Zheng, Songyang Gao, Jia Liu, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing prompting methods have been used to enhance multistep reasoning capabilities of large language models, but they have overlooked the potential of formulating higher-quality problems. |
| Approach: | They propose a method that starts from the problem side and refines problems to be more comprehensible and solvable for models. |
| Outcome: | The proposed method achieves notable and consistent effectiveness on five reasoning benchmarks across different models. |
Depth Growing for Neural Machine Translation (P19-1)
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| Challenge: | Neural machine translation models with tens and even more than a hundred blocks have shown effectiveness in image recognition. |
| Approach: | They propose a two-stage approach with three specially designed components to construct deeper NMT models. |
| Outcome: | The proposed approach improves on WMT14 EnglishGerman and EnglishFrench translation tasks. |
DART: Open-Domain Structured Data Record to Text Generation (2021.naacl-main)
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Linyong Nan, Dragomir Radev, Rui Zhang, Amrit Rau, Abhinand Sivaprasad, Chiachun Hsieh, Xiangru Tang, Aadit Vyas, Neha Verma, Pranav Krishna, Yangxiaokang Liu, Nadia Irwanto, Jessica Pan, Faiaz Rahman, Ahmad Zaidi, Mutethia Mutuma, Yasin Tarabar, Ankit Gupta, Tao Yu, Yi Chern Tan, Xi Victoria Lin, Caiming Xiong, Richard Socher, Nazneen Fatema Rajani
| Challenge: | Data-to-text annotations can be costly when dealing with tables with nontrivial structures. |
| Approach: | They propose a procedure for extracting semantic triples from tables that encodes their structures by exploiting table headers and table title. |
| Outcome: | The proposed method exploits the semantic dependencies between table headers and title to extract semantic triples from tables. |
Multi-Programming Language Sandbox for LLMs (2025.acl-demo)
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Shihan Dou, Jiazheng Zhang, Jianxiang Zang, Yunbo Tao, Weikang Zhou, Haoxiang Jia, Shichun Liu, Yuming Yang, Shenxi Wu, Zhiheng Xi, Muling Wu, Rui Zheng, Changze Lv, Limao Xiong, Shaoqing Zhang, Lin Zhang, Wenyu Zhan, Rongxiang Weng, Jingang Wang, Xunliang Cai, Yueming Wu, Ming Wen, Yixin Cao, Tao Gui, Xipeng Qiu, Qi Zhang, Xuanjing Huang
| Challenge: | MPLSandbox is an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Language Models (LLMs). |
| Approach: | They propose a multi-programming language sandbox that provides unified feedback from compilers and analysis tools for Large Language Models. |
| Outcome: | The proposed multi-language sandbox can provide comprehensive feedback from compilers and analysis tools for large language models (LLMs). |
LASA: Language-Agnostic Semantic Alignment at the Semantic Bottleneck for LLM Safety (2026.acl-long)
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Junxiao Yang, Haoran Liu, Jinzhe Tu, Jiale Cheng, Zhexin Zhang, Shiyao Cui, Jiaqi Weng, Jialing Tao, Hui Xue, Hongning Wang, Han Qiu, Minlie Huang
| Challenge: | Large language models (LLMs) have demonstrated better safety performance in high-resource languages than in low-resourced languages. |
| Approach: | They propose language-agnostic semantic alignment (LASA) which anchors safety alignment directly in semantic bottlenecks. |
| Outcome: | The proposed approach significantly improves safety across all languages: average attack success rate drops from 24.7% to 2.8% on LLaMA-3.1-8B-Instruct and remains within 3–4% across Qwen2.5 and Qwend3 Instruct models (7B–32B). |
Reasoning-Guided Exploration for Online DPO (2026.findings-acl)
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| Challenge: | Recent work has aimed to enhance reasoning capabilities of language models, but these methods are limited to domains with objectively verifiable answers. |
| Approach: | They propose a self-play framework to improve reasoning on general-domain data. |
| Outcome: | Experiments show that the proposed framework improves reasoning performance on general-domain data while maintaining competitive performance on verifiable academic benchmarks. |
SEAD: A Surrogate-free Label-only Membership Inference Attack against Pre-trained LLMs with Semantic-Aware Density (2026.findings-acl)
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| Challenge: | Existing membership inference attacks require access to complete logits, but such access is often unavailable in real-world deployments where only the generated text is exposed. |
| Approach: | They propose a surrogate-free label-only MIA approach that directly estimates token probabilities through Monte Carlo sampling of the target model. |
| Outcome: | The proposed approach outperforms existing label-only attacks and serves as a foundational density estimator in the label-exclusive setting. |
MMAC: A Multilingual, Multimodal Alignment Framework for Cultural Grounding Evaluation (2026.acl-long)
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Weihua Zheng, Zhengyuan Liu, Tanmoy Chakraborty, Weiwen Xu, Xiaoxue Gao, Bryan Chen Zhengyu Tan, Bowei Zou, Chang Liu, Yujia Hu, Xing Xie, Xiaoyuan Yi, Jing Yao, Chaojun Wang, Long Li, Rui Liu, Huiyao Liu, Koji Inoue, Ryuichi Sumida, Tatsuya Kawahara, Fan Xu, Lingyu Ye, Wei Tian, Dongjun Kim, Jimin Jung, Jaehyung Seo, Nadya Yuki Wangsajaya, Pham Minh Duc, Ojasva Saxena, Palash Nandi, Xiyan Tao, Wiwik Karlina, Tuan Luong, Keertana Arun Vasan, Roy Ka-Wei Lee, Nancy F. Chen
| Challenge: | Existing models lack cultural alignment across modalities and languages . a new framework to assess cultural awareness across linguistics and languages is needed . |
| Approach: | They propose a framework that integrates tri-modally aligned cultural benchmarks and a five-dimensional evaluation protocol to assess cross-country awareness disparities. |
| Outcome: | The proposed framework assesses cultural awareness disparities across modalities and languages . it is the first dataset aligned at the input level across text, image, and speech . |
PLACES: Prompting Language Models for Social Conversation Synthesis (2023.findings-eacl)
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Maximillian Chen, Alexandros Papangelis, Chenyang Tao, Seokhwan Kim, Andy Rosenbaum, Yang Liu, Zhou Yu, Dilek Hakkani-Tur
| Challenge: | Currently, collecting high quality conversational data is expensive and infeasible for many applications . a promising direction is to generate synthetic dialogues by prompting large language models . |
| Approach: | They propose to use expert-written conversations as in-context examples to generate synthetic dialogues by prompting large language models. |
| Outcome: | The proposed approach is generalizable to multi-party conversations, compared to human-collected conversations. |
CRASpell: A Contextual Typo Robust Approach to Improve Chinese Spelling Correction (2022.findings-acl)
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| Challenge: | Recent research on Chinese spelling correction methods has poor performance on multi-typo texts. |
| Approach: | They propose to use Bert-based Chinese spelling correction models to overcome these limitations by constructing a noisy context for each training sample and a copy mechanism to encourage the model to choose the input character when the miscorrected and input character are both valid. |
| Outcome: | The proposed model outperforms state-of-the-art models on widely used benchmarks and achieves a remarkable gain. |
Mobile-Bench: An Evaluation Benchmark for LLM-based Mobile Agents (2024.acl-long)
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Shihan Deng, Weikai Xu, Hongda Sun, Wei Liu, Tao Tan, Liujianfeng Liujianfeng, Ang Li, Jian Luan, Bin Wang, Rui Yan, Shuo Shang
| Challenge: | Existing benchmarks for LLM-based mobile agents are insufficient to evaluate their capabilities. |
| Approach: | They propose a benchmark to evaluate LLM-based mobile agents' planning capabilities . they expand UI operations by incorporating 103 APIs to accelerate task completion . |
| Outcome: | The proposed benchmarks are based on 103 collected APIs and real user queries . the data is categorized into three distinct groups: SAST, SAMT, and MAMT . |
The Role of Visual Modality in Multimodal Mathematical Reasoning: Challenges and Insights (2025.acl-long)
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Yufang Liu, Yao Du, Tao Ji, Jianing Wang, Yang Liu, Yuanbin Wu, Aimin Zhou, Mengdi Zhang, Xunliang Cai
| Challenge: | Existing models that leverage visual information do not improve math reasoning performance . authors suggest that visual information is important for multimodal reasoning . |
| Approach: | They propose a dataset to require image reliance for problem-solving and challenge models with similar, yet distinct, images that change the correct answer. |
| Outcome: | The proposed model performance is unaffected by changes to or removal of images in the dataset. |
TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities (2023.acl-demo)
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Zhe Zhao, Yudong Li, Cheng Hou, Jing Zhao, Rong Tian, Weijie Liu, Yiren Chen, Ningyuan Sun, Haoyan Liu, Weiquan Mao, Han Guo, Weigang Gou, Taiqiang Wu, Tao Zhu, Wenhang Shi, Chen Chen, Shan Huang, Sihong Chen, Liqun Liu, Feifei Li, Xiaoshuai Chen, Xingwu Sun, Zhanhui Kang, Xiaoyong Du, Linlin Shen, Kimmo Yan
| Challenge: | Several pre-training models of different modalities are showing a rising trend of homogeneity in their model structures. |
| Approach: | They propose a toolkit that supports pre-training models of different modalities. |
| Outcome: | The proposed toolkit can match the performance of the original implementations on text, vision, and audio benchmarks. |
Dense Information Flow for Neural Machine Translation (N18-1)
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| Challenge: | Recent advances in deep neural networks have improved learning performance for NMT . Residual connections allow features from previous layers to be accumulated to the next layer easily. |
| Approach: | They propose a densely connected NMT architecture that can train more efficiently for NMT. |
| Outcome: | The proposed architecture improves learning performance and attention quality on multiple datasets. |
MolXPT: Wrapping Molecules with Text for Generative Pre-training (2023.acl-short)
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| Challenge: | Experimental results show that Generative pre-trained Transformers (GPT) have great success in natural language processing. |
| Approach: | They propose a unified language model of text and molecules pre-trained on SMILES wrapped by text. |
| Outcome: | The proposed model outperforms strong baselines of molecular property prediction on MoleculeNet and performs comparably to the best model in text-molecule translation while using less than half of its parameters. |
A Study of Reinforcement Learning for Neural Machine Translation (D18-1)
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| Challenge: | Recent studies have shown that reinforcement learning (RL) is an effective approach for improving the performance of neural machine translation systems. |
| Approach: | They propose to leverage reinforcement learning to boost the performance of NMT systems trained with monolingual data. |
| Outcome: | The proposed method achieves competitive results on translation tasks in English-German, Chinese-English and English-English systems. |
GS-Quant: Granular Semantic and Generative Structural Quantization for Knowledge Graph Completion (2026.acl-long)
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| Challenge: | Existing quantization-based approaches to knowledge Graph Completion (KGC) are incomplete. |
| Approach: | They propose a framework that generates semantically coherent discrete codes for KG entities . they introduce a Granular Semantic Enhancement module that injects hierarchical knowledge into the codebook . |
| Outcome: | The proposed framework outperforms existing text-based and embedding-based baselines in the KGC domain. |
A Fast and High-quality Text-to-Speech Method with Compressed Auxiliary Corpus and Limited Target Speaker Corpus (2024.lrec-main)
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| Challenge: | Existing methods to generate high-quality speech with limited target speaker corpus require extensive training data. |
| Approach: | They propose an auxiliary corpus compression algorithm that reduces the training cost while the naturalness of synthesized speech is not significantly degraded. |
| Outcome: | The proposed method significantly reduces training costs while maintaining the naturalness of synthesized speech. |
Reciprocal Learning of Knowledge Retriever and Response Ranker for Knowledge-Grounded Conversations (2022.coling-1)
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| Challenge: | Recent work on grounding dialogue agents with knowledge documents has sparked increased attention . hand-labeling data to that end is time-consuming and many datasets lack knowledge annotations . |
| Approach: | They propose a reciprocal learning approach to optimize a knowledge retriever and a response ranker for knowledge-grounded response retrieval without ground-truth knowledge labels. |
| Outcome: | The proposed model outperforms previous state-of-the-art methods on two public benchmarks. |
Enhancing Contrastive Learning with Noise-Guided Attack: Towards Continual Relation Extraction in the Wild (2024.acl-long)
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| Challenge: | Existing methods for continual relation extraction (CRE) excel in preserving old knowledge but falter when confronted with contaminated data streams. |
| Approach: | They propose a noise-resistant contrastive framework for continual relation extraction (CRE) that preserves old knowledge while learning incremental corrupted relations. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on various benchmarks with increasing noise rates. |
Chain-of-Thought Prompting Obscures Hallucination Cues in Large Language Models: An Empirical Evaluation (2025.findings-emnlp)
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| Challenge: | Chain-of-Thought (CoT) prompting can mitigate hallucinations by encouraging step-by-step reasoning, but its impact on halluciation detection remains underexplored. |
| Approach: | They conduct an empirical evaluation of CoT prompting in Large Language Models (LLMs) to examine their impact on hallucination detection methods. |
| Outcome: | The proposed method significantly affects the internal states and token probability distributions of the LLM. |
Rescue: Ranking LLM Responses with Partial Ordering to Improve Response Generation (2024.acl-srw)
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| Challenge: | Customizing LLMs for a specific task involves separating high-quality responses from lower-quality ones. Obtaining a large volume of expert-annotated data is costly for most tasks. |
| Approach: | They propose a method that trains the model to prioritize the best responses from a pool of candidates created for a task using ranking metrics. |
| Outcome: | The proposed method is more robust, less sensitive to noise, and can be achieved with limited human annotations or through heuristic methods. |
Planning Like Human: A Dual-process Framework for Dialogue Planning (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) operate in a reactive mode, often resulting in efficiency issues or suboptimal performance. |
| Approach: | They propose a dual-process dialogue planning framework that leverages the dual-process theory of human cognition and a deliberative Monte Carlo Tree Search mechanism to emulate human-like conversational dynamics. |
| Outcome: | The proposed framework outperforms existing methods in achieving high-quality dialogues and operational efficiency. |
Visual Interrogation of Attention-Based Models for Natural Language Inference and Machine Comprehension (D18-2)
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| Challenge: | Neural networks models have gained popularity due to their state-of-the-art performance but lack of interpretability hinders their deployment and refinement. |
| Approach: | They propose a visual analytic library that provides a user with a customizable visual anallytic environment. |
| Outcome: | The proposed visualization library provides an interactive environment in which the user can investigate and interrogate the relationships between input, model internals and output predictions. |
Representation Learning with Ordered Relation Paths for Knowledge Graph Completion (D19-1)
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| Challenge: | Existing knowledge graphs are incomplete and lack the order of relations in paths. |
| Approach: | They propose a method which takes relation paths into account but ignores order of relations in paths which is important for reasoning. |
| Outcome: | The proposed method performs better than state-of-the-art methods on two benchmark datasets. |
Outcome Accuracy is Not Enough: Aligning the Reasoning Process of Reward Models (2026.acl-long)
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Binghai Wang, Yantao Liu, Yuxuan Liu, Tianyi Tang, Shenzhi Wang, Chang Gao, Chujie Zheng, Yichang Zhang, Le Yu, Shixuan Liu, Tao Gui, Qi Zhang, Xuanjing Huang, Bowen Yu, Fei Huang, Junyang Lin
| Challenge: | Recent studies observe a phenomenon where reward models achieve high accuracy on static datasets but fail to generalize effectively during RLHF. |
| Approach: | They propose a method that combines rationale consistency with outcome accuracy to improve performance on RM-Bench and JudgeBench. |
| Outcome: | The proposed method surpasses baselines on RM-Bench and JudgeBench by an average of 5% and improves creative writing tasks by 7%. |
VulLibGen: Generating Names of Vulnerability-Affected Packages via a Large Language Model (2024.acl-long)
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Tianyu Chen, Lin Li, ZhuLiuchuan ZhuLiuchuan, Zongyang Li, Xueqing Liu, Guangtai Liang, Qianxiang Wang, Tao Xie
| Challenge: | Existing work on affected package identification is limited by large language models . a recent study shows that 84% third-party packages contain security vulnerabilities . |
| Approach: | They propose a method to use LLM to generate the affected package . they propose supervised fine-tuning, retrieval augmented generation and a local search algorithm . |
| Outcome: | The proposed method has an average precision of 0.806 for identifying vulnerable packages in four most popular ecosystems in GitHub Advisory. |
A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning (2022.coling-1)
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Kunbo Ding, Weijie Liu, Yuejian Fang, Weiquan Mao, Zhe Zhao, Tao Zhu, Haoyan Liu, Rong Tian, Yiren Chen
| Challenge: | Existing zero-shot cross-lingual transfer methods rely on parallel corpora or bilingual dictionaries . however, its effect is limited by the gap between embedding clusters of different languages . |
| Approach: | They propose Embedding-Push, Attention-Pull, and Robust targets to transfer English embeddings to virtual multilingual embedders without semantic loss. |
| Outcome: | Experimental results show that the proposed method outperforms existing methods on cross-lingual tasks and can achieve a better multilingual alignment. |
ARM: An Alignment-and-Replacement Module for Chinese Spelling Check Based on LLMs (2024.emnlp-main)
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| Challenge: | Chinese Spelling Check (CSC) aims to identify and correct spelling errors in Chinese texts, where enhanced semantic understanding of a sentence can significantly improve correction accuracy. |
| Approach: | They propose a plug-and-play Alignment-and -Replacement module that enhances existing Chinese CSC models without retraining or fine-tuning. |
| Outcome: | The proposed module improves existing models while reducing retraining and fine-tuning. |
Improving Large Language Models Function Calling and Interpretability via Guided-Structured Templates (2025.emnlp-main)
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Hy Dang, Tianyi Liu, Zhuofeng Wu, Jingfeng Yang, Haoming Jiang, Tao Yang, Pei Chen, Zhengyang Wang, Helen Wang, Huasheng Li, Bing Yin, Meng Jiang
| Challenge: | Large language models (LLMs) have strong reasoning and tool-use capabilities, yet fail in real-world tool-interactions due to incorrect parameterization, poor tool selection, or misinterpretation of user intent. |
| Approach: | They propose a curriculum-inspired framework that leverages structured reasoning templates to guide LLMs through more deliberate step-by-step instructions for generating function calls. |
| Outcome: | The proposed framework reduces tool-use errors and improves interpretability and transparency of tool-using agents. |
MARS-Bench: A Multi-turn Athletic Real-world Scenario Benchmark for Dialogue Evaluation (2025.findings-emnlp)
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Chenghao Yang, Yinbo Luo, Zhoufutu Wen, Qi Chu, Tao Gong, Longxiang Liu, Kaiyuan Zhang, Jianpeng Jiao, Ge Zhang, Wenhao Huang, Nenghai Yu
| Challenge: | Large Language Models (LLMs) have been widely adopted in real-world dialogue applications, but their robustness is criticized all along. |
| Approach: | They propose to use play-by-play text commentary to build a multi-turn athletic real-world scenario dialogue benchmark to evaluate three critical aspects of multi-turned conversations: ultra multi- turn, interactive multi-twist, and cross-turn tasks. |
| Outcome: | The proposed benchmarks outperform open-source LLMs on three critical aspects of multi-turn conversations: ultra multi-turned, interactive multi- turn, and cross-turn tasks. |
PAD-Net: An Efficient Framework for Dynamic Networks (2023.acl-long)
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| Challenge: | Dynamic networks can significantly improve the model’s representation power with acceptable computational cost. |
| Approach: | They propose a partially dynamic network to transform redundant dynamic parameters into static ones and iterative mode partition to partition dynamic and static parameters efficiently. |
| Outcome: | The proposed network surpasses fully dynamic networks by +0.7% top-1 acc with only 30% dynamic parameters for DY-Conv and +1.9% average score in language understanding with only 50% dynamic parameters. |
FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition (2021.findings-emnlp)
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Yichong Leng, Xu Tan, Rui Wang, Linchen Zhu, Jin Xu, Wenjie Liu, Linquan Liu, Xiang-Yang Li, Tao Qin, Edward Lin, Tie-Yan Liu
| Challenge: | Error correction is widely used in automatic speech recognition (ASR) to post-process the generated sentence. |
| Approach: | They propose a fast correction model that takes multiple ASR candidates as input for better correction accuracy. |
| Outcome: | The proposed model can reduce the word error rate (WER) with multiple candidates by 3.2% and 2.6%. |
LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models (2026.acl-long)
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Ming Zhang, Yujiong Shen, Jingyi Deng, Yuhui Wang, Huayu Sha, Kexin Tan, Qiyuan Peng, Yue Zhang, Junzhe Wang, Shichun Liu, Yueyuan Huang, Jingqi Tong, Changhao Jiang, Yilong Wu, Zhihao Zhang, Mingqi Wu, Mingxu Chai, Zhiheng Xi, Shihan Dou, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing evaluation of Large Language Models on static benchmarks is vulnerable to data contamination and leaderboard overfitting. |
| Approach: | LLMEval-Fair framework provides a framework for dynamic evaluation of Large Language Models . evaluators use a proprietary bank of 220k graduate-level questions to analyze model data . |
| Outcome: | LLMEval-Fair provides robust and credible evaluation framework for Large Language Models . it provides a strong empirical validation for the dynamic evaluation paradigm . |
Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimation (2024.emnlp-main)
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Yuan Ge, Yilun Liu, Chi Hu, Weibin Meng, Shimin Tao, Xiaofeng Zhao, Mahong Xia, Zhang Li, Boxing Chen, Hao Yang, Bei Li, Tong Xiao, JingBo Zhu
| Challenge: | Existing methods for instruction data selection have limitations such as relying on fragile external APIs, being affected by biases in GPT models, or reducing the diversity of the selected instruction dataset. |
| Approach: | They propose an industrial-friendly, expert-aligned and diversity-preserved instruction data selection method: Clustering and Ranking (CaR). |
| Outcome: | The proposed method outperforms Alpaca's existing methods by 32.1% in GPT-4 evaluations. |
Exploring the Secrets Behind the Learning Difficulty of Meaning Representations for Semantic Parsing (2022.emnlp-main)
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| Challenge: | Existing studies show that the design of Meaning Representation (MR) greatly influences the final model performance of a neural semantic parser. |
| Approach: | They propose a data-aware metric called ISS to measure the final performance of MRs. |
| Outcome: | The proposed metric denoting incremental structural stability (ISS) of MRs can be used as an indicator for MR design to avoid the costly training-testing process. |
The Box is in the Pen: Evaluating Commonsense Reasoning in Neural Machine Translation (2020.findings-emnlp)
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| Challenge: | a test suite to evaluate commonsense reasoning capability of neural machine translation is presented . language models pretrained on large-scale corpora achieve a commonsensing accuracy of lower than 72% on target translations of this test suite. |
| Approach: | They propose a test suite to evaluate the commonsense reasoning capability of neural machine translation. |
| Outcome: | The proposed test suite performs poorly on commonsense reasoning of the three ambiguity types in terms of reasoning accuracy and reasoning consistency. |
Reinforcement Learning on Pre-Training Data (2026.acl-long)
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Siheng Li, Kejiao Li, Zenan Xu, Guanhua Huang, Kun Li, Haoyuan Wu, null Wujiajia, Zihao Zheng, Chenchen Zhang, Kun Shi, Xue Gong, Qi Yi, Ruibin Xiong, Tingqiang Xu, Yuhao Jiang, Jianfeng Yan, Yuyuan Zeng, Guanghui Xu, Jinbao Xue, Zhijiang xu, Zheng Fang, Shuai LI, Qibin Liu, Xiaoxue Li, Zhuoyu Li, Yangyu Tao, Fei Gao, Cheng Jiang, Bochao Wang, Kai Liu, Jianchen Zhu, Wai Lam, Bo Zhou, Di Wang
| Challenge: | Recent progress in large language models is driven by scaling of training compute through pre-training with nexttoken prediction (NTP) or post-training (RL) Pre-training using NTP enables models to acquire extensive knowledge and skills from general data, but it suffers from data inefficiency and catastrophic forgetting in continual learning settings. |
| Approach: | They propose to scale training compute through pre-training with next-token prediction (NTP) or post-training by scaling reinforcement learning (RL) to improve learning from general data. |
| Outcome: | Experiments on multiple benchmarks and models show that the proposed approach improves continual pre-training and provides a strong foundation for post-training on Qwen3-8B-Base. |
RaaS: Reasoning-Aware Attention Sparsity for Efficient LLM Reasoning (2025.findings-acl)
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Junhao Hu, Wenrui Huang, Weidong Wang, Zhenwen Li, Tiancheng Hu, Zhixia Liu, Xusheng Chen, Tao Xie, Yizhou Shan
| Challenge: | Large Language Models (LLMs) have demonstrated strong capabilities across various domains, but their large-scale deployment faces a major obstacle: the high computational cost of long-sequence inference. |
| Approach: | They propose an algorithm that retains key-value vectors until they are no longer needed to solve reasoning tasks. |
| Outcome: | The proposed algorithm achieves high accuracy with O(L) time but O(N) memory complexities. |
Multi-Granularity Structural Knowledge Distillation for Language Model Compression (2022.acl-long)
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| Challenge: | Existing methods to transfer knowledge to a small model are not enough to represent the rich semantics of a text. |
| Approach: | They propose to distill the knowledge to a student hierarchically across layers using a large teacher-student framework. |
| Outcome: | Experimental results show that the proposed method outperforms distillation methods on GLUE benchmark. |
Arxiv Copilot: A Self-Evolving and Efficient LLM System for Personalized Academic Assistance (2024.emnlp-demo)
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| Challenge: | Existing solutions for document QA fail to provide personalized and up-to-date information efficiently. |
| Approach: | They propose to deploy a self-evolving, efficient LLM system that can offer personalized research services, maintaining a real-time updated database. |
| Outcome: | The proposed system saves 69.92% of time after efficient deployment. |
BCL: Bayesian In-Context Learning Framework for Information Extraction (2026.findings-acl)
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Haoliang Liu, Chengkun Cai, Xu Zhao, Han Zhu, Shizhou Huang, Xinglin Zhang, Tao Chen, Jenq-Neng Hwang, Zhang Huaping, Lei Li
| Challenge: | Existing information extraction (IE) tasks rely on in-context learning with large language models. |
| Approach: | They propose a Bayesian-based in-context learning framework that refines label representations across IE tasks using particle filtering and Bayes updates. |
| Outcome: | The proposed framework improves performance over existing methods (up to 30%) it underperforms one-shot prompting by a substantial margin on NER tasks and CodeIE fails on RE tasks with near-zero micro-F1. |
LoRE-Merging: Exploring Low-Rank Estimation For Large Language Model Merging (2025.findings-emnlp)
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| Challenge: | a framework for model merging is proposed without additional training . task vectors from fine-tuned models exhibit a limited number of dominant singular values . |
| Approach: | They propose a framework for model merging based on low-rank estimation of task vectors without access to the base model. |
| Outcome: | The proposed framework improves models without additional training without additional inputs. |
XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser (2025.coling-main)
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Xianfu Cheng, Hang Zhang, Jian Yang, Xiang Li, Weixiao Zhou, Fei Liu, Kui Wu, Xiangyuan Guan, Tao Sun, Xianjie Wu, Tongliang Li, Zhoujun Li
| Challenge: | Document AI parsing semi-structured image form is a key information extraction task. |
| Approach: | They propose a multimodal and multilingual semi-structured FORM PARSER which integrates SER and relation extraction into a unified framework. |
| Outcome: | The proposed framework achieves up to 1.79% improvement on RE tasks in multilingual and zero-shot settings. |
ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation (2022.acl-long)
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Bei Li, Quan Du, Tao Zhou, Yi Jing, Shuhan Zhou, Xin Zeng, Tong Xiao, JingBo Zhu, Xuebo Liu, Min Zhang
| Challenge: | Residual networks are an Euler discretization of solutions to Ordinary Differential Equations (ODE). |
| Approach: | They propose a residual block of layers in Transformer that can be described as a higher-order solution to ODE. |
| Outcome: | The proposed architecture can gain large improvements over strong baselines at a slight cost in inference efficiency. |
Divergent Thinking: Escape the Homogeneity Trap in Generative Commonsense Reasoning (2026.findings-acl)
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| Challenge: | Generative commonsense reasoning requires models to synthesize coherent narratives that satisfy lexical constraints and commonsensical logic. |
| Approach: | They propose a framework that allows for deep semantic diversity rather than surface-level lexical variation. |
| Outcome: | The proposed framework achieves over 10% improvement in overall accuracy on NoRa and SPICE score on CommonGen-Lite. |
CORE: Cooperative Training of Retriever-Reranker for Effective Dialogue Response Selection (2023.acl-long)
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| Challenge: | Existing methods to train retrieval-based dialogue systems are suboptimal . existing methods to optimize retrieval and rerank modules are sub-optimal, causing sub-optimum performance. |
| Approach: | They propose a retrieval-based dialogue system with a fast retriever and a smart response reranker that combine the best of both worlds. |
| Outcome: | The proposed method can learn from each other and evolve together . it can be used in industrial applications and has powered industrial applications. |
InfiMM: Advancing Multimodal Understanding with an Open-Sourced Visual Language Model (2024.findings-acl)
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Haogeng Liu, Quanzeng You, Yiqi Wang, Xiaotian Han, Bohan Zhai, Yongfei Liu, Wentao Chen, Yiren Jian, Yunzhe Tao, Jianbo Yuan, Ran He, Hongxia Yang
| Challenge: | InfiMM is a multimodal large language model that adapts to complex vision-language tasks. |
| Approach: | They present a Multimodal Large Language Model that adapts to intricate vision-language tasks using large-scale training data and comprehensive training strategies. |
| Outcome: | Empirical evaluations across a variety of benchmarks underscore InfiMM’s remarkable capability in multimodal understanding. |
EvoR: Evolving Retrieval for Code Generation (2024.findings-emnlp)
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| Challenge: | Existing pipelines for retrieval-augmented code generation (RACG) use static knowledge bases with a single source, limiting adaptation capabilities of Large Language Models (LLMs) Extensive experiments demonstrate that EVOR achieves two to four times of execution accuracy compared to other methods such as Reflexion. |
| Approach: | They propose a retrieval-augmented code generation pipeline that employs the synchronous evolution of queries and diverse knowledge bases. |
| Outcome: | The proposed pipeline achieves two to four times of execution accuracy compared to other methods. |
OEE-CFC: A Dataset for Open Event Extraction from Chinese Financial Commentary (2024.findings-emnlp)
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Qizhi Wan, Changxuan Wan, Rong Hu, Dexi Liu, Xu Wenwu, Kang Xu, Zou Meihua, Liu Tao, Jie Yang, Zhenwei Xiong
| Challenge: | Existing corpora with unconventional entities serving as event arguments lack rich multi-events and shared arguments. |
| Approach: | They develop an open event template that includes 21 event argument roles and an open corpus supporting open event extraction. |
| Outcome: | The proposed corpus includes 17,469 events, 44,221 arguments, 3,644 complex arguments, and 5,898 shared arguments. |
Unlocking Data-free Low-bit Quantization with Matrix Decomposition for KV Cache Compression (2024.acl-long)
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| Challenge: | Existing methods to compress KV cache compromise precision or require extra data for calibration, limiting their practicality in LLM deployment. |
| Approach: | They propose a low-bit quantization technique based on tensor decomposition to effectively compress KV cache. |
| Outcome: | The proposed method reduces memory footprint and performance by 75% . it is compared with existing methods that compromise precision or require extra data for calibration . |
TransGEC: Improving Grammatical Error Correction with Translationese (2023.findings-acl)
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| Challenge: | Experimental results show that data augmentation improves accuracy over strong baselines. |
| Approach: | They propose to use translationese as input for GEC data augmentation to overcome stylistic discrepancies . they propose to obtain human-translated texts with a more similar style to non-native texts . |
| Outcome: | The proposed method improves correction accuracy over strong baselines on four GEC benchmarks. |
T2A-Feedback: Improving Basic Capabilities of Text-to-Audio Generation via Fine-grained AI Feedback (2025.acl-long)
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Zehan Wang, Ke Lei, Chen Zhu, Jiawei Huang, Sashuai Zhou, Luping Liu, Xize Cheng, Shengpeng Ji, Zhenhui Ye, Tao Jin, Zhou Zhao
| Challenge: | Text-to-audio (T2A) models still struggle to satisfy human preferences for prompt-following and acoustic quality when generating complex multi-event audio. |
| Approach: | They propose to use AI feedback learning to enhance basic capabilities of text-to-audio models . they use a large audio preference dataset to evaluate the model's capabilities . |
| Outcome: | The proposed model improves in simple and complex scenarios with AI feedback learning. |
Knowledge Poisoning Attacks on Medical Multi-Modal Retrieval-Augmented Generation (2026.acl-long)
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Peiru Yang, Haoran Zheng, Tong Ju, Shiting Wang, Wanchun Ni, Jiajun Liu, Shangguang Wang, Yongfeng Huang, Tao Qi
| Challenge: | Existing studies have investigated knowledge poisoning attacks in medical RAG systems . knowledge poison attacks can disrupt model outputs and undermine system reliability . |
| Approach: | They propose a knowledge poisoning framework that injects misinformation into textual data . they propose to use paired visual data as a query-agnostic trigger to promote retrieval . |
| Outcome: | The proposed framework produces clinically plausible but incorrect generations on five LLMs and datasets. |
KaFT: Knowledge-aware Fine-tuning for Boosting LLMs’ Domain-specific Question-Answering Performance (2025.findings-acl)
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| Challenge: | Recent literature reveals that supervised fine-tuning (SFT) is suboptimal for domain-specific question-answering tasks. |
| Approach: | They propose a query diversification strategy for robust conflict detection and a knowledge-aware fine-tuning approach to effectively boost LLMs’ performance. |
| Outcome: | The proposed approach improves the model generalization and alleviates the hallucination. |
An Extensible Plug-and-Play Method for Multi-Aspect Controllable Text Generation (2023.acl-long)
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| Challenge: | Multi-aspect controllable text generation has attracted increasing attention . but the mutual interference of multiple prefixes limits its extensibility to training-time unseen combinations. |
| Approach: | They propose to use trainable gates to normalize the intervention of prefixes to restrain the interference. |
| Outcome: | The proposed approach outperforms baselines on constraint accuracy, text quality, and extensibility. |
Knowledge Extraction on Semi-Structured Content: Does It Remain Relevant for Question Answering in the Era of LLMs? (2026.eacl-long)
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Kai Sun, Yin Huang, Srishti Mehra, Mohammad Kachuee, Xilun Chen, Renjie Tao, Zhaojiang Lin, Andrea Jessee, Nirav Shah, Alex L Betty, Yue Liu, Anuj Kumar, Wen-tau Yih, Xin Luna Dong
| Challenge: | Existing literature on knowledge extraction for question answering questions whether it is still relevant for question answerrs. |
| Approach: | They extend an existing benchmark with knowledge extraction annotations and evaluate commercial and open-source LLMs of varying sizes. |
| Outcome: | The proposed model can achieve high QA accuracy, but can still benefit from knowledge extraction through augmentation with extracted triples and multi-task learning. |
MdEval: Massively Multilingual Code Debugging (2026.findings-acl)
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Shukai Liu, Linzheng Chai, Jian Yang, Jiajun Shi, He Zhu, Liran Wang, Jin Ke, Wei Zhang, Hualei Zhu, Shuyue Guo, Tao Sun, Jiaheng Liu, Yunlong Duan, Yu Hao, Liqun Yang, Guanglin Niu, Ge Zhang, Zhoujun Li
| Challenge: | Existing benchmarks primarily focus on Python and are limited in terms of language diversity. |
| Approach: | They propose a multilingual debugging benchmark that includes 3.9K test samples of 20 programming languages and introduces the debug instruction corpora MdEval-Instruct by injecting bugs into the correct multilingual queries and solutions. |
| Outcome: | The proposed benchmark includes 3.9K test samples of 20 programming languages and covers the automated program repair task, bug localization task, and bug identification task. |
d-TreeRPO: Towards More Reliable Policy Optimization for Diffusion Language Models (2026.acl-long)
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Leyi Pan, Shuchang Tao, Yunpeng Zhai, Zheyu Fu, Liancheng Fang, Minghua He, Lingzhe Zhang, Zhaoyang Liu, Bolin Ding, Aiwei Liu, Lijie Wen
| Challenge: | Existing RL methods suffer from reliability bottlenecks due to reward sparsity and intractable computations . d-TreeRPO provides fine-grained and verifiable step-wise reward signals . |
| Approach: | They propose a reliable reinforcement learning framework for diffusion large language models that leverages tree-structured rollouts and bottom-up advantage computation based on verifiable outcome rewards. |
| Outcome: | The proposed framework outperforms baseline models and achieves significant improvements across reasoning benchmarks. |
PlugAT: A Plug and Play Module to Defend against Textual Adversarial Attack (2022.coling-1)
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| Challenge: | Recent studies have shown that adversarial examples can be easily fooled by adversarially perturbed examples. |
| Approach: | They propose a pluggable defense module PlugAT to provide robust predictions by adding a few trainable parameters to the model inputs while keeping the original model frozen. |
| Outcome: | The proposed model improves robustness over several strong baselines whilst training only 9.1% parameters. |
Detecting Adversarial Samples through Sharpness of Loss Landscape (2023.findings-acl)
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Rui Zheng, Shihan Dou, Yuhao Zhou, Qin Liu, Tao Gui, Qi Zhang, Zhongyu Wei, Xuanjing Huang, Menghan Zhang
| Challenge: | Existing studies have shown that adversarial samples are more vulnerable than normal ones to textual adversarials. |
| Approach: | They propose a simple and effective sharpness-based detector that can distinguish adversarial samples by maximizing the loss increment within the region where the inference sample is located. |
| Outcome: | The proposed method outperforms previous detection methods by large margins on three text classification tasks. |
Benchmarking Long-Context Language Models on Long Code Understanding (2025.acl-long)
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Jia Li, Xuyuan Guo, Lei Li, Kechi Zhang, Ge Li, Jia Li, Zhengwei Tao, Fang Liu, Chongyang Tao, Yuqi Zhu, Zhi Jin
| Challenge: | Currently, long-context language models are limited by the lack of a rigorous evaluation framework for long code understanding. |
| Approach: | They propose to use a long code understanding benchmark LongCodeU to evaluate LCLMs' long code comprehension ability for practical applications. |
| Outcome: | The proposed benchmarks show that current LCLMs are limited in their long code understanding ability, particularly when the long code length is greater than 32K, falling far short of their claimed 128K to 1M context windows. |
Length-Adaptive Distillation: Customizing Small Language Model for Dynamic Token Pruning (2023.findings-emnlp)
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| Challenge: | Existing methods to accelerate inference speed are model compression and dynamic computation (e.g., dynamic token pruning). |
| Approach: | They propose a two-stage knowledge distillation framework that produces a customized small language model for dynamic token pruning. |
| Outcome: | The proposed framework can make the small language model more customized for dynamic token pruning and achieve better speed-performance trade-off. |
CoEvo: Coevolution of LLM and Retrieval Model for Domain-Specific Information Retrieval (2025.emnlp-main)
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Ang Li, Yiquan Wu, Yinghao Hu, Lizhi Qing, Shihang Wang, Chengyuan Liu, Tao Wu, Adam Jatowt, Ming Cai, Fei Wu, Kun Kuang
| Challenge: | Recent methods to enhance queries by generating intermediary elements can degrade retrieval performance . combining LLMs and retrievers can be difficult, resulting in unreliable or irrelevant intermediaries . |
| Approach: | They propose a framework that facilitates the coevolution of large language models and retrieval models. |
| Outcome: | The proposed framework facilitates the coevolution of LLMs and retrieval models. |
ProcWorld: Benchmarking Large Model Planning in Reachability-Constrained Environments (2025.emnlp-main)
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| Challenge: | Existing benchmarks for embodied spatial reasoning and long-term planning are non-trivial due to the combinatorial complexity of long-horizon abstract reasoning. |
| Approach: | They propose a large-scale benchmark for partially observable embodied spatial reasoning and long-term planning with large language models and vision language models. |
| Outcome: | The proposed model performs better in 16 task types, 5,000 rooms, and over 10 million evaluation trajectories with diverse data distribution. |
DeepRapper: Neural Rap Generation with Rhyme and Rhythm Modeling (2021.acl-long)
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| Challenge: | Existing systems for rap generation focus on rhyming lyrics but ignore rhythmic beats . rap lyrics need to be semantically meaningful and fashionable to convey interesting stories . |
| Approach: | They develop a Transformer-based rap generation system that can model both rhymes and rhythms. |
| Outcome: | The proposed system generates high-quality raps with rhymes and rhythms . it is based on a Transformer-based language model . |
How to Represent Context Better? An Empirical Study on Context Modeling for Multi-turn Response Selection (2022.findings-emnlp)
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| Challenge: | Existing work on building a conversational system for open domain human-machine conversation is attracting more attention . early models concatenate all utterances or independently encode each dialogue turn, which may lead to an inadequate understanding of dialogue status. |
| Approach: | They propose to use a turn-aware context modeling layer to adapt existing models . they propose to model multi-turn contexts from the perspective of sequential relationship, local relationship, and query-alike manner . |
| Outcome: | The proposed method can be adapted to several advanced response selection models. |
ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)
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Ruibin Yuan, Hanfeng Lin, Yi Wang, Zeyue Tian, Shangda Wu, Tianhao Shen, Ge Zhang, Yuhang Wu, Cong Liu, Ziya Zhou, Liumeng Xue, Ziyang Ma, Qin Liu, Tianyu Zheng, Yizhi Li, Yinghao Ma, Yiming Liang, Xiaowei Chi, Ruibo Liu, Zili Wang, Chenghua Lin, Qifeng Liu, Tao Jiang, Wenhao Huang, Wenhu Chen, Jie Fu, Emmanouil Benetos, Gus Xia, Roger Dannenberg, Wei Xue, Shiyin Kang, Yike Guo
| Challenge: | Despite LLMs' impressive capabilities in musical knowledge, music reasoning remains an unsolved task. |
| Approach: | They propose an open-source large language model (LLM) that integrates intrinsic musical abilities into LLaMA2 and GPT-3.5. |
| Outcome: | The proposed model can understand and generate music with a pure text tokenizer without external multi-modal neural structures or tokenizers. |
Robust and Scalable Model Editing for Large Language Models (2024.lrec-main)
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Yingfa Chen, Zhengyan Zhang, Xu Han, Chaojun Xiao, Zhiyuan Liu, Chen Chen, Kuai Li, Tao Yang, Maosong Sun
| Challenge: | Existing methods that ignore contextual knowledge fail to reliably fall back to parametric knowledge when presented with irrelevant context. |
| Approach: | They propose to use contextual knowledge to update and correct LLMs' knowledge by in-context editing instead of retraining. |
| Outcome: | The proposed method outperforms current state-of-the-art methods by a large margin on a dataset that contains irrelevant questions. |
On the Complementarity between Pre-Training and Random-Initialization for Resource-Rich Machine Translation (2022.coling-1)
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| Challenge: | Pre-Training (PT) of text representations has been successfully applied to low-resource Neural Machine Translation (NMT) however, it often fails to achieve notable gains on resource-rich NMT on par with its Random-Initialization (RI) counterpart. |
| Approach: | They propose to combine pre-training and random-initialization techniques to achieve significant improvements in NMT. |
| Outcome: | The proposed model fusion algorithm can achieve significant improvements on two resource-rich translation benchmarks. |
Revisiting Demonstration Selection Strategies in In-Context Learning (2024.acl-long)
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| Challenge: | Large language models (LLMs) have shown an impressive ability to perform a wide range of tasks using in-context learning (ICL). |
| Approach: | They propose a data- and model-dependent method to select models using in-context learning, TopK + ConE, and propose unified explanations for the effectiveness of previous methods. |
| Outcome: | The proposed method improves language understanding and generation tasks with different model scales. |
Characterizing the Impacts of Instances on Robustness (2023.findings-acl)
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Rui Zheng, Zhiheng Xi, Qin Liu, Wenbin Lai, Tao Gui, Qi Zhang, Xuanjing Huang, Jin Ma, Ying Shan, Weifeng Ge
| Challenge: | Existing defense approaches focus on developing new model structures or training algorithms, but they do little to tap the potential of training instances. |
| Approach: | They propose a method that can distinguish between robust and non-robust instances according to the model’s sensitivity to perturbations on individual instances during training. |
| Outcome: | The proposed method can distinguish between robust and non-robust instances according to the model’s sensitivity to perturbations on individual instances during training. |
Controllable Contamination Detection for Reliable LLM Evaluation with Statistical Guarantees (2026.acl-long)
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Zheng Zhang, Qi Liu, Siyuan Liang, Ning Li, Zirui Hu, Weibo Gao, Rui Li, Zhenya Huang, Leszek Rutkowski, Baosheng Yu, Dacheng Tao
| Challenge: | Existing training data detectors fail to detect clean samples from contaminated test sets . existing methods fail to identify clean samples due to black-box nature of LLMs . |
| Approach: | They propose a framework that detects and filters contaminated evaluation data . they propose 'failure detection' to reduce the proportion of contaminated samples mistakenly retained . |
| Outcome: | The proposed framework reduces false discovery rate (FDR) under valid FDR control while maintaining evaluation consistency. |
TriPlay-RL: Tri-Role Self-Play Reinforcement Learning for LLM Safety Alignment (2026.acl-long)
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Zhewen Tan, Wenhan Yu, Jianfeng Si, Tongxin Liu, Kaiqi Guan, Huiyan Jin, Jiawen Tao, Xiaokun Yuan, Xiangzheng Zhang, Duohe Ma, Tong Yang, Lin Sun
| Challenge: | Existing approaches to safety alignment of large language models rely on costly manual annotations or human review. |
| Approach: | They propose a closed-loop reinforcement learning framework called TriPlay-RL that enables iterative collaboration among three roles with near-zero manual annotation. |
| Outcome: | The proposed framework achieves 20%–50% improvement in adversarial effectiveness while preserving high output diversity while achieving 10%–30% gains in safety performance without degrading general reasoning capability. |
MobileVLM: A Vision-Language Model for Better Intra- and Inter-UI Understanding (2024.findings-emnlp)
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Qinzhuo Wu, Weikai Xu, Wei Liu, Tao Tan, Liujian Liujianfeng, Ang Li, Jian Luan, Bin Wang, Shuo Shang
| Challenge: | Recent mobile AI agents based on VLMs lack basic mobile capabilities due to their pre-trained nature. |
| Approach: | They propose a mobile AI agent based on VLMs that includes additional pre-training stages to enhance both intra- and inter-UI understanding. |
| Outcome: | The proposed model outperforms existing VLMs on the Chinese mobile dataset Mobile3M . |
Monotonic Paraphrasing Improves Generalization of Language Model Prompting (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable proficiency in zero-shot decision making and instruction following. |
| Approach: | They propose an end-to-end decoding strategy that paraphrases given prompts or instructions into their lower perplexity counterparts based on an ensemble of a paraphrase LM for prompt rewriting, and a target LM that constrains the generation for lower perxity. |
| Outcome: | The proposed method can efficiently paraphrase the original prompt without altering its semantic meaning while decreasing the perplexity of each generation as calculated by the target LM. |
Don’t Take It Literally: An Edit-Invariant Sequence Loss for Text Generation (2022.naacl-main)
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Guangyi Liu, Zichao Yang, Tianhua Tao, Xiaodan Liang, Junwei Bao, Zhen Li, Xiaodong He, Shuguang Cui, Zhiting Hu
| Challenge: | Neural text generation models are typically trained by maximizing log-likelihood with the sequence cross entropy (CE) loss. |
| Approach: | They propose an Edit-Invariant Sequence Loss method which computes the matching loss of a target sequence with all n-grams in the generated sequence. |
| Outcome: | The proposed method outperforms the common CE loss and strong baselines on a wide range of tasks. |
Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching (2022.findings-naacl)
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Kunbo Ding, Weijie Liu, Yuejian Fang, Zhe Zhao, Qi Ju, Xuefeng Yang, Rong Tian, Zhu Tao, Haoyan Liu, Han Guo, Xingyu Bai, Weiquan Mao, Yudong Li, Weigang Guo, Taiqiang Wu, Ningyuan Sun
| Challenge: | Existing studies have shown that cross-lingual knowledge distillation can improve the performance of pre-trained models for cross-linguistic similarity matching tasks. |
| Approach: | They propose a multi-stage distillation framework for constructing a small-size but high-performance cross-lingual model using contrastive learning, bottleneck, and parameter recurrent strategies. |
| Outcome: | The proposed model can compress the size of XLM-R and MiniLM by more than 50% while the performance is only reduced by about 1%. |
PDALN: Progressive Domain Adaptation over a Pre-trained Model for Low-Resource Cross-Domain Named Entity Recognition (2021.emnlp-main)
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| Challenge: | Existing approaches to Named Entity Recognition (NER) are limited in labeled resources and domain shift. |
| Approach: | They propose a progressive domain adaptation knowledge distillation approach to adapt high-resource domains to low-resourced target domains by employing three components to achieve superior domain adaptability. |
| Outcome: | The proposed approach can adapt high-resource domains to low-resourced target domains even if they are diverse in terms and writing styles. |
Part Represents Whole: Improving the Evaluation of Machine Translation System Using Entropy Enhanced Metrics (2022.findings-aacl)
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| Challenge: | Existing machine translation metrics have poor correlations with human assessments . entropy-based evaluations are often limited to a limited number of samples . |
| Approach: | They propose a fast and unsupervised approach to enhance machine translation metrics using entropy by introducing sentence-level difficulty. |
| Outcome: | The proposed method outperforms existing metrics on five sub-tracks in the WMT19 Metrics shared tasks. |
ROSE Doesn’t Do That: Boosting the Safety of Instruction-Tuned Large Language Models with Reverse Prompt Contrastive Decoding (2024.findings-acl)
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| Challenge: | Existing methods for aligning LLMs output with expected safety require substantial training efforts and expensive computational resources. |
| Approach: | They propose a method to directly boost the safety of existing instruction-tuned large language models without additional training. |
| Outcome: | The proposed method improves safety of instruction-tuned large language models without training and requires expensive computational resources. |
O1-Pruner: Length-Harmonizing Fine-Tuning for O1-Like Reasoning Pruning (2026.findings-acl)
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Haotian Luo, Haiying He, Yibo Wang, Shiwei Liu, Wei Li, Xiaochun Cao, Dacheng Tao, Naiqiang Tan, Li Shen
| Challenge: | Recent long-thought reasoning models adopt extended reasoning processes similar to how humans ponder over complex problems. |
| Approach: | They propose a model that uses RL-style fine-tuning to reduce inference overhead while maintaining accuracy. |
| Outcome: | The proposed model reduces inference overhead while maintaining accuracy. |
Breaking the Reasoning Barrier A Survey on LLM Complex Reasoning through the Lens of Self-Evolution (2025.findings-acl)
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Tao He, Hao Li, Jingchang Chen, Runxuan Liu, Yixin Cao, Lizi Liao, Zihao Zheng, Zheng Chu, Jiafeng Liang, Ming Liu, Bing Qin
| Challenge: | OpenAI's O1 and subsequent projects like DeepSeek R1 have significantly advanced research on complex reasoning in LLMs. |
| Approach: | They analyze existing reasoning studies from the perspective of self-evolution and summarize O1-like works from open-source projects like DeepSeek R1 and Kimi-k1.5. |
| Outcome: | The proposed models are based on open-source models and pioneer advanced methodologies like Scaling Reinforcement Learning (RL). |
SDAR-VL: Stable and Efficient Block-wise Diffusion for Vision-Language Understanding (2026.acl-long)
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| Challenge: | Existing block-wise discrete diffusion models lack robust autoregressive (AR) decoders. |
| Approach: | They propose a block-wise discrete diffusion framework for large-scale vision-language understanding with a progressive beta noise curriculum. |
| Outcome: | The proposed framework improves training efficiency, convergence stability, and task performance over conventional block diffusion. |
PAMN: Multi-phase Correlation Modeling for Contrast-Enhanced 3D Medical Image Retrieval (2025.findings-emnlp)
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| Challenge: | Current 3D medical imaging models focus on spatial features, neglecting phase-specific progression detailed in clinical reports. |
| Approach: | They propose a framework that fuses imaging phases with clinical text to enhance 3D medical image retrieval. |
| Outcome: | The proposed framework outperforms state-of-the-art models on a phase-series dataset of 12,230 hospital CT scans. |
How do Language Models Reshape Entity Alignment? A Survey of LM-Driven EA Methods: Advances, Benchmarks, and Future (2025.emnlp-main)
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| Challenge: | Entity alignment (EA) is critical for knowledge graph (KG) integration. |
| Approach: | They propose a taxonomy that categorizes methods in three stages: data preparation, feature embedding, and alignment. |
| Outcome: | The proposed taxonomy categorizes methods in three key stages: data preparation, feature embedding, and alignment. |
Machine Translation With Weakly Paired Documents (D19-1)
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| Challenge: | Recent studies explore the possibility of unsupervised machine translation with monolingual data only. |
| Approach: | They propose a method to mine bilingual sentences from weakly paired documents . they use word distribution-level alignments to constrain word distributions of two weakly-paired documents. |
| Outcome: | The proposed method outperforms previous results on six translation tasks using weakly paired bilingual documents and a large number of bilingual sentences. |
EvoCoT: Overcoming the Exploration Bottleneck in Reinforcement Learning for LLMs (2026.findings-acl)
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| Challenge: | Existing approaches to reinforcement learning with verifiable reward (RLVR) are limited by difficulty or lack of exploration. |
| Approach: | They propose a self-evolving curriculum learning framework based on chain-of-thought reasoning optimization that constrains exploration space by self-generating and verifying CoT trajectories. |
| Outcome: | The proposed framework enables LLMs to solve previously unsolved problems without external supervision and is compatible with various RL fine-tuning methods. |
Toward Optimal LLM Alignments Using Two-Player Games (2025.findings-emnlp)
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Rui Zheng, Hongyi Guo, Zhihan Liu, Xiaoying Zhang, Yuanshun Yao, Xiaojun Xu, Zhaoran Wang, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang, Yang Liu, Hang Li
| Challenge: | Alignment of large language models (LLM) is a process that ensures the model’s responses to user prompts align with human intentions and social values. |
| Approach: | They propose an alignment method based on a two-agent game consisting of an adversarial agent and a defensive agent. |
| Outcome: | The proposed method improves on a two-agent game with an adversarial agent and a defensive agent. |
ChatASU: Evoking LLM’s Reflexion to Truly Understand Aspect Sentiment in Dialogues (2024.lrec-main)
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| Challenge: | Existing studies on interactive ASU ignore the coreference issue for opinion targets while this phenomenon is ubiquitous in interactive scenarios especially dialogues, limiting the ASU performance. |
| Approach: | They propose a Chat-based Aspect Sentiment Understanding task that integrates various NLP tasks with the chat paradigm and propose 'trusted self-reflexion' approach with ChatGLM as backbone to address aspect coreference issue. |
| Outcome: | The proposed task outperforms state-of-the-art baselines and shows that it is highly effective. |
Revisiting Model Interpolation for Efficient Reasoning (2026.acl-long)
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| Challenge: | Existing models that interpolate weights of two specialized models can be abused for efficient reasoning. |
| Approach: | They propose to merge two specialized models and create a model that combines efficiency and efficiency. |
| Outcome: | The proposed method outperforms existing models on efficiency and effectiveness. |
MEIT: Multimodal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation (2025.findings-acl)
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Zhongwei Wan, Che Liu, Xin Wang, Chaofan Tao, Hui Shen, Jing Xiong, Rossella Arcucci, Huaxiu Yao, Mi Zhang
| Challenge: | Recent studies have focused on classifying cardiac conditions using ECG data but have overlooked ECG report generation, which is time-consuming and requires clinical expertise. |
| Approach: | They propose a Multimodal ECG Instruction Tuning framework that extends the capability of large language models (LLMs) for the task. |
| Outcome: | The proposed framework outperforms open-source LLMs and LLM backbones across two large-scale ECG datasets. |
Beyond Error Propagation in Neural Machine Translation: Characteristics of Language Also Matter (D18-1)
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| Challenge: | Neural machine translation suffers from exposure bias and error propagation problem. |
| Approach: | They conduct a series of analyses to deeply understand the accuracy drop problem . they find that the left part of the translated sentence is often better than its right part . |
| Outcome: | The results show that the left part of the translated sentence is often better than its right part in left-to-right decoding models. |
World Models with Hints of Large Language Models for Goal Achieving (2025.naacl-long)
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| Challenge: | Existing methods address this by adding intrinsic rewards, but they fail to provide meaningful guidance in long-horizon decision-making tasks with large state and action spaces lacking purposeful exploration. |
| Approach: | They propose a multi-modal model-based RL approach that integrates the proposed hinting subgoals into the model rollouts to encourage goal discovery and reaching in challenging tasks. |
| Outcome: | The proposed model outperforms existing methods in challenging, sparse-reward environments such as HomeGrid, Crafter, and Minecraft by 41.8%, 21.1%, and 9.9%. |
Parameter-efficient Continual Learning Framework in Industrial Real-time Text Classification System (2022.naacl-industry)
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Tao Zhu, Zhe Zhao, Weijie Liu, Jiachi Liu, Yiren Chen, Weiquan Mao, Haoyan Liu, Kunbo Ding, Yudong Li, Xuefeng Yang
| Challenge: | Existing continual learning methods use data replay, parameter isolation and regularization to mitigate catastrophic forgetting. |
| Approach: | They propose a parameter-efficient continual learning framework that updates parameters offline and then trains using an online regularization method. |
| Outcome: | The proposed framework reduces catastrophic forgetting and saves the model with the changed parameters instead of all parameters. |
Investigating and Mitigating Object Hallucinations in Pretrained Vision-Language (CLIP) Models (2024.emnlp-main)
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| Challenge: | Existing studies have revealed that Large Vision-Language Models suffer from hallucinations in practice, including object hallucines, spatial hallucinos, attribute hallucinications, etc. |
| Approach: | They propose to use CLIP model to mitigate object hallucinations by using a data augmentation method to create negative samples with a variety of hallucinian issues. |
| Outcome: | The proposed method mitigates object hallucinations and can be used as a visual encoder, effectively alleviating the object halluination issue in LVLMs. |
How to Make Large Language Models Generate 100% Valid Molecules? (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) can learn to perform a wide range of tasks, but generating valid molecules using representations like SMILES is challenging in few-shot settings. |
| Approach: | They propose a language framework that converts invalid SMILES to SELFIES and LLMs as post-hoc correctors to ensure that the molecules generated by LLM are 100% valid. |
| Outcome: | The proposed model performs worse with SELFIES than with SMILES and improves on other metrics. |
UER: An Open-Source Toolkit for Pre-training Models (D19-3)
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Zhe Zhao, Hui Chen, Jinbin Zhang, Xin Zhao, Tao Liu, Wei Lu, Xi Chen, Haotang Deng, Qi Ju, Xiaoyong Du
| Challenge: | Existing work on pre-training models have shown that it is important to use a framework to deploy various pre- training models efficiently. |
| Approach: | They propose an assemble-on-demand pre-training toolkit that assembles pre-trained models on demand and encapsulates them with rich modules. |
| Outcome: | The proposed framework can reproduce state-of-the-art models or develop models that remain unexplored. |
PromptST: Abstract Prompt Learning for End-to-End Speech Translation (2023.emnlp-main)
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| Challenge: | Experimental results show that PromptST can improve speech-to-text translation by capturing richer linguistic knowledge. |
| Approach: | They propose a plug-in prompt-enhanced S2T model that captures richer linguistic knowledge . they use a 10GB linguistic probing benchmark to investigate the fusion of speech and text features . |
| Outcome: | The proposed model can improve on a strong baseline by capturing richer linguistic knowledge. |
Concept-Based Label Embedding via Dynamic Routing for Hierarchical Text Classification (2021.acl-long)
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| Challenge: | Existing methods for hierarchical text classification focus on modeling the text, but the concept of sharing among classes has been ignored in previous work. |
| Approach: | They propose a concept-based method that explicitly represents the concept and model the sharing mechanism among classes for the hierarchical text classification. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two widely used datasets. |
QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization (2021.naacl-main)
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Ming Zhong, Da Yin, Tao Yu, Ahmad Zaidi, Mutethia Mutuma, Rahul Jha, Ahmed Hassan Awadallah, Asli Celikyilmaz, Yang Liu, Xipeng Qiu, Dragomir Radev
| Challenge: | Existing work on meeting summarization tasks is limited to short summaries that cover all the content of a meeting. |
| Approach: | They propose a query-based multi-domain meeting summarization task that generates a single short summary of meetings based on a transcript. |
| Outcome: | The proposed task is based on 1,808 query-summary pairs over 232 meetings in multiple domains. |
DPDV: Dual-Pathway and Dual-View Representation Learning for Bridging Information Asymmetry in Text-Video Retrieval (2026.acl-long)
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| Challenge: | Existing methods for text-based person anomaly search fail to address the pose-semantic gap . asymmetric cross-modal information poses a challenge to accurately establishing retrieval relationships . |
| Approach: | They propose a video retrieval framework that partitions visual features into two categories based on relevance to the text query and performs effective interaction. |
| Outcome: | The proposed framework achieves leading retrieval performance on five benchmark datasets. |
MM-Doc-R1: Training Agents for Long Document Visual Question Answering through Multi-turn Reinforcement Learning (2026.findings-acl)
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Jiahang Lin, Kai Hu, Binghai Wang, Yuhao Zhou, Zhiheng Xi, Honglin Guo, Shichun Liu, Junzhe Wang, Shihan Dou, Enyu Zhou, Hang Yan, Zhenhua Han, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing work on long document visual question answering is based on Retrieval-Augmented Generation (RAG) where textual or visual content is encoded into embeddings and relevance is determined by similarity scores with respect to the original query. |
| Approach: | They propose a framework that employs an agentic, vision-aware workflow to address long document visual question answering through iterative information discovery and synthesis. |
| Outcome: | The proposed framework outperforms existing RL systems by 10.4% on the MMLongbench-Doc benchmark and demonstrates superior training performance over GRPO. |
Expedited Training of Visual Conditioned Language Generation via Redundancy Reduction (2024.acl-long)
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| Challenge: | EVLGen is a framework for visual-language pre-training with high computational demands. |
| Approach: | They propose a streamlined framework for the pre-training of visually conditioned language generation models with high computational demands. |
| Outcome: | The proposed framework accelerates training of vision-language models by a factor of 5 without compromising performance. |
CEAN: Contrastive Event Aggregation Network with LLM-based Augmentation for Event Extraction (2024.eacl-long)
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| Challenge: | Event Extraction is a crucial yet arduous task in natural language processing (NLP), as its performance is hindered by laborious data annotation. |
| Approach: | They propose a Contrastive Event Aggregation Network with LLM-based Augmentation to promote low-resource learning and reduce data noise for event extraction. |
| Outcome: | The proposed approach achieves new state-of-the-art results on the ACE2005 and ERE-EN datasets. |
LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation (2025.findings-emnlp)
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Ming Zhang, Yujiong Shen, Zelin Li, Huayu Sha, Binze Hu, Yuhui Wang, Chenhao Huang, Shichun Liu, Jingqi Tong, Changhao Jiang, Mingxu Chai, Zhiheng Xi, Shihan Dou, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Current medical benchmarks have limitations in question design, data sources and evaluation methods. |
| Approach: | They propose a new benchmark covering five core medical areas . it includes 2,996 questions created from real-world electronic health records . |
| Outcome: | The proposed model covers five core medical areas and includes 2,996 questions created from real-world electronic health records and expert-designed clinical scenarios. |
Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translation (2021.acl-long)
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| Challenge: | Knowledge distillation (KD) is commonly used to construct synthetic data for training non-autoregressive translation models. |
| Approach: | They propose to use knowledge distillation to generate training data for non-autoregressive translation models by leveraging pretraining. |
| Outcome: | The proposed approach achieves 28.2 and 33.9 BLEU points on the WMT14 English-German and WMT16 Romanian-English datasets. |
UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction (2021.findings-acl)
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| Challenge: | Mainstream methods that ignore the diversity among keyphrases or weakly capture the relation between tasks implicitly ignore keyphrase diversity. |
| Approach: | They propose a novel end-to-end learning framework that jointly learns to extract and generate keyphrases by exploiting latent semantic relation between extraction and generation. |
| Outcome: | The proposed approach outperforms mainstream methods on a benchmarked document on keyphrase prediction. |
MADNet: Maximizing Addressee Deduction Expectation for Multi-Party Conversation Generation (2023.emnlp-main)
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| Challenge: | Existing methods for multi-party conversations rely on addressee labels and can only be applied to an ideal setting where addresses are missing. |
| Approach: | They propose a method that maximizes addressee deduction expectation in heterogeneous graph neural networks for MPC generation. |
| Outcome: | The proposed method outperforms baseline models on Ubuntu IRC channel benchmarks on the task of MPC generation under a common and challenging setting where addressee labels are missing. |
UniCoder: Scaling Code Large Language Model via Universal Code (2024.acl-long)
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Tao Sun, Linzheng Chai, Jian Yang, Yuwei Yin, Hongcheng Guo, Jiaheng Liu, Bing Wang, Liqun Yang, Zhoujun Li
| Challenge: | Experimental results show that UniCoder with the universal code significantly outperforms the previous prompting methods by a large margin. |
| Approach: | They introduce the universal code (UniCode) as the intermediate representation of algorithm steps using conventions of programming languages. |
| Outcome: | The proposed model outperforms previous prompting methods by a large margin . the proposed model is based on a dataset of natural-language questions and code solutions . |
Inference and Verbalization Functions During In-Context Learning (2024.findings-emnlp)
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| Challenge: | Previous work has found that, in some settings, ICL performance is minimally affected by using demonstrations with irrelevant label words. |
| Approach: | They hypothesize that large language models (LMs) perform in-context learning from a handful of demonstrations via two sequential processes: an inference function that solves the task and a verbalization function that maps the inferred answer to the label space. |
| Outcome: | The proposed model can be localized in specific layers across open-source models, including GEMMA-7B, MISTRAL-7B-V0.3, GEIMA-2-27B, and LLAMA-3.1-70B. |
Hint-Based Training for Non-Autoregressive Machine Translation (D19-1)
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| Challenge: | AutoRegressive Translation models have to generate tokens sequentially during decoding and thus suffer from high inference latency. |
| Approach: | They propose to use hidden states and word alignments to help train NART models. |
| Outcome: | The proposed model improves on the WMT14 En-De and De-En datasets but is faster in inference than the current models. |
A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of Labeling (2022.emnlp-main)
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| Challenge: | Existing document-level relation extraction methods focus on fully supervised scenarios but in real-world, incomplete labeling is a common problem because the number of entity pairs grows quadratically with the number. |
| Approach: | They propose a positive-unlabeled learning framework for document-level relation extraction (RE) that uses shift and squared ranking loss positive- unlabeles (SSR-PU) learning to solve incomplete labeling problem. |
| Outcome: | The proposed framework outperforms state-of-the-art methods under fully supervised and extremely unlabeled conditions and achieves 14 F1 points over the baseline with incomplete labeling. |
APo-VAE: Text Generation in Hyperbolic Space (2021.naacl-main)
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| Challenge: | Existing models that learn embeddings only in Euclidean vector space do not account for such structural property of language. |
| Approach: | They propose a Poincare Variational Autoencoder to capture latent hierarchies in hyperbolic space . they propose enabling adversarial learning procedures to empower robust model training . |
| Outcome: | The proposed model outperforms existing models in a hyperbolic latent space . it captures latent language hierarchies in hyperbolical space and is robust to training . |
Robust Membership Inference for Large Language Models under Adversarial Generative Corruption (2026.acl-long)
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Yuanhong Huang, Huili Wang, Xueying Bai, Jinrui Wang, Jiajun Liu, Ziqin Wang, Wanchun Ni, Shangguang Wang, Tao Qi
| Challenge: | Membership inference attacks are a promising tool for auditing training data of LLMs . existing methods rely on the assumption that LLM's assign higher confidence scores to training samples than to non-training ones. |
| Approach: | They propose a membership inference framework that can be robust against adversarial MIAs. |
| Outcome: | The proposed framework can be robust against adversarial MIA methods and AIGT detectors while maintaining the performance of baselines. |
Uni-Dubbing: Zero-Shot Speech Synthesis from Visual Articulation (2024.acl-long)
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Songju Lei, Xize Cheng, Mengjiao Lyu, Jianqiao Hu, Jintao Tan, Runlin Liu, Lingyu Xiong, Tao Jin, Xiandong Li, Zhou Zhao
| Challenge: | Multimodal speech synthesis is a key challenge due to the scarcity of datasets that pair audio with corresponding video. |
| Approach: | They propose a method that incorporates modality alignment during the pre-training phase on multimodal datasets and freezes the video modality extraction component and the encoder module within the pretrained weights. |
| Outcome: | The proposed method achieves a reduced word error rate (WER) of 31.73%, surpassing the previous best of 33.9% with single-modality audio. |
Analogical Reasoning on Chinese Morphological and Semantic Relations (P18-2)
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| Challenge: | Analogical reasoning is effective in capturing linguistic regularities. |
| Approach: | They propose to use Chinese lexical knowledge to build an analogical reasoning task using a large dataset. |
| Outcome: | The proposed dataset proves to be reliable benchmark for evaluating Chinese word embeddings. |
3AM: An Ambiguity-Aware Multi-Modal Machine Translation Dataset (2024.lrec-main)
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Xinyu Ma, Xuebo Liu, Derek F. Wong, Jun Rao, Bei Li, Liang Ding, Lidia S. Chao, Dacheng Tao, Min Zhang
| Challenge: | Existing studies have shown that visual information in existing MMT datasets is insufficient, causing models to disregard it and overestimate their capabilities. |
| Approach: | They propose to use 3AM to create an ambiguity-aware multimodal machine translation dataset. |
| Outcome: | The proposed dataset includes more ambiguity and a greater variety of captions and images than other MMT datasets. |
ProFit: Leveraging High-Value Signals in SFT via Probability-Guided Token Selection (2026.findings-acl)
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| Challenge: | Traditional fine-tuning ignores one-to-many nature of language, leading to overfitting . authors propose a method to fine- tune LLMs by leveraging tokens. |
| Approach: | They propose a method to fine-tune Large Language Models by leveraging tokens to mask low-probability tokens. |
| Outcome: | The proposed method outperforms baselines on general reasoning and mathematical benchmarks. |
DVAGen: Dynamic Vocabulary Augmented Generation (2025.emnlp-demos)
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| Challenge: | Existing dynamic vocabulary approaches struggle to generalize to novel or out-of-vocabulary words, limiting their flexibility in handling diverse token combinations. |
| Approach: | They propose an open-source framework for training, evaluation, and visualization of dynamic vocabulary-augmented language models. |
| Outcome: | The proposed framework validates the effectiveness of dynamic vocabulary-augmented language models on modern LLMs and shows support for batch inference significantly improving inference throughput. |
Taming Text-to-Image Synthesis for Novices: User-centric Prompt Generation via Multi-turn Guidance (2025.emnlp-main)
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Yilun Liu, Minggui He, Feiyu Yao, Yuhe Ji, Shimin Tao, Jingzhou Du, Justin Li, Jian Gao, Zhang Li, Hao Yang, Boxing Chen, Osamu Yoshie
| Challenge: | Existing solutions for text-to-image synthesis are sensitive on textual prompts, posing a challenge for novice users. |
| Approach: | They propose a dialogue-based TIS prompt generation model that emphasizes user experience for novice users. |
| Outcome: | The proposed model emphasizes user experience for novice users . it improves user-centricity score while maintaining a competitive quality of synthesized images. |
Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation (2025.findings-acl)
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Qiyue Gao, Xinyu Pi, Kevin Liu, Junrong Chen, Ruolan Yang, Xinqi Huang, Xinyu Fang, Lu Sun, Gautham Kishore, Bo Ai, Stone Tao, Mengyang Liu, Jiaxi Yang, Chao-Jung Lai, Chuanyang Jin, Jiannan Xiang, Benhao Huang, Zeming Chen, David Danks, Hao Su, Tianmin Shu, Ziqiao Ma, Lianhui Qin, Zhiting Hu
| Challenge: | Recent studies have evaluated and shown limitations in specific capabilities such as visual understanding, but a systematic evaluation of VLMs’ fundamental WM abilities remains absent. |
| Approach: | They propose a framework that assesses perception and prediction to provide an atomic evaluation of VLMs as WMs. |
| Outcome: | The proposed framework assesses perception and prediction abilities on 15 latest VLMs and compares them to human-level models. |
Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding (2024.findings-acl)
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Heming Xia, Zhe Yang, Qingxiu Dong, Peiyi Wang, Yongqi Li, Tao Ge, Tianyu Liu, Wenjie Li, Zhifang Sui
| Challenge: | Large Language Models (LLMs) have a high inference latency stemming from autoregressive decoding. |
| Approach: | They propose a novel decoding paradigm that drafts multiple tokens and verifies them in parallel . they aim to provide a catalyst for further research on Speculative Decoding . |
| Outcome: | The proposed method drafts multiple tokens and verifies them in parallel . it can be used to accelerate inference in large language models. |
Prompt-Singer: Controllable Singing-Voice-Synthesis with Natural Language Prompt (2024.naacl-long)
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Yongqi Wang, Ruofan Hu, Rongjie Huang, Zhiqing Hong, Ruiqi Li, Wenrui Liu, Fuming You, Tao Jin, Zhou Zhao
| Challenge: | Recent singing-voice-synthesis methods lack ability to control style attributes of synthesized singing. |
| Approach: | They propose a singing-voice-synthesis method that enables attribute controlling on singer gender, vocal range and volume with natural language. |
| Outcome: | The proposed method achieves favorable control ability and audio quality. |
Speech Sense Disambiguation: Tackling Homophone Ambiguity in End-to-End Speech Translation (2024.acl-long)
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| Challenge: | End-to-end speech translation (ST) models require simultaneous crossmodal and crosslingual transformations to be effective. |
| Approach: | They propose a homophone-aware contrastive learning approach that integrates a speech-text masking strategy to reduce ambiguity. |
| Outcome: | The proposed approach achieves SOTA results on BLEU scores on different MuST-C and CoVoST ST tasks, underlining its effectiveness in reducing speech sense ambiguity. |
Supervised Optimism Correction: Be Confident When LLMs Are Sure (2025.findings-acl)
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| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated remarkable success across diverse tasks such as instruction following, code generation, and medical diagnosis. |
| Approach: | They propose a supervised fine-tuning-based auxiliary loss for Q-value estimations during supervised refinement. |
| Outcome: | The proposed method outperforms beam search on GSM8K, MATH, and GAOKAO on reasoning benchmarks. |
MIE: A Medical Information Extractor towards Medical Dialogues (2020.acl-main)
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| Challenge: | EMRs are important but many doctors suffer from writing them, which is time-consuming and tedious. |
| Approach: | They propose an automatic conversion of medical dialogues to EMRs using a window-sliding style . they propose a medical information extractor (MIE) that extracts medical information from medical dialogue . |
| Outcome: | The proposed model extracts medical information from doctor-patient dialogues. |
"Excuse me, may I say something..." CoLabScience, A Proactive AI Assistant for Biomedical Discovery and LLM-Expert Collaborations (2026.acl-long)
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| Challenge: | Existing large language models (LLMs) are reactive and respond only when prompted, limiting their effectiveness in collaborative settings. |
| Approach: | They introduce a proactive LLM assistant designed to enhance biomedical collaboration between AI systems and human experts through timely, context-aware interventions. |
| Outcome: | The proposed model outperforms baselines in intervention precision and collaborative task utility, highlighting the potential of proactive LLMs as intelligent scientific assistants. |
Revisiting the Reliability of Language Models in Instruction-Following (2026.acl-long)
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| Challenge: | Several benchmarks have been proposed to measure instruction-following accuracy, but these scores do not translate to reliable services in real-world use. |
| Approach: | They propose a new metric reliable@k and develop an automated pipeline to generate cousin prompts. |
| Outcome: | The proposed model can be instantiated with cousin prompts and generates high-quality cousin prompt data. |
Double Path Networks for Sequence to Sequence Learning (C18-1)
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| Challenge: | Existing approaches for Sequence to Sequence learning have been developed . convolutional neural networks and self-attention networks are the most popular . |
| Approach: | They propose to integrate convolutional and self-attention layers into a double path network for sequence to sequence learning. |
| Outcome: | The proposed method significantly improves performance over state-of-the-art systems. |
Lost in the Context: Insufficient and Distracted Attention to Contexts in Preference Modeling (2025.acl-long)
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Shihan Dou, Jiayi Chen, Chenhao Huang, Feng Chen, Wei Chengzhi, Huiyuan Zheng, Shichun Liu, Yan Liu, Chenxiao Liu, Chao Xin, Lin Yan, Zongzhang Zhang, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing reward models concatenate contexts and responses, but they often ignore crucial segments of the context that are important for evaluating the response quality. |
| Approach: | They propose a reward model that evaluates the response quality based on a given context and assigns a rewards reward. |
| Outcome: | The proposed framework significantly improves preference modeling by increasing attention to relevant information within the context and achieves better generalizability. |
Exploiting Monolingual Data at Scale for Neural Machine Translation (D19-1)
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| Challenge: | Neural machine translation (NMT) is a well-known and expensive task. |
| Approach: | They propose a method to use target-side monolingual data for neural machine translation and propose 'synthetic bitext' they propose generating synthetic bitext by translating monolingual into the other domain using models pretrained on genuine bitext. |
| Outcome: | The proposed approach achieves state-of-the-art results on WMT16, WMT17, WTM18 EnglishGerman translations and WTM19 GermanFrench translations. |
Event Detection without Triggers (N19-1)
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| Challenge: | Existing approaches to event detection require annotated triggers and event types in training data. |
| Approach: | They propose a framework that encodes the representation of a sentence based on target event types. |
| Outcome: | The proposed framework achieves competitive performances compared with state-of-the-art methods. |
LAD-RAG: Layout-aware Dynamic RAG for Visually-Rich Document Understanding (2026.acl-long)
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Zhivar Sourati, Zheng Wang, Marianne Menglin Liu, Yazhe Hu, Mengqing Guo, Sujeeth Bharadwaj, Kyu J. Han, Tao Sheng, Sujith Ravi, Morteza Dehghani, Dan Roth
| Challenge: | Conventional retrieval-augmented generation (RAG) methods encode content in isolated chunks during ingestion, losing structural and cross-page dependencies, and retrieve a fixed number of pages at inference. |
| Approach: | They propose a Layout-Aware Dynamic RAG framework that encodes content in isolated chunks during ingestion and retrieves a fixed number of pages at inference. |
| Outcome: | Experiments on MMLongBench-Doc, LongDocURL, DUDE, and MP-DoxVQA show that LAD-RAG improves retrieval, achieving over 90% perfect recall on average without any top-k tuning, and outperforming baseline retrievers by up to 20% in recall at comparable noise levels. |
Parameter-Efficient Prompt Tuning Makes Generalized and Calibrated Neural Text Retrievers (2023.findings-emnlp)
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| Challenge: | Prompt tuning is a technique that updates few parameters in pre-trained models for language understanding and generation tasks. |
| Approach: | They propose to leverage prompt tuning for neural text retrieval to improve generalization and cross-domain generalization. |
| Outcome: | The proposed approach can mitigate the two issues faced by fine-tuning retrieval methods and improve the out-of-domain zero-shot generalization of the retrieval models. |
Self-Demos: Eliciting Out-of-Demonstration Generalizability in Large Language Models (2024.findings-naacl)
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| Challenge: | Existing methods that rely on limited demos and out-of-demonstration (OOD) queries fail when faced with out- of-demotion queries. |
| Approach: | They propose a query-aware prompting method that elicits the inherent generalizability of large language models by query-based demo generation. |
| Outcome: | The proposed method outperforms state-of-the-art methods in the OOD setting and two public math benchmarks. |
ELBA-Bench: An Efficient Learning Backdoor Attacks Benchmark for Large Language Models (2025.acl-long)
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| Challenge: | Existing backdoor models are limited in coverage of attack, system integrity and backdoor alignment . ELBA-Bench provides over 1300 experiments encompassing 12 attack methods, 18 datasets, and 12 LLMs. |
| Approach: | They propose a framework that allows attackers to inject backdoor through parameter efficient fine-tuning or without fine-uning techniques. |
| Outcome: | ELBA-Bench provides over 1300 experiments encompassing 12 attack methods, 18 datasets, and 12 LLMs. |
A Study of Syntactic Multi-Modality in Non-Autoregressive Machine Translation (2022.naacl-main)
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| Challenge: | Non-autoregressive translation models suffer from the multi-modality problem when a source sentence corresponds to multiple correct translations. |
| Approach: | They propose to decompose the syntactic multi-modality problem into short- and long-range models and evaluate them on synthesized and real datasets. |
| Outcome: | The proposed loss functions can handle short- and long-range syntactic multi-modalities better than existing models. |
LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit (2024.emnlp-industry)
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Ruihao Gong, Yang Yong, Shiqiao Gu, Yushi Huang, Chengtao Lv, Yunchen Zhang, Dacheng Tao, Xianglong Liu
| Challenge: | Existing quantization techniques have been categorized as 'simple' and 'highly efficient' however, their configurations vary from each other and cannot be fairly compared . |
| Approach: | They propose a plug-and-play compression toolkit to explore the impact of quantization. |
| Outcome: | The proposed toolkit explores the impact of quantization on large language models. |
UniDrop: A Simple yet Effective Technique to Improve Transformer without Extra Cost (2021.naacl-main)
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| Challenge: | Existing approaches to improve the performance of natural language processing models are over-parameterized and overfitted. |
| Approach: | They propose an approach to integrate dropout techniques into the training of Transformer models. |
| Outcome: | The proposed approach can achieve 1.5 BLEU improvement on IWSLT14 translation tasks and better accuracy for the classification even using strong pre-trained RoBERTa as backbone. |
ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development (2026.findings-acl)
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Jie Yang, Honglin Guo, Li Ji, Jiazheng Zhou, Rui Zheng, Zhikai Lei, Shuo Zhang, Zhiheng Xi, Shichun Liu, Yuxin Wang, Bo Wang, Yining Zheng, Tao Gui, Xipeng Qiu
| Challenge: | Large Language Models (LLMs) have redefined the role of AI in software engineering . current benchmarks focus on localized code generation, but neglect dynamic, full-process requirements of real-world engineering. |
| Approach: | They propose a benchmark to evaluate agentic backend coding within a realistic, executable workflow. |
| Outcome: | The ABC-Bench benchmark evaluates agentic backend coding within a realistic, executable workflow. |
Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future (2024.acl-long)
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Zheng Chu, Jingchang Chen, Qianglong Chen, Weijiang Yu, Tao He, Haotian Wang, Weihua Peng, Ming Liu, Bing Qin, Ting Liu
| Challenge: | Recent studies have revealed that chain-of-thought prompting significantly enhances LLM’s reasoning capabilities, which attracts widespread attention from both academics and industry. |
| Approach: | They propose to summarize advanced methods through a taxonomy that offers novel perspectives. |
| Outcome: | The proposed method delineates the challenges and future directions, thereby shedding light on future research. |
StepCoder: Improving Code Generation with Reinforcement Learning from Compiler Feedback (2024.acl-long)
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Shihan Dou, Yan Liu, Haoxiang Jia, Enyu Zhou, Limao Xiong, Junjie Shan, Caishuang Huang, Xiao Wang, Xiaoran Fan, Zhiheng Xi, Yuhao Zhou, Tao Ji, Rui Zheng, Qi Zhang, Tao Gui, Xuanjing Huang
| Challenge: | Existing work integrates reinforcement learning with compiler feedback to enhance code generation quality but the long code generated by LLMs makes RL exploration ineffective. |
| Approach: | They propose a framework that integrates reinforcement learning and compiler feedback to enhance code generation quality. |
| Outcome: | The proposed framework outperforms state-of-the-art approaches in corresponding benchmarks and integrates reinforcement learning with compiler feedback to improve code generation quality. |
MTO: A Multi-turn Conversational Text-to-OverpassQL Dataset for Enhanced OpenStreetMap Query Generation (2026.findings-acl)
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| Challenge: | a framework for constructing multi-turn Text-to-OverpassQL dialogue datasets is proposed . a dataset of over 7,800 dialogues contains more than 20,000 individual utterances . |
| Approach: | They propose a framework for constructing multi-turn Text-to-OverpassQL dialogue datasets . they convert Overpass queries into syntax trees using a custom parser based on OverpassQl . |
| Outcome: | The proposed dataset includes over 7,800 dialogues, each containing 2 to 4 user utterances . it is the first multi-turn Text-to-OverpassQL dataset built upon the OverpassNL corpus . |
Learning from Imperfect Data: Towards Efficient Knowledge Distillation of Autoregressive Language Models for Text-to-SQL (2024.findings-emnlp)
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| Challenge: | Existing text-to-SQL LLMs are computationally expensive and difficult to deploy in real-world applications. |
| Approach: | They propose to distill a larger teacher model into a smaller student model by using imperfect data to improve the KD. |
| Outcome: | The proposed method achieves the best tradeoff between performance and efficiency on 5 text-to-SQL benchmarks. |
Runaway is Ashamed, But Helpful: On the Early-Exit Behavior of Large Language Model-based Agents in Embodied Environments (2025.findings-emnlp)
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| Challenge: | Experiments with 4 different LLMs across 5 embodied environments show significant efficiency improvements, with only minor drops in agent performance. |
| Approach: | They propose an intrinsic method that injects exit instructions during generation and an extransic system that verifies task completion to determine when to halt an agent’s trial. |
| Outcome: | The proposed method injects exit instructions during generation and an exit method verifies task completion to determine when to halt an agent’s trial. |
Trust Within? Seek Beyond? Knowledge Boundary Aware Policy Optimization for Agentic Search (2026.acl-long)
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Tao Feng, Xinke Jiang, Xinyan Hu, Yonggang Zhang, Zhen Tao, Wentao Zhang, Boyang Liu, Wenhao Jiang, Chao Wu
| Challenge: | Existing approaches to augment large language models with external knowledge suffer from a lack of calibration regarding the model’s knowledge boundary. |
| Approach: | They propose a reinforcement learning framework that explicitly aligns retrieval decisions with quantified knowledge states. |
| Outcome: | The proposed framework outperforms strong baselines while exhibiting reduced hallucination rates. |
WebCoderBench: Benchmarking Web Application Generation with Comprehensive and Interpretable Evaluation Metrics (2026.acl-long)
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| Challenge: | Web applications (web apps) are a key arena for large language models to demonstrate their code generation capabilities and commercial potential. |
| Approach: | a new benchmark for large language models (LLMs) is designed to provide real-world user requirements and generalizable evaluation metrics. |
| Outcome: | a new benchmark for large language models (LLMs) provides a real-world, generalizable, and interpretable evaluation score . the benchmark measures user requirements, expression styles and human-preference-aligned weights . a web application can be used to demonstrate its commercial potential, authors say . |
MIND: A Large-scale Dataset for News Recommendation (2020.acl-main)
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Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, Ming Zhou
| Challenge: | Personalized news recommendation is an important technique for personalized news service. |
| Approach: | They propose to build a large-scale news recommendation dataset from Microsoft News . they demonstrate that news recommendation relies on the quality of news content understanding . |
| Outcome: | The proposed dataset contains 1 million users and more than 160k English news articles, each of which has rich textual content such as title, abstract and body. |
Multilingual Neural Machine Translation with Language Clustering (D19-1)
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| Challenge: | Existing work on multilingual neural machine translation has been neglected due to its burdensome training process. |
| Approach: | They develop a framework that clusters languages into different groups and trains one multilingual model for each cluster. |
| Outcome: | The proposed model reduces the cost of training and improves translation accuracy. |
A Novel Framework Based on Medical Concept Driven Attention for Explainable Medical Code Prediction via External Knowledge (2022.findings-acl)
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| Challenge: | Existing methods to predict medical codes from clinical notes lack interpretability due to lengthy and noisy clinical notes. |
| Approach: | They propose a framework based on medical concept driven attention to integrate external knowledge for explainable medical code prediction from clinical notes. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on a benchmark dataset showing that it is more accurate than existing methods. |
SemRegex: A Semantics-Based Approach for Generating Regular Expressions from Natural Language Specifications (D18-1)
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| Challenge: | Existing approaches to generate programs from natural language do not address program aliasing . semantically equivalent programs may have many syntactically different forms . |
| Approach: | They propose a semantics-based approach to generate regular expressions from natural language. |
| Outcome: | The proposed approach improves on three public datasets. |
Soft Contextual Data Augmentation for Neural Machine Translation (P19-1)
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| Challenge: | Existing methods for enhancing training data are limited in natural language tasks due to text characteristics. |
| Approach: | They propose a data augmentation method that softly augments a randomly chosen word in a sentence by its contextual mixture of multiple related words. |
| Outcome: | The proposed method outperforms baseline methods on small and large scale machine translation datasets. |
The GaoYao Benchmark: A Comprehensive Framework for Evaluating Multilingual and Multicultural Abilities of Large Language Models (2026.acl-long)
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Yilun Liu, Chunguang Zhao, Mengyao Piao, Lingqi Miao, Shimin Tao, Minggui HE, Chenxin Liu, Zhang Li, null Mahongxia, Jiaxin Guo, Chen Liu, Liqun Deng, Jiansheng Wei, Xiaojun Meng, Fanyi Du, Daimeng Wei, Yanghua Xiao
| Challenge: | Existing multilingual evaluation benchmarks neglect cultural nuances and lack language coverage in subjective tasks. |
| Approach: | They propose a framework that categorizes evaluation tasks into three cultural layers and nine cognitive sub-layers. |
| Outcome: | The proposed framework surpasses prior coverage by up to 111% on 20+ LLMs. |
Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents (2022.findings-acl)
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| Challenge: | Existing text semantic matching models do not provide granularity for text comparison. |
| Approach: | They propose a simple yet effective training strategy for text semantic matching by disentangling keywords from intents. |
| Outcome: | The proposed approach achieves stable performance improvements against a wide range of models on three benchmarks. |
MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training (2021.findings-acl)
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| Challenge: | Symbolic music understanding is useful for many music applications, but lack of training data hinders representation learning. |
| Approach: | They propose a pre-trained model for music understanding that uses symbolic music data to train music representations. |
| Outcome: | The proposed model improves on four music understanding tasks. |
ImgTrojan: Jailbreaking Vision-Language Models with ONE Image (2025.naacl-long)
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| Challenge: | Existing studies on the safety of large language models (LLMs) with human values have focused on the integration of multi-modal user input into these models. |
| Approach: | They propose a method to bypass safety constraints of large language models by using poisoned images instead of original textual captions. |
| Outcome: | The proposed attack bypasses safety constraints of large language models (VLMs) by replacing the original textual captions with malicious jailbreak prompts. |
Do Image–Text Metrics Respect Semantic Invariances? (2026.findings-acl)
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Amit Agarwal, Hitesh Laxmichand Patel, Meizhu Liu, Jyotika Singh, Karan Dua, Hansa Meghwani, Matthew Rowe, M. Avendi, Yassi Abbasi, Tao Sheng, Sujith Ravi, Dan Roth
| Challenge: | Reference-free image–to–text evaluators are now standard for scoring image–caption alignment, yet it is unclear whether they respect semantic invariances. |
| Approach: | They propose an invariance probe on five popular evaluators under semantics-preserving perturbations along three axes: spatial edits, object changes, and socio-linguistic framing. |
| Outcome: | The proposed invariance probe shows that spatial edits and simple phrasing changes shift scores by ()6% on average and cause ranking flips in up to (),37% of cases. |
Self-Evolution Learning for Discriminative Language Model Pretraining (2023.findings-acl)
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| Challenge: | Random masking does not consider the importance of the different words in the sentence meaning, e.g., entity-level masking requires expensive prior knowledge and generally does not use existing model weights. |
| Approach: | They propose a token masking and learning method that uses a random masking strategy to learn the under-explored tokens. |
| Outcome: | The proposed method improves linguistic knowledge learning and generalization on 10 tasks. |
CFinBench: A Comprehensive Chinese Financial Benchmark for Large Language Models (2025.naacl-long)
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Ying Nie, Binwei Yan, Tianyu Guo, Hao Liu, Haoyu Wang, Wei He, Binfan Zheng, Weihao Wang, Qiang Li, Weijian Sun, Yunhe Wang, Dacheng Tao
| Challenge: | Large language models (LLMs) have achieved remarkable performance on various NLP tasks, yet their potential in more challenging task like finance, has not been fully explored. |
| Approach: | They propose a benchmark to assess the financial knowledge of large language models (LLMs) in China. |
| Outcome: | The proposed benchmark is the most comprehensive evaluation benchmark to date for LLMs in finance. |
Prompt Refinement with Image Pivot for Text-to-Image Generation (2024.acl-long)
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| Challenge: | Recent advances in text-to-image generation have markedly expanded the boundaries of digital artistry, enabling the creation of visually compelling images with unprecedented ease. |
| Approach: | They propose to decompose the prompt refinement process into two tasks: inferring user-preferred images from user languages and translating them into system languages. |
| Outcome: | Experiments show that PRIP outperforms baselines and transfers to unseen systems in a zero-shot manner. |
Descriptive Prompt Paraphrasing for Target-Oriented Multimodal Sentiment Classification (2023.findings-emnlp)
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| Challenge: | Current researches mainly work on either of two types of targets in a decentralized manner. |
| Approach: | They propose a model to perform sentiment polarity on a target jointly considering its corresponding multiple modalities including text, image, and others. |
| Outcome: | The proposed model performs well on four datasets spanning the above two target types and is prompt-based language modelling. |
CAIR: Causal Adaptive Information-based Reinforcement Learning for Multimodal Emotion Reasoning (2026.findings-acl)
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| Challenge: | Existing methods for multimodal emotion reasoning produce fluent but superficial explanations that lack authentic logical derivation. |
| Approach: | They propose a framework that treats rationales as causal mediators between raw perceptual signals and emotional semantics and an adaptive optimization mechanism to balance perception and reasoning across varying cognitive loads. |
| Outcome: | The proposed framework outperforms specialized SFT models by 14.4% while enhancing rationale faithfulness. |
Improving Sharpness-Aware Minimization with Fisher Mask for Better Generalization on Language Models (2022.findings-emnlp)
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| Challenge: | Existing methods for fine-tuning pretrained language models suffer from poor generalization . however, they add a perturbation to each model parameter equally, which is sub-optimal . |
| Approach: | They propose a sharpness-aware minimization optimization procedure that introduces a Fisher mask to improve the efficiency of SAM. |
| Outcome: | The proposed method outperforms the vanilla sharpness-aware minimization method on GLUE and SuperGLUE benchmarks. |
Towards Making the Most of ChatGPT for Machine Translation (2023.findings-emnlp)
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| Challenge: | Prior studies have shown that ChatGPT achieves comparable results to commercial systems for high-resource languages, but lags behind in complex tasks, e.g., low-resourced and distant-language-pairs translation. |
| Approach: | They propose task-specific prompts and domain-specific prompts which are based on task information and domain information and a task-specific prompt. |
| Outcome: | The proposed prompts improve the performance of ChatGPT in complex tasks and generate hallucinations for non-English-centric tasks. |
ProphetChat: Enhancing Dialogue Generation with Simulation of Future Conversation (2022.acl-long)
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| Challenge: | generative dialogue models use dialogue histories to generate the response . however, generating a response based on the historical information is not easy . |
| Approach: | They propose a framework that utilizes simulated dialogue futures to enhance response generation. |
| Outcome: | The proposed framework can generate better responses over strong baselines on two open-domain dialogue datasets. |
When Agents Look the Same: Quantifying Distillation-Induced Similarity in Tool-Use Behaviors (2026.acl-long)
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| Challenge: | Existing metrics fail to distinguish mandatory behaviors required for task success from non-mandatory patterns that reflect a model’s autonomous preferences. |
| Approach: | They propose to use response pattern similarity and action graph similarity to isolate non-mandatory behaviors from mandatory behaviors. |
| Outcome: | Evaluating 18 models from 8 providers on -Bench and 2-Bench against Claude Sonnet 4.5, the authors find that within-family model pairs score 5.9 pp higher in response pattern similarity and action graph similarity . |
Polymorphic Universal Transformer (2026.acl-long)
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Yilong Chen, Zitian Gao, Yihao Xiao, Jason Klein Liu, Xinyu Yang, Yifan Luo, Haoming Luo, Zhengmao Ye, Tingwen Liu, Ran Tao, Bryan Dai
| Challenge: | Compute Distribution Skew is a pathological phenomenon in ultra-deep recurrent models . it causes over-smoothing, representation rank collapse, and degraded reasoning performance. |
| Approach: | They propose a dynamic architecture that redefines recursive computation by decoupling parameter count from depth. |
| Outcome: | The proposed model significantly improves representation rank and reasoning robustness while reducing computation by 64.7%. |
Attend, Select and Eliminate: Accelerating Multi-turn Response Selection with Dual-attention-based Content Elimination (2023.findings-acl)
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| Challenge: | Pre-trained language models can be used to perform multi-turn response selection, but they can be expensive. |
| Approach: | They propose a framework and a strategy that progressively selects and eliminates unimportant content under context-response dual-attention. |
| Outcome: | The proposed method can effectively speed-up SOTA models without much performance degradation and shows a better trade-off between speed and performance than previous methods. |
RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization (2026.acl-long)
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Yihong Dong, Xue Jiang, Yongding Tao, Huanyu Liu, Kechi Zhang, Lili Mou, Rongyu Cao, Yingwei MA, Jue Chen, Binhua Li, Zhi Jin, Fei Huang, Yongbin Li, Ge Li
| Challenge: | Reinforcement Learning with Verifiable Reward (RLVR) has significantly advanced the complex reasoning abilities of Large Language Models (LLMs). |
| Approach: | They propose a hybrid-policy optimization approach that synergizes internal exploitation with external data to achieve stronger reasoning capabilities. |
| Outcome: | The proposed approach achieves state-of-the-art performance on six math reasoning benchmarks and superior performance on out-of distribution reasoning tasks. |
Improving Speech Translation by Fusing Speech and Text (2023.findings-emnlp)
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| Challenge: | In speech translation, multimodal data to address limitations of individual modalities has shown significant effectiveness. |
| Approach: | They propose a cross-modal model which supports three input modalities for speech, text and fused speech-text. |
| Outcome: | The proposed model achieves an average of 34.0 BLEU on MuST-C, GigaST and newstest benchmark. |
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)
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Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang
| Challenge: | Existing approaches to textual robustness evaluation focus on slightly modifying the input data, which maintains the original meaning and results in a different prediction. |
| Approach: | They propose a multilingual robustness evaluation toolkit for NLP that integrates universal text transformations, task-specific transformations and adversarial attack. |
| Outcome: | The toolkit includes universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses. |
CodeMEM: AST-Guided Adaptive Memory for Repository-Level Iterative Code Generation (2026.findings-acl)
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| Challenge: | Existing memory management approaches show promise but remain limited by natural language-centric representations. |
| Approach: | They propose an AST-guided dynamic memory management system for repository-level iterative code generation that maintains and updates repository context through AST operations. |
| Outcome: | The proposed system improves instruction following by 12.2% and reduces interaction rounds by 2–3 while maintaining competitive inference latency and token efficiency. |
Extract and Attend: Improving Entity Translation in Neural Machine Translation (2023.findings-acl)
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| Challenge: | Existing methods to improve entity translation in Neural machine translation still suffer from inaccurate translation of entities due to the lack of entity training instances. |
| Approach: | They propose an extract-and-tend approach to enhance entity translation in NMT by extracting entities from a dictionary and attending to them with a prefix. |
| Outcome: | Experiments on En-Zh and En-Ru show that the proposed approach improves translation accuracy and translation quality. |
No Label? No Problem: Unsupervised Continual Learning for Adaptive Medical ASR (2026.eacl-industry)
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| Challenge: | Medical audio often contains specialized terminology, such as medication names, which existing ASR systems struggle to transcribe accurately. |
| Approach: | They propose an unsupervised continual learning ASR framework that adapts to new data while preserving prior knowledge. |
| Outcome: | Experiments on real-world medical audio show that the proposed framework improves over state-of-the-art models. |
NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application (2021.findings-emnlp)
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| Challenge: | Existing language models are pre-trained and distilled on general corpus like Wikipedia, which has gaps with the news domain and may be suboptimal for news intelligence. |
| Approach: | They propose a method to distill existing language models on Wikipedia to enable efficient news intelligence. |
| Outcome: | The proposed model can be used to build and test a news intelligence application on Wikipedia and Wikipedia. |
CoF-CoT: Enhancing Large Language Models with Coarse-to-Fine Chain-of-Thought Prompting for Multi-domain NLU Tasks (2023.emnlp-main)
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| Challenge: | Chain-of-Thought prompting is popular in reasoning tasks, but its application to Large Language Models (LLMs) in Natural Language Understanding (NLU) is under-explored. |
| Approach: | They propose a Coarse-to-Fine Chain-of-Thought approach that breaks down NLU tasks into multiple reasoning steps where LLMs can learn to acquire essential concepts. |
| Outcome: | The proposed approach is effective in assisting the LLMs adapt to multi-grained NLU tasks under zero-shot and few-shot multi-domain settings. |
BPID: A Benchmark for Personal Identity Deduplication (2024.emnlp-industry)
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Runhui Wang, Yefan Tao, Adit Krishnan, Luyang Kong, Xuanqing Liu, Yuqian Deng, Yunzhao Yang, Henrik Johnson, Andrew Borthwick, Shobhit Gupta, Aditi Gundlapalli, Davor Golac
| Challenge: | Data deduplication is a critical task in data management and mining, focused on consolidating duplicate records that refer to the same entity. |
| Approach: | They propose to use a dataset with 1,000,000 unlabeled synthetic PII profiles and a subset of 10,000 pairs curated and labeled as matches or non-matches. |
| Outcome: | The proposed datasets contain synthetic profiles built from publicly available sources that do not represent real individuals. |
Breaking the Impasse: Dual-Scale Evolutionary Policy Training for Social Language Agents (2026.acl-long)
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| Challenge: | Reinforcement Learning with Verifiable Rewards (RLVR) is effective for closed-ended tasks, but it is not applicable to open-ended social language games. |
| Approach: | They propose a method that uses a time-scaled evolutionary perception mechanism to detect impasse by quantifying dual-scale value baseline divergence alongside match entropy. |
| Outcome: | Experiments on multiple social language games show that the proposed method outperforms baselines and avoids policy degeneration. |
Generation with Dynamic Vocabulary (2024.emnlp-main)
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| Challenge: | Using static vocabulary, vocabulary is ignored in advanced generation tasks. |
| Approach: | They propose a dynamic vocabulary that can involve arbitrary text spans during generation. |
| Outcome: | The proposed vocabulary can be deployed in a plug-and-play way, thus is attractive for various downstream applications. |
OntoGuard: Enforcing Action Admissibility for LLM Agents in Complex Interactive Environments (2026.findings-acl)
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| Challenge: | Existing approaches to large language models (LLMs) are limited by their ability to enforce environmental and behavioral admissibility. |
| Approach: | They propose an ontological framework to guard LLM agents by enforcing environmental and behavioral admissibility. |
| Outcome: | Experiments on ScienceWorld and VirtualHome show that OntoGuard can enforce environmental and behavioral admissibility while preventing invalid actions. |
KuiLeiXi: a Chinese Open-Ended Text Adventure Game (2021.acl-demo)
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Yadong Xi, Xiaoxi Mao, Le Li, Lei Lin, Yanjiang Chen, Shuhan Yang, Xuhan Chen, Kailun Tao, Zhi Li, Gongzheng Li, Lin Jiang, Siyan Liu, Zeng Zhao, Minlie Huang, Changjie Fan, Zhipeng Hu
| Challenge: | Recent advances in pre-trained language models have made it possible to generate human-like text. |
| Approach: | They propose to integrate an open-ended text adventure game in Chinese, named KuiLeiXi, where players interact with the AI until the plot goals are reached. |
| Outcome: | The proposed game lacks incentives and relies on players to explore on their own. |
MathFlow: Enhancing the Perceptual Flow of MLLMs for Visual Mathematical Problems (2026.acl-long)
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| Challenge: | Existing multimodal large language models (MLLMs) exhibit significant limitations when extracting essential information and reasoned properties from diagrams and performing complex reasoning based on these visual inputs. |
| Approach: | They propose a benchmark that provides a fine-grained evaluation of MLLMs’ perception and reasoning capabilities. |
| Outcome: | The proposed benchmark shows that existing MLLMs exhibit limitations when extracting essential information and reasoned properties from diagrams and performing complex reasoning based on these visual inputs. |
ProSparse: Introducing and Enhancing Intrinsic Activation Sparsity within Large Language Models (2025.coling-main)
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Chenyang Song, Xu Han, Zhengyan Zhang, Shengding Hu, Xiyu Shi, Kuai Li, Chen Chen, Zhiyuan Liu, Guangli Li, Tao Yang, Maosong Sun
| Challenge: | Activation sparsity is a promising paradigm for accelerating model inference . few large language models achieve high activation spar and comparable performance . |
| Approach: | They propose a method to achieve activation sparsity and acceleration in large language models . they introduce ReLU activation and adopt progressive sparse regularization . |
| Outcome: | The proposed method achieves high activation sparsity and comparable model performance. |
Benchmarking Meaning Representations in Neural Semantic Parsing (2020.emnlp-main)
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| Challenge: | Existing work on meaning representations is not comprehensively evaluated due to the lack of readily-available execution engines. |
| Approach: | They propose a unified benchmark on meaning representations by integrating existing semantic parsing datasets, completing the missing logical forms, and implementing the missing execution engines. |
| Outcome: | The proposed benchmark combines existing parsing datasets, completes missing logical forms, and implements missing execution engines. |