Papers by Liang Zhao
CLaw: Benchmarking Chinese Legal Knowledge in Large Language Models - A Fine-grained Corpus and Reasoning Analysis (2025.findings-emnlp)
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| Challenge: | a new benchmark is designed to evaluate LLMs on Chinese legal knowledge and its application in reasoning . general pre-training that ingests legal texts without specialized focus compromises reliability of LLM responses . achieving trustworthy legal reasoning in LLM requires a robust synergy of accurate knowledge retrieval and strong general reasoning capabilities. |
| Approach: | They propose a benchmark specifically engineered to evaluate LLMs on Chinese legal knowledge and its application in reasoning. |
| Outcome: | The proposed benchmark evaluates LLMs on Chinese legal knowledge and its application in reasoning. |
DentalGPT: Incentivizing Multimodal Reasoning in Dentistry (2026.findings-acl)
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Zhenyang Cai, Jiaming Zhang, Junjie Zhao, Ziyi Zeng, Yanchao Li, Liang Jingyi, Junying Chen, Yunjin Yang, Jiajun You, Shuzhi Deng, null Xieruiqiii, Yuanting Chen, Xiangyi Feng, Jianquan Li, Liangyi Chen, Junwen Wang, Shan Jiang, Benyou Wang
| Challenge: | Current multimodal large language models (MLLMs) show limited understanding of dental images. |
| Approach: | They propose a dental-specialized multimodal large language model trained via staged multimodal alignment and reinforcement learning. |
| Outcome: | The proposed model outperforms state-of-the-art models on disease classification and dental VQA tasks. |
Dual-Axis Generative Reward Model Toward Semantic and Turn-taking Robustness in Interactive Spoken Dialogue Models (2026.acl-long)
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Yifu Chen, Shengpeng Ji, Zhengqing Liu, Qian Chen, Wen Wang, Ziqing Wang, Yangzhuo Li, Tianle Liang, Zhou Zhao
| Challenge: | Reinforcement learning (RL) has improved text- and vision-language models, but its application in SDMs is hindered. |
| Approach: | They propose a dual-axis Generative Reward Model that provides semantic quality and interaction timing for SDMs. |
| Outcome: | The proposed model achieves state-of-the-art performance on interaction-quality assessment across a wide spectrum of datasets. |
GroupToM-Bench: Benchmarking Group Theory of Mind and Nonlinear Social Emergence in MLLMs (2026.acl-long)
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Weidong Tang, Jierui Li, Yueling Hou, Zihan Mei, Can Zhang, Xinyan Wan, Zhiyuan Liang, Pengfei Zhou, Yang You, Wangbo Zhao
| Challenge: | Existing models for general intelligence fail to model how mental states interact and crystallize into group-level outcomes. |
| Approach: | They propose a multimodal benchmark for group-level Theory of Mind (ToM) to probe nonlinear collective behavior. |
| Outcome: | The proposed model performs significantly below human levels, exposing blind spots in modeling social structures and nonlinear collective behavior. |
Document-Level Event Argument Extraction With a Chain Reasoning Paradigm (2023.acl-long)
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| Challenge: | Document-level event argument extraction aims to identify event arguments beyond sentence level, where a significant challenge is to model long-range dependencies. |
| Approach: | They propose a chain reasoning paradigm which captures long-range interdependence due to the chains’ compositional nature and generates decomposable first-order logic rules for reasoning. |
| Outcome: | The proposed method outperforms previous methods on two benchmarks and is robust enough to defend against adversarial attacks. |
Multi-modal Stance Detection: New Datasets and Model (2024.findings-acl)
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| Challenge: | Existing methods for stance detection for pure texts have limited results to multi-modal content. |
| Approach: | They propose a multi-modal stance detection framework that leverages target information to learn multi-modal stance features from textual and visual modalities. |
| Outcome: | The proposed framework achieves state-of-the-art in multi-modal stance detection on five datasets based on Twitter . |
CLHA: A Simple Yet Effective Contrastive Learning Framework for Human Alignment (2024.lrec-main)
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Feiteng Fang, Liang Zhu, Xi Feng, Jinchang Hou, Qixuan Zhao, Chengming Li, Xiping Hu, Ruifeng Xu, Min Yang
| Challenge: | Large language models (LLMs) have attracted considerable attention from academic and industrial communities due to their outstanding performance in various natural language processing tasks. |
| Approach: | They propose a Contrastive Learning Framework for Human Alignment to evaluate the noise within the data and dynamically adjust the training process. |
| Outcome: | The proposed framework surpasses other algorithms in terms of reward model scores, automatic evaluations, and human assessments on the widely used dataset "Helpful and Harmless" |
Modular and Parameter-Efficient Multimodal Fusion with Prompting (2022.findings-acl)
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| Challenge: | Recent research has made impressive progress in large-scale multimodal pre-training. |
| Approach: | They propose to use prompt vectors to align multimodal modalities by pretraining text inputs with prompts or embedding vectors. |
| Outcome: | The proposed method achieves comparable performance to several other multimodal fusion methods in low-resource settings. |
SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues (2021.acl-long)
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| Challenge: | Existing studies focus on identifying entities' relations from the semantics of dialogues-they utilize either the attention mechanism or a refined token graph to locate informative words. |
| Approach: | They propose a sequential structure prediction task to incrementally parse SocAoG for dynamic inference upon any incoming utterance. |
| Outcome: | Empirical results show that the proposed model infers social relations more accurately than the state-of-the-art methods. |
SHIFT: Selected Helpful Informative Frame for Video-guided Machine Translation (2025.emnlp-main)
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| Challenge: | Video-guided machine translation (VMT) aims to improve translation quality by integrating contextual information from paired short video clips. |
| Approach: | They propose a plug-and-play framework for video-guided machine translation with multimodal large language models. |
| Outcome: | The proposed framework improves performance of MLLMs while reducing computational cost. |
HacRED: A Large-Scale Relation Extraction Dataset Toward Hard Cases in Practical Applications (2021.findings-acl)
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Qiao Cheng, Juntao Liu, Xiaoye Qu, Jin Zhao, Jiaqing Liang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan, Yanghua Xiao
| Challenge: | Relation extraction (RE) is an essential topic in natural language processing and has attracted extensive attention. |
| Approach: | They propose a case-oriented construction framework to build a hard case relation extraction dataset with 65,225 relational facts annotated from 9,231 documents. |
| Outcome: | The proposed model achieves a high 96% F1 score on data quality and is far lower than humans. |
SDiaReward: Modeling and Benchmarking Spoken Dialogue Rewards with Modality and Colloquialness (2026.acl-long)
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Jingyu Lu, Yuhan Wang, Fan Zhuo, Xize Cheng, Changhao Pan, Xueyi Pu, Yifu Chen, Chenyuhao Wen, Tianle Liang, Zhou Zhao
| Challenge: | SDiaReward is an end-to-end spoken dialogue system that integrates paralinguistic nuances and spontaneous nature of human conversation. |
| Approach: | They propose an end-to-end multi-turn reward model trained on SDiaReward-Dataset . it is a collection of episode-level preference pairs targeting modality and colloquiality gaps . |
| Outcome: | The proposed model outperforms general-purpose audio LLMs in episode-level evaluation. |
DTCA: Decision Tree-based Co-Attention Networks for Explainable Claim Verification (2020.acl-main)
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| Challenge: | Recent methods to discover evidence for explainable claim verification are nontransparent and unexplained. |
| Approach: | They propose a Decision Tree-based Co-Attention model to discover evidence for explainable claim verification using neural networks. |
| Outcome: | The proposed model boosts the F1-score by more than 3.11%, 2.41% on two public datasets. |
Large Language Models Can Learn Representation in Natural Language (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) are unable to complete complex tasks involving multiple entities, such as tool APIs. |
| Approach: | They propose a method which uses natural language representations to refine entity descriptions for improved retrieval and LLM utilization. |
| Outcome: | The proposed method improves GPT-4's performance on classification tasks and API call tasks. |
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. |
Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge (2021.emnlp-main)
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| Challenge: | Existing methods for aspect category sentiment analysis do not necessarily occur in a sentence. |
| Approach: | They propose a Beta Distribution-guided aspect-aware graph construction based on external knowledge . they use aspect-related words as the pivots to derive aspect-relevant weights . |
| Outcome: | The proposed approach outperforms the state-of-the-art methods on 6 benchmark datasets. |
An Auxiliary Task Boosted Multi-task Learning Method for Service Account Retrieval with Limited Human Annotation (2023.emnlp-industry)
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| Challenge: | Existing approaches to service account retrieval have limited human annotation, resulting in labor-intensive and time-consuming tasks. |
| Approach: | They propose an Auxiliary task Boosted Multi-Task Learning method which introduces multiple auxiliary tasks and enhances the performance of the main task, service account retrieval. |
| Outcome: | The proposed method improves the performance of the main task, service account retrieval. |
Adversarial Regularization as Stackelberg Game: An Unrolled Optimization Approach (2021.emnlp-main)
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Simiao Zuo, Chen Liang, Haoming Jiang, Xiaodong Liu, Pengcheng He, Jianfeng Gao, Weizhu Chen, Tuo Zhao
| Challenge: | Existing approaches to adversarial regularization treat adversarials and defending players equally, which is undesirable because only the defending player contributes to the generalization performance. |
| Approach: | They propose a method which formulates adversarial regularization as a Stackelberg game and induces a competition between a leader and a follower. |
| Outcome: | The proposed method outperforms existing adversarial regularization baselines on a set of machine translation and natural language understanding tasks. |
Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs (2026.findings-acl)
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| Challenge: | Multi-agent LLMs are rapidly moving from prototype to real-world use . network topology is a first-order security parameter in multi-aggent systems . |
| Approach: | They propose a framework for comparing topology-conditioned memory leakage in multi-agent LLM systems. |
| Outcome: | The proposed framework evaluates topology-conditioned memory leakage in multi-agent LLM systems. |
PBoS: Probabilistic Bag-of-Subwords for Generalizing Word Embedding (2020.findings-emnlp)
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| Challenge: | Existing word embeddings assume fixed finite-size vocabularies, hindering their ability to provide useful word representations for out-of-vocaulary words. |
| Approach: | They propose a model that generalizes word embeddings without extra contextual information . they use the spellings of words to model subword segmentation and compute subword-based compositional word embeds. |
| Outcome: | The proposed model can generate meaningful subword segmentations without any source of explicit morphological knowledge. |
Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation (P19-3)
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Zhiting Hu, Haoran Shi, Bowen Tan, Wentao Wang, Zichao Yang, Tiancheng Zhao, Junxian He, Lianhui Qin, Di Wang, Xuezhe Ma, Zhengzhong Liu, Xiaodan Liang, Wanrong Zhu, Devendra Sachan, Eric Xing
| Challenge: | Texar is an open-source text generation toolkit that supports a broad set of text generation tasks. |
| Approach: | They introduce Texar, an open-source text generation toolkit that supports text generation tasks. |
| Outcome: | Texar supports machine translation, summarization, dialog, content manipulation, and more. |
HELENE: Hessian Layer-wise Clipping and Gradient Annealing for Accelerating Fine-tuning LLM with Zeroth-order Optimization (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) face memory challenges due to the high cost of backpropagation. |
| Approach: | They propose a zeroth-order (ZO) optimization that matches memory usage to inference . they propose scalable and memory-efficient zeroth order (ZE) optimizer that integrates annealed A-GNB gradients with diagonal Hessian estimation and layer-wise clipping as a second-order pre-conditioner. |
| Outcome: | The proposed algorithm outperforms state-of-the-art methods with an average speedup of 20 over MeZO on RoBERTa-large and OPT-1.3B. |
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. |
Emphasising Structured Information: Integrating Abstract Meaning Representation into LLMs for Enhanced Open-Domain Dialogue Evaluation (2025.findings-emnlp)
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| Challenge: | Existing evaluation metrics struggle to evaluate adversarial negative examples . existing metrics struggle in handling adversarials, resulting in low correlations with human judgments. |
| Approach: | They propose a framework that integrates AMR and domain-specific language models for automatic open-domain dialogue evaluation. |
| Outcome: | The proposed evaluation framework achieves strong correlations with human judgments across multiple datasets. |
Generalizing Word Embeddings using Bag of Subwords (D18-1)
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| Challenge: | Existing word embeddings techniques have a fixed vocabulary, i.e., they can only provide vectors over a finite set of common words that appear frequently in a given corpus. |
| Approach: | They propose a subword-level word vector generation model that views words as bags of character n-grams and provides good vectors for rare or unseen words. |
| Outcome: | The proposed model performs state-of-the-art in English word similarity task and in joint prediction of part-of speech tag and morphosyntactic attributes in 23 languages. |
TMFN: A Target-oriented Multi-grained Fusion Network for End-to-end Aspect-based Multimodal Sentiment Analysis (2024.lrec-main)
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| Challenge: | Existing methods for multimodal aspect-based sentiment analysis focus on fusing image regional information and textual words. |
| Approach: | They propose a multimodal aspect-based sentiment analysis method that integrates regional and global image information with global image data. |
| Outcome: | Experiments show that the proposed method outperforms state-of-the-art methods on two benchmark datasets. |
Robust Tool Use via Fission-GRPO: Learning to Recover from Execution Errors (2026.acl-long)
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Zhiwei Zhang, Fei Zhao, Rui Wang, Zezhong Wang, Bin Liang, Jiakang Wang, Yao Hu, Shaosheng Cao, Kam-Fai Wong
| Challenge: | Large language models (LLMs) can call tools effectively, but they remain brittle in multi-turn execution. |
| Approach: | They propose a framework that converts execution errors into on-policy corrective supervision within the RL training loop. |
| Outcome: | The proposed framework improves the error recovery rate of Qwen3-8B by 5.7% absolute and overall accuracy by 4.0% on BFCL v4 Multi-Turn. |
Dynamic Feature Fusion for Sign Language Translation Using HyperNetworks (2025.findings-naacl)
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| Challenge: | Using RGB and keypoint streams, sign language translation is highly dependent on the brain's ability to process color, shape, and motion simultaneously. |
| Approach: | They propose a hypernetwork-based fusion method that extracts salient features from RGB and keypoint streams and introduces self-distillation and SST contrastive learning to maintain feature advantages while aligning the global semantic space. |
| Outcome: | The proposed method achieves state-of-the-art performance on two public sign language datasets, reducing model parameters by about two-thirds. |
Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization (P19-1)
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Paul Pu Liang, Zhun Liu, Yao-Hung Hubert Tsai, Qibin Zhao, Ruslan Salakhutdinov, Louis-Philippe Morency
| Challenge: | Existing methods to regularize multimodal data are imperfect due to imperfect modalities, missing entries or noise corruption. |
| Approach: | They propose a method to regularize multimodal data by tensor rank minimization . they use correlations between time and modalities to generate low-rank tenses . |
| Outcome: | The proposed model achieves good results across various levels of imperfection. |
DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models (2024.acl-long)
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Damai Dai, Chengqi Deng, Chenggang Zhao, R.x. Xu, Huazuo Gao, Deli Chen, Jiashi Li, Wangding Zeng, Xingkai Yu, Y. Wu, Zhenda Xie, Y.k. Li, Panpan Huang, Fuli Luo, Chong Ruan, Zhifang Sui, Wenfeng Liang
| Challenge: | Mixture-of-Experts (MoE) architectures face challenges in ensuring expert specialization . despite the promising performance, scaling language models to an extremely large scale is associated with exceedingly high computational costs. |
| Approach: | They propose an architecture that allows for ultimate expert specialization by segmenting experts into mN ones and activating mK from them. |
| Outcome: | The proposed architecture achieves comparable performance with GShard with 2B parameters and computation. |
Looking Beyond Text: Reducing Language Bias in Large Vision-Language Models via Multimodal Dual-Attention and Soft-Image Guidance (2025.emnlp-main)
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| Challenge: | Large vision-language models (LVLMs) have been criticized for their language bias. |
| Approach: | They propose to use a dual-attention mechanism to construct separate attention for visual and text inputs to enhance integration of visual inputs across models. |
| Outcome: | Experiments show that the proposed model debiases LVLMs from their language bias, enhancing visual comprehension and reducing hallucinations without additional resources. |
Target-Guided Open-Domain Conversation (P19-1)
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| Challenge: | a new study aims to improve opendomain chat systems by integrating goals and strategy into the system. |
| Approach: | They propose a structured approach that introduces coarse-grained keywords to control intended content of system responses and attains smooth conversation transition through turn-level supervised learning. |
| Outcome: | The proposed system produces meaningful and effective conversations significantly better than other approaches. |
Token-wise Curriculum Learning for Neural Machine Translation (2021.findings-emnlp)
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| Challenge: | Existing curriculum learning approaches to Neural Machine Translation (NMT) require sampling sufficient amounts of “easy” samples from training data at the early stage of training. |
| Approach: | They propose a token-wise curriculum learning approach that creates sufficient amounts of easy samples from training data. |
| Outcome: | The proposed approach outperforms baselines on five language pairs on low-resource languages. |
PITA: Prompting Task Interaction for Argumentation Mining (2024.acl-long)
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| Challenge: | Argumentation mining (AM) aims to detect arguments and their inherent relations from textual compositions. |
| Approach: | They propose a method to model the inter-relationships among three subtasks within a generative framework. |
| Outcome: | The proposed method achieves state-of-the-art performance on two AM benchmarks. |
MemeReaCon: Probing Contextual Meme Understanding in Large Vision-Language Models (2025.emnlp-main)
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Zhengyi Zhao, Shubo Zhang, Yuxi Zhang, Yanxi Zhao, Yifan Zhang, Zezhong Wang, Huimin Wang, Yutian Zhao, Bin Liang, Yefeng Zheng, Binyang Li, Kam-Fai Wong, Xian Wu
| Challenge: | Current approaches focus on isolated meme analysis, either for harmful content detection or standalone interpretation, overlooking a fundamental challenge: the same meme can express different intents depending on its conversational context. |
| Approach: | They propose a benchmark to evaluate how large vision language models understand memes in their original context. |
| Outcome: | The proposed benchmark evaluates how large vision language models understand meme intent in their original context. |
GenPilot: A Multi-Agent System for Test-Time Prompt Optimization in Image Generation (2025.findings-emnlp)
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Wen Ye, Zhaocheng Liu, Gui Yuwei, Tingyu Yuan, Yunyue Su, Bowen Fang, Chaoyang Zhao, Qiang Liu, Liang Wang
| Challenge: | Existing methods for text-to-image synthesis lack systematic error analysis and refinement strategies, resulting in limited reliability and effectiveness. |
| Approach: | They propose a plug-and-play multi-agent system called GenPilot that integrates error analysis, clustering-based adaptive exploration, fine-grained verification and a memory module for iterative optimization. |
| Outcome: | The proposed method improves text consistency and structural coherence on images with a plug-and-play system. |
Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization (2021.acl-long)
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Chen Liang, Simiao Zuo, Minshuo Chen, Haoming Jiang, Xiaodong Liu, Pengcheng He, Tuo Zhao, Weizhu Chen
| Challenge: | 'lottery tickets' can be trained to match the performance of a full model . subnetwork training can also outperform random sampled subnetworks of the same size . |
| Approach: | They propose to train a subnetwork of 'lottery tickets' to match the full model's performance. |
| Outcome: | The proposed model outperforms subnetworks of the same size in a phase transition phenomenon . the proposed model improves single task fine-tuning by 0.9 points on BERT-base and 1.0 points on GLUE large . |
LayoutDIT: Layout-Aware End-to-End Document Image Translation with Multi-Step Conductive Decoder (2023.findings-emnlp)
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| Challenge: | Existing methods struggle to capture the visual layout in complex document images. |
| Approach: | They propose to integrate layout knowledge into document image translation by using a layout-aware encoder and a multi-step conductive decoder to achieve the translation step by step. |
| Outcome: | The proposed model outperforms state-of-the-art methods with better parameter efficiency. |
Circuit Complexity Bounds for RoPE-based Transformer Architecture (2025.emnlp-main)
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| Challenge: | Recent studies provide the circuit complexity bounds to Transformer-like architectures. position embedding has emerged as a crucial technique in modern large language models. |
| Approach: | They propose to use position embedding to improve Transformer-like architectures by analyzing their circuits and analyzing the results. |
| Outcome: | The proposed model is able to solve canonical tasks without embedding positional information. |
Spatial-Agent: Agentic Geo-spatial Reasoning with Scientific Core Concepts (2026.acl-long)
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| Challenge: | Existing LLM-based agents lack inherent spatial awareness, relying on web search or text matching while hallucinating spatial relationships. |
| Approach: | They propose a spatial-based agent that can perform real-world geospatial computations . they use natural-language questions to parse into executable workflows based on geoFlow Graphs - directed acyclic graphs with nodes corresponding to spatial concepts and edges representing transformations. |
| Outcome: | The proposed agent outperforms existing baselines on MapEval-API and MapQA benchmarks while producing interpretable and executable geospatial workflows. |
DUAL RM: Beyond Rule-based Preference Reward Modeling via Meta-Reward (2026.acl-long)
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Xiaobo Liang, Wanfu Wang, Qipeng Huang, Yuyang Ding, Zecheng Tang, Yixin Ji, Qianben Chen, Zhe Zhao, Kehai Chen, Juntao Li, Min Zhang
| Challenge: | Existing preference-based reward modeling methods face a recursive dependency where each verifier requires a meta-verifier, leading to continuous and costly dependence on human annotation. |
| Approach: | They propose a dual RM that couples discriminative and generative reward models under a non-parametric meta-reward. |
| Outcome: | The proposed model achieves strong performance across major preference benchmarks and even when trained exclusively on language modality, it exhibits robust cross-modal transfer on Omni-RewardBench. |
Learning to Plan by Updating Natural Language (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have shown remarkable performance in basic natural language tasks. |
| Approach: | They propose a method that iteratively updates the task plan with new steps and behavioral instructions to guide LLMs to generate the correct solutions step by step. |
| Outcome: | The proposed method improves performance on five reasoning type tasks and can be directly applied to other LLMs. |
Analyzing and Reducing the Performance Gap in Cross-Lingual Transfer with Fine-tuning Slow and Fast (2023.acl-long)
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| Challenge: | Existing research shows that a multilingual pre-trained language model fine-tuned with one (source) language performs well on downstream tasks for non-source languages . However, there is a clear performance gap between the source and non-sourced languages - this gap can be reduced by reducing forgetting. |
| Approach: | They propose a method to fine-tune a multilingual pre-trained language model fine- tuned with one (source) language and four training policies to address the performance gap. |
| Outcome: | The proposed method outperforms baselines on the XNLI dataset by a clear margin. |
From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems (2025.findings-emnlp)
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Zekun Zhou, Xiaocheng Feng, Lei Huang, Xiachong Feng, Ziyun Song, Ruihan Chen, Liang Zhao, Weitao Ma, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin
| Challenge: | rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research. |
| Approach: | They organize the relevant studies into three main categories: hypothesis formulation, hypothesis validation, and manuscript publication. |
| Outcome: | The authors summarize the current state of research in three main areas: hypothesis formulation, hypothesis validation, and manuscript publication. |
PPTC-R benchmark: Towards Evaluating the Robustness of Large Language Models for PowerPoint Task Completion (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are increasingly used for task completion in real-world situations. |
| Approach: | They propose a PowerPoint Task Completion-Robustness (PPTC-R) benchmark to measure LLMs’ robustness to the user PPT task instruction and software version (Powerpoint). |
| Outcome: | The proposed benchmark compares 3 closed-source and 4 open-source LLMs to the PowerPoint task instruction and software version (Powerpoint) . |
Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions (2026.findings-acl)
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| Challenge: | Existing large language models lack spatial computing capabilities and access to up-to-date geospatial data. |
| Approach: | They propose a Retrieval-Augmented Generation framework for geospatial question answering . it integrates structured spatial databases with LLMs via a hybrid spatial retriever . |
| Outcome: | Experiments show that Spatial-RAG significantly improves over baselines. |
MENTOR: Efficient Autoregressive Image Generation with Balanced Multimodal Control (2026.findings-acl)
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| Challenge: | Recent text-to-image models achieve impressive visual quality but still face challenges in precise controllability, balancing multimodal inputs, and high training cost for multimodal image generation. |
| Approach: | They propose an autoregressive framework with a two-stage training paradigm for controllable multimodal image generation. |
| Outcome: | Extensive experiments on DreamBench++ and DreamBech show that the proposed framework achieves a strong balance between textual and visual guidance for controllable image generation. |
Alleviating Hallucinations from Knowledge Misalignment in Large Language Models via Selective Abstention Learning (2025.acl-long)
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Lei Huang, Xiaocheng Feng, Weitao Ma, Yuchun Fan, Xiachong Feng, Yuxuan Gu, Yangfan Ye, Liang Zhao, Weihong Zhong, Baoxin Wang, Dayong Wu, Guoping Hu, Lingpeng Kong, Tong Xiao, Ting Liu, Bing Qin
| Challenge: | Large language models (LLMs) suffer from severe hallucination issues due to the knowledge misalignment between the pre-training stage and the supervised fine-tuning stage. |
| Approach: | They propose a training objective with an abstention mechanism that selectively rejects tokens that misalign with the desired knowledge distribution via a special [REJ] token. |
| Outcome: | The proposed model selectively rejects tokens that misalign with the desired knowledge distribution via a special [REJ] token. |
Neural Relation Classification with Text Descriptions (C18-1)
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| Challenge: | State-of-the-art methods for relation classification suffer from data sparsity issue greatly. |
| Approach: | They propose a new neural relation classification method which integrates entities’ text descriptions into deep neural networks models. |
| Outcome: | The proposed method achieves much better experimental results than other state-of-the-art methods on the SemEval 2010 dataset. |
CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)
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Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan
| Challenge: | Existing language evaluation benchmarks for English are limited to English . lack of such benchmarks makes it difficult to replicate success in other languages . |
| Approach: | They introduce a large-scale Chinese language understanding evaluation benchmark . the benchmark uses a set of current state-of-the-art pre-trained Chinese models . |
| Outcome: | The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is released . the benchmark evaluates models across a wide range of tasks on original Chinese text . existing language evaluation benchmarks are mostly limited to English . |
PresentAgent: Multimodal Agent for Presentation Video Generation (2025.emnlp-demos)
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| Challenge: | Existing methods for generating static slides or text summaries are limited to producing narrated presentations. |
| Approach: | They propose a multimodal agent that transforms long-form documents into narrated presentations. |
| Outcome: | The present agent produces fully synchronized visual and spoken content that closely mimics human-style presentations. |
CDS: Data Synthesis Method Guided by Cognitive Diagnosis Theory (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, but the complexity of emerging tasks and higher performance demands highlight the need for continuous improvement. |
| Approach: | They propose a method that refines evaluation results and characterizes model profiles at the knowledge component level. |
| Outcome: | The proposed method improves performance across multiple benchmarks and academic exams. |
Learning How to Remember: A Meta-Cognitive Management Method for Structured and Transferable Agent Memory (2026.findings-acl)
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| Challenge: | Existing approaches store memory in fixed representations and reuse it at a single or implicit level of abstraction, which limits generalization and often leads to negative transfer when distribution shift. |
| Approach: | They propose a Meta-Cognitive Memory Abstraction method which decouples task execution from memory management by combining a frozen task model with a learned memory copilot. |
| Outcome: | Experiments on ALFWorld, ScienceWorld, and BabyAI show that the proposed method improves performance, out-of-distribution generalization, and cross-task transfer over several baselines. |
VersaTune: An Efficient Data Composition Framework for Training Multi-Capability LLMs (2025.emnlp-main)
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| Challenge: | Existing work focuses on domain-specific enhancements during fine-tuning, the challenge of which lies in catastrophic forgetting of knowledge across other domains. |
| Approach: | They propose a data composition framework that allows LLMs to enhance their multi-domain capabilities during supervised fine-tuning. |
| Outcome: | The proposed framework improves multi-domain fostering performance by 29.77% compared to uniform weights. |
Logic-Thinker: Teaching Large Language Models to Think more Logically. (2025.findings-emnlp)
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| Challenge: | Recent Large Reasoning Models (LRMs) have demonstrated the ability to generate long chains of thought (LongCoT) LongCoT still faces challenges such as redundancy and logical incoherence. |
| Approach: | They propose a neural-symbolic reasoning framework that generates chains of thought . they propose Logic-Thinker, which transforms symbolic solvers into chains of thoughts . |
| Outcome: | The proposed framework outperforms models fine-tuned with ThinkerCoT on logic reasoning tasks. |
ARCH: Efficient Adversarial Regularized Training with Caching (2021.findings-emnlp)
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Simiao Zuo, Chen Liang, Haoming Jiang, Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen, Tuo Zhao
| Challenge: | Existing approaches to regularize models require generating a perturbation for each sample in each epoch. |
| Approach: | They propose an adversarial regularization method where perturbations are generated and cached once every several epochs. |
| Outcome: | The proposed method significantly eases the computational burden (saves up to 70% of computational time) it produces a notably better (in most of the tasks) or comparable model generalization. |
SLIDE: A Framework Integrating Small and Large Language Models for Open-Domain Dialogues Evaluation (2024.findings-acl)
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| Challenge: | Existing approaches to evaluate open domain dialogues have a one-to-many problem . existing approaches lack commonsense reasoning biases and perform poorly in domain-specific scenarios. |
| Approach: | They propose a framework that leverages both a small, specialised model and LLMs for the evaluation of open-domain dialogues. |
| Outcome: | The proposed framework achieves state-of-the-art performance in both classification and evaluation tasks and exhibits better correlation with human judgements. |
PsychEval: A Multi-Session and Multi-Therapy Benchmark for High-Realism AI Psychological Counselor (2026.findings-acl)
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Qianjun Pan, Junyi Wang, Jie Zhou, Yutao Yang, Junsong Li, Kaiyin Xu, Yougen Zhou, Yihan Li, JingYuan Zhao, Qin Chen, Ningning Zhou, Kai Chen, Liang He
| Challenge: | Existing models focus on a single therapy, but complex cases require flexible strategies among various therapies. |
| Approach: | They propose a multi-session, multi-therapy, and highly realistic benchmark . it is designed to address three key challenges: 1) can we train a highly realistic AI counselor? 2) How to systematically evaluate an AI counselor?" |
| Outcome: | The proposed benchmark is annotated with extensive professional skills and includes over 677 meta-skills and 4577 atomic skills. |
Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-Language Models (2024.acl-long)
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Weihong Zhong, Xiaocheng Feng, Liang Zhao, Qiming Li, Lei Huang, Yuxuan Gu, Weitao Ma, Yuan Xu, Bing Qin
| Challenge: | Large Vision-Language Models (LVLMs) suffer from multimodal hallucinations . however, the generated hallucines could influence the models’ subsequent generation . |
| Approach: | They propose a framework to evaluate LVLMs' behaviors when encountering generated hallucinations and a method to revise the output distribution of LVLs with the one derived from the residual visual input. |
| Outcome: | The proposed framework reduces the performance of open-source LVLMs by 31%, indicating that they are prone to accept the generated hallucinations and make false claims that they would not have supported without distractions. |
Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning (2022.emnlp-main)
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| Challenge: | Existing research on building ES conversation systems only considered single-turn interactions with users, which is over-simplified and has limited support for multi-turn systems. |
| Approach: | They propose a multi-turn ES conversation system that uses lookahead heuristics to estimate future user feedback after using particular strategies. |
| Outcome: | The proposed system significantly outperforms baselines in both dialogue generation and strategy planning. |
ReasVQA: Advancing VideoQA with Imperfect Reasoning Process (2025.naacl-long)
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| Challenge: | Existing approaches to VideoQA often fail when complex reasoning or temporal relationships are involved. |
| Approach: | They propose a method that leverages reasoning processes generated by Multimodal Large Language Models to improve VideoQA models. |
| Outcome: | The proposed method improves VideoQA models on three benchmarks. |
Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets (P19-1)
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| Challenge: | Natural Language Sentence Matching (NLSM) is a popular NLP task. |
| Approach: | They propose to use QuoraQP to train and evaluate NLSM models using a selection bias framework. |
| Outcome: | The proposed framework can improve generalization ability of trained models and give more trustworthy evaluation results for real-world adoptions. |
ELAD: Explanation-Guided Large Language Models Active Distillation (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) are hindered by their memory inefficiency, computational demands, and the high costs of API inferences. |
| Approach: | They propose an Explanation-Guided LLMs Active Distillation framework that employs an active learning strategy to optimize the balance between annotation costs and model performance. |
| Outcome: | The proposed framework significantly improves the efficiency of LLMs knowledge distillation. |
PRIME: A Process-Outcome Alignment Benchmark for Verifiable Reasoning in Mathematics and Engineering (2026.acl-long)
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Xiangfeng Wang, Hangyu Guo, Yanlin Lai, Mitt Huang, Liang Zhao, Chengyuan Yao, Yinmin Zhang, Qi Han, null Xiaoxiaoren, Chun Yuan, Tong Xu, Zheng Ge, Xiangyu Zhang, Daxin Jiang
| Challenge: | Current outcome-centric verification paradigms neglect potential errors in the derivation process. |
| Approach: | They propose a process-aware RLVR training paradigm utilizing verifiers selected via **PRIME**. |
| Outcome: | The proposed approach outperforms the baseline verification paradigm on AIME24, AIME25, and Beyond-AIME models. |
M2PT: Multimodal Prompt Tuning for Zero-shot Instruction Learning (2024.emnlp-main)
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Taowen Wang, Yiyang Liu, James Liang, Junhan Zhao, Yiming Cui, Yuning Mao, Shaoliang Nie, Jiahao Liu, Fuli Feng, Zenglin Xu, Cheng Han, Lifu Huang, Qifan Wang, Dongfang Liu
| Challenge: | Multimodal Large Language Models (MLLMs) exhibit remarkable performance across a wide range of domains. |
| Approach: | They propose a multimodal prompt tuning approach for efficient instruction tuning of MLLMs. |
| Outcome: | The proposed approach shows superior performance on multimodal evaluation datasets compared to state-of-the-art methods. |
C2PO: Diagnosing and Disentangling Bias Shortcuts in LLMs (2026.findings-acl)
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| Challenge: | Existing approaches to solve large language models address stereotypical and structural biases in isolation . however, prior paradigms address these in isolation, often at the expense of exacerbating the other . |
| Approach: | They propose a framework to tackle latent spurious feature correlations within input that drive erroneous reasoning shortcuts. |
| Outcome: | The proposed framework mitigates stereotypical and structural biases while preserving robust general reasoning capabilities. |
Structured Attention for Unsupervised Dialogue Structure Induction (2020.emnlp-main)
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| Challenge: | Using structured attention, a model can learn dialogue structure in unsupervised fashion. |
| Approach: | They propose to incorporate structured attention layers into a Variational Recurrent Neural Network model with discrete latent states to learn dialogue structure in an unsupervised fashion. |
| Outcome: | The proposed model learns semantic structures similar to templates used to generate a dialogue corpus on two-party datasets and on multi-party dialogues, disentangling dialogues without human annotation. |
Iterative Nearest Neighbour Machine Translation for Unsupervised Domain Adaptation (2023.findings-acl)
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| Challenge: | Existing methods for supervised domain adaptation of machine translation focus on fine-tuning, which is non-extensible. |
| Approach: | They propose to perform unsupervised domain adaptation in a non-parametric manner by using in-domain monolingual data and performing nearest neighbour inference on both forward and backward directions. |
| Outcome: | The proposed method significantly improves the in-domain translation performance and achieves state-of-the-art results among non-parametric methods. |
The ManDi Corpus: A Spoken Corpus of Mandarin Regional Dialects (2022.lrec-1)
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| Challenge: | Existing methods of remote speech data collection were limited by the telephone bandwidth and were therefore of low quality for phonetic research. |
| Approach: | They introduce a spoken corpus of regional Mandarin dialects and Standard Mandarin. |
| Outcome: | The proposed corpus contains 357 recordings (about 9.6 hours) of monosyllabic words, disyllable words, short sentences, a short passage and a poem, produced in standard Mandarin and in one of six regional Mandarin dialects. |
Diagnosing and Remedying Representation Deficiencies for Deterministic Reasoning in KGQA (2026.acl-long)
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| Challenge: | Existing approaches to solve non-deterministic reasoning problems in large language models are limited by their complexity and lack of a clear understanding of the problem. |
| Approach: | They propose a method to diagnose and correct non-deterministic reasoning behaviors in large language models. |
| Outcome: | The proposed method outperforms baselines and WebQSP benchmarks on the widely used WebQ SP and CWQ benchmarks. |
Bayesian Active Learning with Gaussian Processes Guided by LLM Relevance Scoring for Dense Passage Retrieval (2026.findings-acl)
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Junyoung Kim, Anton Korikov, Jiazhou Liang, Justin Cui, Yifan Simon Liu, Qianfeng Wen, Mark Zhao, Scott Sanner
| Challenge: | Existing approaches passively rely on first-stage dense retrievers, which leads to two limitations: failing to retrieve relevant passages in semantically distinct clusters and failing to propagate relevance signals to the broader corpus. |
| Approach: | They propose a framework that propagates sparse LLM relevance signals across the embedding space to guide global exploration. |
| Outcome: | Experiments show that the proposed framework outperforms existing approaches under the same budget on all four datasets. |
LLM-Driven Multi-Perspective Location Completion for Next Location Prediction (2026.findings-acl)
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| Challenge: | Existing methods assume that check-in data is complete, overlooking the subjective nature of user behavior, leading to inaccurate capture of user preferences. |
| Approach: | They propose a framework that uses spatial coordinates to augment location completion by transforming geographic coordinates into text. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on three real-world datasets. |
ZhuJiu-Knowledge: A Fairer Platform for Evaluating Multiple Knowledge Types in Large Language Models (2024.naacl-demo)
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| Challenge: | evaluating the knowledge of large language models (LLMs) is crucial, and rapid advancement in large language modeling has heightened the importance of model evaluations. |
| Approach: | They propose a fairer benchmark for evaluating multiple knowledge types of LLMs by focusing on commonsense knowledge, world knowledge, and language knowledge. |
| Outcome: | The proposed framework evaluates 14 current mainstream LLMs and provides a detailed discussion and analysis of their results. |
Efficient and Effective Prompt Tuning via Prompt Decomposition and Compressed Outer Product (2025.naacl-long)
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| Challenge: | Existing methods for fine-tuning pre-trained language models overlook intrinsic semantic associations between soft prompt tokens, leading to high discreteness and limited interactions. |
| Approach: | They propose a low-parameters Prompt Tuning method which leverages prompt decomposition and compressed outer product to facilitate multiple interactions among prompt tokens. |
| Outcome: | Experiments on six architectures and eight datasets show that the proposed method outperforms state-of-the-art methods in performance and efficiency. |
Read Extensively, Focus Smartly: A Cross-document Semantic Enhancement Method for Visual Documents NER (2022.coling-1)
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| Challenge: | Existing methods to extract entities from visually-rich documents ignore the inherent multimodality of VRDs and thus the suboptimal results are achieved. |
| Approach: | They propose a multimodal semantic enhancement method that filters redundant information in the current document and a cross-document information awareness technique to enrich the entity-related context. |
| Outcome: | The proposed method outperforms existing methods on two documents understanding benchmarks covering eight languages. |
Mosaic-IT: Cost-Free Compositional Data Synthesis for Instruction Tuning (2025.findings-acl)
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| Challenge: | Current instruction tuning relies on teacher models or human intervention to generate and refine the instructions and responses for training, which are costly, non-sustainable, and may lack diversity. |
| Approach: | They propose a human/model-free compositional data synthesis method that can create rich and diverse augmentations from existing instruction tuning data to enhance large language models. |
| Outcome: | The proposed method improves performance over benchmarks and reduces training costs by 80% compared with original instruction tuning. |
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. |
Extracting and Combining Abilities For Building Multi-lingual Ability-enhanced Large Language Models (2025.emnlp-main)
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| Challenge: | Existing work relies on training with multi-lingual ability-related data, which may not be available for low-resource languages. |
| Approach: | They propose a multi-lingual ability-enhanced LLM that extracts language-agnostic ability-related weights from LLMs and combine them across different languages by simple addition and subtraction operations without training. |
| Outcome: | The proposed approach extracts language-agnostic ability-related weights from LLMs and combine them across different languages without training. |
LearnAct: Few-Shot Mobile GUI Agent with a Unified Demonstration Benchmark (2026.findings-acl)
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Guangyi Liu, Pengxiang Zhao, Liang Liu, Zhiming Chen, Yuxiang Chai, Yaozhen Liang, WenHao Wang, Siheng Chen, Zhengxi Lu, Shuai Ren, Hao Wang, Shibo He, Yong Liu, Wenchao Meng
| Challenge: | Mobile GUI agents show promise in automating tasks but face significant generalization challenges in long-tail scenarios. |
| Approach: | They propose a benchmark framework for mobile GUI agents that measures the performance of GUI agents by analyzing their performance. |
| Outcome: | The LearnGUI benchmark outperforms existing methods in offline and online evaluations and demonstrates consistent gains across model architectures. |
Hi-ArG: Exploring the Integration of Hierarchical Argumentation Graphs in Language Pretraining (2023.emnlp-main)
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Jingcong Liang, Rong Ye, Meng Han, Qi Zhang, Ruofei Lai, Xinyu Zhang, Zhao Cao, Xuanjing Huang, Zhongyu Wei
| Challenge: | Recent studies have discussed its capability to assist language models for various applications. |
| Approach: | They propose a structure to organize arguments using the **Hi**erarchical **Ar**gumentation **G**raph (Hi-ArG) and propose two approaches to exploit Hi-AarG, including a text-graph multi-modal model GreaseArR and a framework augmented with graph information. |
| Outcome: | The proposed structure supersedes existing language models on two argumentation tasks while incorporating graph information during further training improves vanilla language models. |
A3: Android Agent Arena for Mobile GUI Agents with Essential-State Procedural Evaluation (2026.findings-acl)
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Yuxiang Chai, Shunye Tang, Han Xiao, Weifeng Lin, Hanhao Li, Jiayu Zhang, Liang Liu, Pengxiang Zhao, Guangyi Liu, Guozhi Wang, Shuai Ren, Rongduo Han, Haining Zhang, Siyuan Huang, Hongsheng Li
| Challenge: | Existing evaluation methods for mobile GUI agents rely on static frame assessments or offline static apps. |
| Approach: | They propose an evaluation system that leverages large language models as reward models to verify task completion and process achievement. |
| Outcome: | The proposed system addresses the limitations of traditional function based evaluation methods on online dynamic apps. |
AssoCiAm: A Benchmark for Evaluating Association Thinking while Circumventing Ambiguity (2025.emnlp-main)
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| Challenge: | Recent advances in multimodal large language models (MLLMs) have garnered significant attention, offering a promising pathway toward artificial general intelligence (AGI). |
| Approach: | They propose a benchmark to evaluate associative ability while circumventing the inherent ambiguity in association tasks by decomposing ambiguities into two types and propose 'assoCiAm' they conduct extensive experiments on MLLMs, revealing a strong positive correlation between cognition and association. |
| Outcome: | The proposed method shows that ambiguity in association evaluations makes MLLMs more random-like and the model's behavior more random. |
VideoLLM Knows When to Speak: Enhancing Time-Sensitive Video Comprehension with Video-Text Duet Interaction Format (2025.findings-emnlp)
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| Challenge: | Recent studies on video large language models focus on model architectures and training datasets . interaction format between user and model is unsatisfactory for time-sensitive tasks . |
| Approach: | They propose a video-text duet interaction format that allows for continuous playback of the video . when a text message ends, the video continues to play, similar to the alternative of two performers in a duet. |
| Outcome: | The proposed format improves performance on time-sensitive tasks with minimal training efforts. |
Question Tells You Where the Answer Is: Intention-aware Long-Context KV Cache Compression (2026.acl-long)
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Liang Zhao, Xiaocheng Feng, Weihong Zhong, Lei Huang, Kun Zhu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin
| Challenge: | Recent methods to reduce the KV cache size fail to identify crucial KVs for generation while excluding others accurately, resulting in severe information loss. |
| Approach: | They propose an intention-aware KV cache eviction method that identifies and retains crucial KVs according to the attention distribution of intention, which semantically reflects the user’s goal and determines which part of the context is relevant. |
| Outcome: | The proposed method can maintain the model performance while reducing the KV cache size from 128K to 2K, leading to a 6.3x increase in decoding speed and 7.8x enhancement in memory efficiency compared to the default setting. |
Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data (N19-1)
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| Challenge: | Neural machine translation systems have become state-of-the-art approaches for Grammatical Error Correction (GEC) task. |
| Approach: | They propose a copy-augmented architecture for the Grammatical Error Correction task by copying unchanged words from the source sentence to the target sentence. |
| Outcome: | The proposed architecture outperforms all recently published state-of-the-art results by a large margin. |
RelCLIP: Adapting Language-Image Pretraining for Visual Relationship Detection via Relational Contrastive Learning (2022.emnlp-main)
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| Challenge: | Existing visual relationship detection models only use numeric ids of relation labels for training, but ignore semantic correlation between labels. |
| Approach: | They propose a visual Relationship prediction framework that transfers natural language knowledge from Contrastive Language-Image Pre-training models to enhance the relationship prediction. |
| Outcome: | The proposed framework improves visual relationship prediction by matching semantic correlations with relation triplets. |
A Query-Response Framework for Whole-Page Complex-Layout Document Image Translation with Relevant Regional Concentration (2025.findings-acl)
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| Challenge: | Existing methods for document image translation rely on the vanilla encoder-decoder paradigm . a novel dynamic aggregation mechanism is designed to enhance the text semantics in query features toward translation. |
| Approach: | They propose a Query-Response DIT framework that reformulates the DIT task into a parallel response/translation process of multiple queries. |
| Outcome: | The proposed framework improves translation quality on four translation directions on three benchmarks. |
DORM: Preference Data Weights Optimization for Reward Modeling in LLM Alignment (2025.findings-emnlp)
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Rongzhi Zhang, Chenwei Zhang, Xinyang Zhang, Liang Qiu, Haoming Jiang, Yuchen Zhuang, Qingru Zhang, Hyokun Yun, Xian Li, Bing Yin, Tuo Zhao, Chao Zhang
| Challenge: | Existing approaches to align large language models with human preferences are noisy and varying in importance of preference samples. |
| Approach: | a new method enhances reward modeling by learning to dynamically weigh preference data. |
| Outcome: | a new method improves the performance of large language models with human preferences . it initializes data importance and iteratively refines them to maximize validation performance. |
Unilaw-R1: A Large Language Model for Legal Reasoning with Reinforcement Learning and Iterative Inference (2025.emnlp-main)
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| Challenge: | Reasoning-focused large language models (LLMs) are rapidly evolving across various domains, yet their capabilities in handling complex legal problems remain underexplored. |
| Approach: | They propose a large language model tailored for legal reasoning with a 7-billion parameter scale and a two-stage training strategy combining Supervised Fine-Tuning and Reinforcement Learning. |
| Outcome: | The proposed model outperforms all models of similar scale on authoritative benchmarks and outperformed Qwen-2.5-7B-Instruct (46.6%) by an average margin of 6.6%. |
EventWeave: A Dynamic Framework for Capturing Core and Supporting Events in Dialogue Systems (2026.acl-long)
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Zhengyi Zhao, Shubo Zhang, Yiming Du, Bin Liang, Baojun Wang, Zhongyang Li, Binyang Li, Kam-Fai Wong
| Challenge: | Existing dialogue systems process conversational turns in isolation, overlooking event structures that guide natural interactions. |
| Approach: | They propose a framework that explicitly models relationships between conversational events to generate more contextually appropriate dialogue responses. |
| Outcome: | Experiments on three dialogue datasets show that the proposed approach produces more natural responses while requiring less computational overhead. |
Can MLLMs Understand the Deep Implication Behind Chinese Images? (2025.acl-long)
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Chenhao Zhang, Xi Feng, Yuelin Bai, Xeron Du, Jinchang Hou, Kaixin Deng, Guangzeng Han, Qinrui Li, Bingli Wang, Jiaheng Liu, Xingwei Qu, Yifei Zhang, Qixuan Zhao, Yiming Liang, Ziqiang Liu, Feiteng Fang, Min Yang, Wenhao Huang, Chenghua Lin, Ge Zhang, Shiwen Ni
| Challenge: | MLLMs perform poorly on traditional culture images, indicating limitations in understanding high-level semantics and lacking a deep knowledge base of Chinese traditional culture. |
| Approach: | They propose to use Chinese images to assess MLLMs' higher-order perception and understanding of Chinese visual content. |
| Outcome: | The proposed model incorporates images that represent Chinese traditional culture, such as famous Chinese traditional paintings, to ensure the authenticity of the Chinese context. |
Single-to-mix Modality Alignment with Multimodal Large Language Model for Document Image Machine Translation (2025.acl-long)
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| Challenge: | Document Image Machine Translation (DIMT) faces generalization challenges due to limited training data and the complex interplay between visual and textual information. |
| Approach: | They propose a single-to-mix Modality alignment framework leveraging Multimodal Large Language Models (MLLMs) this framework aligns an imageonly encoder with multimodal representations of an MLLM pre-trained on large-scale document image datasets. |
| Outcome: | The proposed framework improves translation quality in cross-domain generalization and challenging document image scenarios. |
T2: An Adaptive Test-Time Scaling Strategy for Contextual Question Answering (2025.emnlp-main)
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Zhengyi Zhao, Shubo Zhang, Zezhong Wang, Huimin Wang, Yutian Zhao, Bin Liang, Yefeng Zheng, Binyang Li, Kam-Fai Wong, Xian Wu
| Challenge: | Existing efficient test-time scaling methods introduce budget constraints or early stop mechanisms to avoid overthinking for straightforward questions but add human bias to the reasoning process. |
| Approach: | They propose a framework that dynamically adapts reasoning depth based on question complexity. |
| Outcome: | Experimental results show that the proposed framework achieves higher accuracy than baseline methods and reduces computational overhead by up to 25.2%. |
LexicalAT: Lexical-Based Adversarial Reinforcement Training for Robust Sentiment Classification (D19-1)
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| Challenge: | Existing text classification models are fragile and sensitive to simple perturbations. |
| Approach: | They propose a generator-classifier adversarial training approach to improve classification models . they use a large-scale lexical knowledge base to generate attacking examples . |
| Outcome: | The proposed approach outperforms strong baselines and reduces test errors on neural networks. |
Document Image Machine Translation with Dynamic Multi-pre-trained Models Assembling (2024.naacl-long)
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| Challenge: | Existing TIMT tasks focus on text-line-level images. |
| Approach: | They propose to extend the existing TIMT task and introduce a new framework to translate a source document image to markdown-formatted target translation. |
| Outcome: | The proposed task aims to translate a source document image with long context and complex layout structure to markdown-formatted target translation. |
Novel Slot Detection With an Incremental Setting (2023.findings-emnlp)
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| Challenge: | Current dialogue systems face diverse user requests and rapid change domains, making quickly adapt to scenarios with previous unseen slot types becomes a major challenge. |
| Approach: | They propose an incremental novel slot detection task which separates the dialogue system to deal with novel types as two major phrases: 1) model discovers unknown slots; 2) training model to possess the capability to handle new classes. |
| Outcome: | The proposed approach overcomes catastrophic forgetting during the process of INSD and is highly effective. |
MAS-Bench: A Unified Benchmark for Shortcut-Augmented Hybrid Mobile GUI Agents (2026.acl-long)
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Pengxiang Zhao, Guangyi Liu, Yaozhen Liang, Weiqing He, Zhengxi Lu, WenHao Wang, Yuehao Huang, Yuxiang Chai, Zhaolu Kang, Yaxuan Guo, Hao Wang, Kexin Zhang, Liang Liu, Yong Liu
| Challenge: | Shortcuts such as APIs and deep-links have emerged as efficient complements to flexible GUI operations, but systematic evaluation of GUI–shortcut hybrid agents remains underexplored. |
| Approach: | They propose a benchmark that evaluates GUI-shortcut hybrid agents with a specific focus on the mobile domain. |
| Outcome: | MAS-Bench evaluates agent's ability to generate shortcuts by discovering and creating reusable, low-cost workflows. |
Open-ended Commonsense Reasoning with Unrestricted Answer Candidates (2023.findings-emnlp)
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Chen Ling, Xuchao Zhang, Xujiang Zhao, Yanchi Liu, Wei Cheng, Mika Oishi, Takao Osaki, Katsushi Matsuda, Haifeng Chen, Liang Zhao
| Challenge: | Current approaches to commonsense reasoning are limited due to limited answer scope. |
| Approach: | They propose to solve a commonsense question without a pre-defined answer scope . they leverage pre-trained language models to iteratively retrieve reasoning paths on the external knowledge base . |
| Outcome: | The proposed method achieves better performance on two commonsense benchmark datasets. |
OneEE: A One-Stage Framework for Fast Overlapping and Nested Event Extraction (2022.coling-1)
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| Challenge: | Event extraction (EE) is an essential task of information extraction, which aims to extract structured event information from unstructured text. |
| Approach: | They propose a tagging scheme and a model to form EE as word-word relation recognition using parallel grid tapping. |
| Outcome: | The proposed model achieves state-of-the-art on 3 overlapped and nested EE benchmarks and faster than baselines. |
SELF-GUARD: Empower the LLM to Safeguard Itself (2024.naacl-long)
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| Challenge: | Recent studies have investigated methods to improve the safety of large language models (LLMs) safety training involves fine-tuning the LLM with adversarial samples, which activate the LRM’s capabilities against jailbreak. |
| Approach: | They propose a safety training approach that integrates safety training and safeguards to train the LLM to perform harmfulness detection on its own outputs. |
| Outcome: | The proposed method reduces harmful output and adds a [harmful] or [harmless] tag to the end of the LLM's response. |
Length Extrapolation of Transformers: A Survey from the Perspective of Positional Encoding (2024.findings-emnlp)
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Liang Zhao, Xiachong Feng, Xiaocheng Feng, Weihong Zhong, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin, Ting Liu
| Challenge: | Existing methods to enhance length extrapolation of large language models have been developed, but a systematic survey is lacking. |
| Approach: | They propose to examine the effects of positional encoding on length extrapolation. |
| Outcome: | The proposed methods improve the extrapolation of large language models, but they are still lacking a systematic survey. |
Efficient Temporal Extrapolation of Multimodal Large Language Models with Temporal Grounding Bridge (2024.emnlp-main)
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| Challenge: | despite advances in multimodal large language models, the challenge of interpreting long-form videos remains a challenge . despite advancements in video-language benchmarks, the inefficiency in temporal grounding and limited pre-trained context window size remains . |
| Approach: | They propose a framework that bootstraps MLLMs with advanced temporal grounding capabilities and broadens their contextual scope. |
| Outcome: | The proposed framework significantly enhances the temporal capabilities of existing MLLMs. |
VoxMind: An End-to-End Agentic Spoken Dialogue System (2026.acl-long)
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Tianle Liang, Yifu Chen, Shengpeng Ji, Yijun Chen, Zhiyang Jia, Jingyu Lu, Fan Zhuo, Xueyi Pu, Yangzhuo Li, Zhou Zhao
| Challenge: | Existing research on end-to-end spoken dialogue models has focused on core perception and generation, with limited exploration of tool-augmented extensions. |
| Approach: | They propose a framework to equip end-to-end spoken dialogue models with comprehensive agentic abilities by leveraging a 470-hour AgentChat dataset. |
| Outcome: | The proposed framework outperforms Gemini-2.5-Pro on spoken agent tasks while maintaining general conversational quality. |
Aligning Cross-lingual Sentence Representations with Dual Momentum Contrast (2021.emnlp-main)
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| Challenge: | Existing work uses sentences within the same batch as negatives, which suffers from easy negatives. |
| Approach: | They propose to align sentence representations from different languages into a unified embedding space . they adapt MoCo to further improve the quality of alignment . |
| Outcome: | The proposed model achieves state-of-the-art on several tasks. |
GRAG: Graph Retrieval-Augmented Generation (2025.findings-naacl)
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| Challenge: | Naive Retrieval-Augmented Generation (RAG) focuses on individual documents during retrieval and is not suitable for networked documents. |
| Approach: | They propose a novel divide-and-conquer strategy that retrieves optimal subgraph structure in linear time. |
| Outcome: | The proposed approach outperforms current state-of-the-art methods on graph reasoning benchmarks. |
PPTC Benchmark: Evaluating Large Language Models for PowerPoint Task Completion (2024.findings-acl)
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| Challenge: | Recent evaluations of Large Language Models (LLMs) focus on their zero-shot/few-shot capabilities for basic natural language tasks and their ability to translate instructions into tool APIs. |
| Approach: | They propose a PowerPoint Task Completion benchmark to assess LLMs’ ability to create and edit PPT files based on user instructions. |
| Outcome: | The proposed system outperforms open-source and closed LLMs with 75.1% accuracy in single-turn dialogue testing but only achieves 6% session accuracy. |
ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models (2024.emnlp-main)
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Haiquan Zhao, Lingyu Li, Shisong Chen, Shuqi Kong, Jiaan Wang, Kexin Huang, Tianle Gu, Yixu Wang, Jian Wang, Liang Dandan, Zhixu Li, Yan Teng, Yanghua Xiao, Yingchun Wang
| Challenge: | Emotion Support Conversation (ESC) is a crucial application for reducing stress and providing emotional guidance. |
| Approach: | They re-organize 2,801 role-playing cards to define roles of role-players . they train a specific role- playing model called ESC-Role which behaves more like a confused person than GPT-4 . |
| Outcome: | The proposed model behaves more like a confused person than GPT-4, and the model performs better than GPLs. |
SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models (2022.acl-long)
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| Challenge: | Text-based methods lag behind graph embedding-based approaches for knowledge graph completion (KGC) |
| Approach: | They propose three types of negatives to improve contrastive learning to improve learning efficiency. |
| Outcome: | The proposed model outperforms embedding-based methods on several benchmark datasets. |
Conv-Basis: A New Paradigm for Efficient Attention Inference and Gradient Computation in Transformers (2025.findings-emnlp)
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| Challenge: | a large computational cost for attention computation in large language models is a major obstacle . |
| Approach: | They propose a convolution-like structure for attention computation using convolution matrices . they then propose an efficient approximation method to approximate the attention matrix . |
| Outcome: | The proposed method achieves nearly linear time complexity in n1+o(1) time. |
FedSpaLLM: Federated Pruning of Large Language Models (2025.naacl-long)
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| Challenge: | Existing pruning methods assume public access to calibration data, which is impractical for privacy-sensitive applications. |
| Approach: | They propose a federated learning framework for pruning LLMs that prunes models locally based on private data while accounting for system heterogeneity and communication efficiency. |
| Outcome: | The proposed framework reduces communication overhead and personalizes pruning process based on client resources in federated settings. |
Towards Infinite-Long Prefix in Transformer (2025.emnlp-main)
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| Challenge: | Prefix Learning is an empirically efficient and effective method for language models . but the theoretical understandings are limited on the performance of such methods . |
| Approach: | They propose a method that can train an ultra-long prefix in a stylized setting using the Neural Tangent Kernel framework. |
| Outcome: | The proposed method can achieve superior performance on vision, natural language, and math data. |
Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention (2025.acl-long)
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Jingyang Yuan, Huazuo Gao, Damai Dai, Junyu Luo, Liang Zhao, Zhengyan Zhang, Zhenda Xie, Yuxing Wei, Lean Wang, Zhiping Xiao, Yuqing Wang, Chong Ruan, Ming Zhang, Wenfeng Liang, Wangding Zeng
| Challenge: | Long-context modeling is crucial for next-generation language models, but high computational cost of standard attention mechanisms poses significant computational challenges. |
| Approach: | They propose a natively trained Sparse Attention mechanism that integrates algorithms with hardware-aligned optimizations to achieve efficient long-context modeling. |
| Outcome: | The proposed model maintains or exceeds Full Attention models across general benchmarks, long-context tasks, and instruction-based reasoning. |
Mitigating Language-Level Performance Disparity in mPLMs via Teacher Language Selection and Cross-lingual Self-Distillation (2024.naacl-long)
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| Challenge: | Large-scale multilingual pretrained language models (mPLMs) yield impressive performance on cross-language tasks, yet significant performance disparities exist across different languages within the same mPLm. |
| Approach: | They propose to leverage the learned knowledge from well-performing languages to guide under-performing ones within the same mPLM. |
| Outcome: | The proposed model shows that it can guide under-performing languages while minimizing language-level performance disparities across different mPLMs. |
Little Giants: Synthesizing High-Quality Embedding Data at Scale (2025.naacl-long)
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| Challenge: | Synthetic data generation is an increasingly popular way of training models without the need for large, manually labeled datasets. |
| Approach: | They propose a framework that aligns open-source small models to efficiently generate large-scale embedding data. |
| Outcome: | The proposed framework outperforms state-of-the-art embedding models by using only 1/10 of the GPT API calls. |
WatME: Towards Lossless Watermarking Through Lexical Redundancy (2024.acl-long)
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| Challenge: | Existing methods for text watermarking rely on arbitrary vocabulary partitioning during decoding, which compromises the availability of suitable tokens and significantly degrades the quality of responses. |
| Approach: | They propose a method that leverages linguistic prior knowledge of lexical redundancies in LLM vocabularies to seamlessly integrate watermarks. |
| Outcome: | The proposed approach preserves the expressive power of large language models while preserving watermark detectability. |
PCA-Bench: Evaluating Multimodal Large Language Models in Perception-Cognition-Action Chain (2024.findings-acl)
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Liang Chen, Yichi Zhang, Shuhuai Ren, Haozhe Zhao, Zefan Cai, Yuchi Wang, Peiyi Wang, Xiangdi Meng, Tianyu Liu, Baobao Chang
| Challenge: | a new multimodal decision-making benchmark evaluates the integrated capabilities of multimodal large language models. |
| Approach: | They propose a multimodal decision-making benchmark for evaluating MLLMs . they propose an automatic evaluation protocol to assess 10 prevalent ML models . |
| Outcome: | The proposed benchmark improves performance of multimodal large language models in three scenarios . the model is required to integrate multiple capabilities to make accurate decisions . |
Uncertainty Quantification for In-Context Learning of Large Language Models (2024.naacl-long)
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Chen Ling, Xujiang Zhao, Xuchao Zhang, Wei Cheng, Yanchi Liu, Yiyou Sun, Mika Oishi, Takao Osaki, Katsushi Matsuda, Jie Ji, Guangji Bai, Liang Zhao, Haifeng Chen
| Challenge: | Existing studies on in-context learning have focused on quantifying the uncertainty associated with the model's response, but they neglect the complexity of the LLM and the uniqueness of in-constitut learning. |
| Approach: | They propose a method to quantify the uncertainty associated with in-context learning and propose corresponding estimation method to quantify both types of uncertainties. |
| Outcome: | The proposed method offers an unsupervised way to understand the prediction of in-context learning in a plug-and-play fashion. |
Grammar-Based Code Representation: Is It a Worthy Pursuit for LLMs? (2025.findings-acl)
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Qingyuan Liang, Zhao Zhang, Zeyu Sun, Zheng Lin, Qi Luo, Xiao Yueyi, Yizhou Chen, Yuqun Zhang, Haotian Zhang, Lu Zhang, Chenbin Chenbin, Yingfei Xiong
| Challenge: | Existing research demonstrates the effectiveness of grammar-based code representations in small-scale models, showing their ability to reduce syntax errors and enhance performance. |
| Approach: | They develop a series of billion-scale grammar-based code representations that incorporate grammar rules into the code generation process. |
| Outcome: | Experiments on HumanEval and MBPP show that grammar-based representations reduce syntax errors and improve performance even in billion-scale models. |
A Survey of Multimodal Mathematical Reasoning: From Perception, Alignment to Reasoning (2026.acl-long)
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Tianyu Yang, Sihong Wu, Yilun Zhao, Zhenwen Liang, Lisen Dai, Chen Zhao, Minhao Cheng, Arman Cohan, Xiangliang Zhang
| Challenge: | Multimodal mathematical Reasoning (MMR) has attracted increasing attention for its ability to solve mathematical problems involving both textual and visual modalities. |
| Approach: | They review the theoretical frameworks of multimodal reasoning and examine the challenges they face in visual math tasks. |
| Outcome: | The proposed models can solve problems involving both textual and visual modalities. |
MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation (2022.naacl-main)
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| Challenge: | Existing methods for training pre-trained language models have limited practicality due to latency requirements. |
| Approach: | They propose a method that uses a Mixture-of-Experts structure to increase model capacity and inference speed. |
| Outcome: | The proposed method outperforms existing distillation methods on natural language understanding and question answering tasks. |
H-MAS: Hierarchical Multi-Agent Scheduling for Multi-Tenant LLM Serving (2026.findings-acl)
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Yuhan Liu, Cong Xu, Qi Jia, Yihua Wang, Feiyu Chen, Liang Jin, Lu Liu, Yaqian Zhao, Yuting Ding, Xiang Li
| Challenge: | Multi-tenant Model-as-a-Service (MaaS) workloads exhibit non-stationarity across multiple time scales . existing request schedulers often rely on a fixed policy that remains unchanged at runtime . |
| Approach: | They propose a hierarchical multi-agent scheduler that operates in a layered closed loop . they propose to maintain 1.2–3.0 higher Goodput than SGLang and vLLM . |
| Outcome: | Experiments show that H-MAS achieves 1.2–3.0 higher Goodput than SGLang and vLLM . it maintains more stable QoS under diverse request lengths and heterogeneous SLO targets . |
OPERA: Operation-Pivoted Discrete Reasoning over Text (2022.naacl-main)
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Yongwei Zhou, Junwei Bao, Chaoqun Duan, Haipeng Sun, Jiahui Liang, Yifan Wang, Jing Zhao, Youzheng Wu, Xiaodong He, Tiejun Zhao
| Challenge: | Existing methods to predict logical forms ignore the utilization of symbolic operations and lack reasoning ability and interpretability. |
| Approach: | They propose an operation-pivoted discrete reasoning framework that uses symbolic operations as neural modules to facilitate reasoning ability and interpretability. |
| Outcome: | Extensive experiments on DROP and RACENum datasets show the reasoning ability of OPERA. |
Self-Training with Differentiable Teacher (2022.findings-naacl)
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| Challenge: | Existing methods for self-training are interpreted as teacher-student frameworks, where the teacher generates pseudo-labels and the student makes predictions. |
| Approach: | They propose a differentiable self-training method that treats teacher-student as a Stackelberg game where a leader is always in a more advantageous position than a follower. |
| Outcome: | The proposed model outperforms existing methods on semi- and weakly-supervised learning tasks on semi and weak supervised tasks. |
Born a BabyNet with Hierarchical Parental Supervision for End-to-End Text Image Machine Translation (2024.lrec-main)
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| Challenge: | Existing research on text image machine translation (TIMT) is divided into two types: Cascade methods combine text image recognition and MT models to recognize source language text images. |
| Approach: | They propose a method which is optimized with hierarchical parental supervision to improve translation performance. |
| Outcome: | The proposed method significantly outperforms existing methods on synthetic and real-world tests on both synthetic and realistic images. |
Improving MLLM’s Document Image Machine Translation via Synchronously Self-reviewing Its OCR Proficiency (2025.findings-acl)
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| Challenge: | Multimodal Large Language Models (MLLMs) have shown strong performance in document image tasks, especially Optical Character Recognition (OCR). However, they struggle with Document Image Machine Translation (DIMT), which requires handling both cross-modal and cross-lingual challenges. |
| Approach: | They propose a novel fine-tuning paradigm that allows the model to generate OCR text before producing translation text, which allows it to leverage its strong monolingual OCR ability while learning to translate text across languages. |
| Outcome: | The proposed model can leverage its strong monolingual OCR ability while learning to translate text across languages. |
X-ray Made Simple: Lay Radiology Report Generation and Robust Evaluation (2026.findings-acl)
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Kun Zhao, Chenghao Xiao, Sixing Yan, Haoteng Tang, William K. Cheung, Noura Al Moubayed, Liang Zhan, Chenghua Lin
| Challenge: | Technical language and templated nature of professional reports hinder patient comprehension and allow models to artificially boost lexical metrics such as BLEU by reproducing common report patterns. |
| Approach: | They propose a layman's RRG framework that leverages layperson-friendly language to enhance patient accessibility and promote robust evaluation and report generation by encouraging models to focus on semantic accuracy over rigid templates. |
| Outcome: | The proposed framework improves model performance with more layman-style data, compared to templated professional language and inflated lexical scores. |
REACT: Representation Extraction And Controllable Tuning to Overcome Overfitting in LLM Knowledge Editing (2025.emnlp-main)
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Haitian Zhong, Yuhuan Liu, Ziyang Xu, Guofan Liu, Qiang Liu, Shu Wu, Zhe Zhao, Liang Wang, Tieniu Tan
| Challenge: | Large language model editing methods suffer from overfitting, where factual updates can propagate beyond their intended scope, overemphasizing the edited target even when it’s contextually inappropriate. |
| Approach: | They propose a framework for precise and controllable knowledge editing that utilizes two-phase representations and a linear transformation to compute a directional "belief shift" vector. |
| Outcome: | The proposed framework significantly reduces overfitting across nearly all evaluation metrics and on COUNTERFACT and MQuAKE. |
Source Critical Reinforcement Learning for Transferring Spoken Language Understanding to a New Language (C18-1)
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| Challenge: | a study aims to develop a language transferring system to avoid the trouble of acquiring and labeling a new big SLU corpus . general-purpose translators cannot handle the lot of semantic labels, not to mention cultural differences . a RL-based language transfer method can be used to adapt the adapted translator to a target language . |
| Approach: | They propose to use reinforcement learning to adapt a spoken language understanding model to a target language. |
| Outcome: | The proposed language transferring method improves domain classification accuracy by 22% compared with naive translation . the proposed language transfer method can be used on Chinese to English translators with more proper slot tags . |
CoreEval: Automatically Building Contamination-Resilient Datasets with Real-World Knowledge toward Reliable LLM Evaluation (2025.acl-long)
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| Challenge: | Publicly available datasets can be used to evaluate performance of large language models . however, contamination of test data can artificially inflate model performance . |
| Approach: | They propose a Contamination-resilient Evaluation strategy that updates data with real-world knowledge. |
| Outcome: | The proposed evaluation strategy can be used to update datasets with real-world knowledge. |
Curriculum Learning based Hierarchical Scoring and Analysis Framework for Question Answering Task Evaluation (2026.findings-acl)
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| Challenge: | Existing evaluation methods rely on rule-based matching with shallow semantic understanding or adopt LLM-as-a-Judge approaches that incur high cost and latency while offering limited error interpretability. |
| Approach: | They propose a curriculum learning based hierarchical framework for QA task evaluation that supports quick scoring and fine-grained error analysis. |
| Outcome: | The proposed framework outperforms baseline methods on quick scoring and error analysis tasks while being 25 faster. |
SymbolicThought: Integrating Language Models and Symbolic Reasoning for Consistent and Interpretable Human Relationship Understanding (2026.acl-demo)
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| Challenge: | Existing methods for constructing character relationships from plain text are time-consuming and low in coverage. |
| Approach: | They propose a human-in-the-loop framework that combines LLM-based extraction with symbolic reasoning. |
| Outcome: | The proposed framework improves annotation accuracy and consistency while significantly reducing time cost. |
mmE5: Improving Multimodal Multilingual Embeddings via High-quality Synthetic Data (2025.findings-acl)
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| Challenge: | Multimodal embedding models encode multimedia inputs into latent vector representations. |
| Approach: | They propose to synthesize multimodal multilingual data using a multimodal large language model . they identify three criteria for high-quality synthetic multimodal data . |
| Outcome: | The proposed model outperforms existing models on the MMEB Benchmark and the XTD benchmark. |
Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models (2026.acl-long)
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Xin Cheng, Wangding Zeng, Damai Dai, Qinyu Chen, Bingxuan Wang, Zhenda Xie, Kezhao Huang, Xingkai Yu, Zhewen Hao, Han Zhang, Yu-Kun Li, Huishuai Zhang, Dongyan Zhao, Wenfeng Liang
| Challenge: | Mixture-of-Experts (MoE) scales capacity via conditional computation, but lacks knowledge lookup primitive. |
| Approach: | They propose a conditional memory instantiated via Deep Sparse Embedding (DSE) they propose 'u-shaped scaling law' that identifies optimal balance between MoE experts and DSE memory . |
| Outcome: | The proposed model outperforms an iso-parameter and isoFLOPs MoE baseline across knowledge and reasoning benchmarks and is infrastructure-efficient. |
From Chaotic OCR Words to Coherent Document: A Fine-to-Coarse Zoom-Out Network for Complex-Layout Document Image Translation (2025.coling-main)
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| Challenge: | Document Image Translation (DIT) aims to translate documents in images from one language to another. |
| Approach: | They propose a novel end-to-end network called Zoom-out DIT to improve document translation by combining word positioning, sentence recognition and document organization. |
| Outcome: | The proposed network improves word positioning, sentence recognition and document organization, and improves translation quality. |
OS Agents: A Survey on MLLM-based Agents for Computer, Phone and Browser Use (2025.acl-long)
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Xueyu Hu, Tao Xiong, Biao Yi, Zishu Wei, Ruixuan Xiao, Yurun Chen, Jiasheng Ye, Meiling Tao, Xiangxin Zhou, Ziyu Zhao, Yuhuai Li, Shengze Xu, Shenzhi Wang, Xinchen Xu, Shuofei Qiao, Zhaokai Wang, Kun Kuang, Tieyong Zeng, Liang Wang, Jiwei Li, Yuchen Eleanor Jiang, Wangchunshu Zhou, Guoyin Wang, Keting Yin, Zhou Zhao, Hongxia Yang, Fan Wu, Shengyu Zhang, Fei Wu
| Challenge: | a new generation of (M)LLMs is enabling the creation of superintelligent AI assistants . OS Agents can complete tasks autonomously and have the potential to significantly enhance the lives of billions of users worldwide. |
| Approach: | They propose to build OS Agents that operate within operating systems' GUIs and GUIs . they examine evaluation metrics and benchmarks to identify promising directions . |
| Outcome: | The proposed agents are based on operating systems (OS) and operating systems frameworks. |
Denoising based Sequence-to-Sequence Pre-training for Text Generation (D19-1)
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| Challenge: | PoDA pre-trains encoders and decoders by denoising noise-corrupted text . Unlike encoder-only or decode-only methods, it can be used for text generation tasks without using any task-specific techniques. |
| Approach: | They propose a sequence-to-sequence (seq2sequ) pre-training method PoDA which denoises autoencoders by denoising noise-corrupted text. |
| Outcome: | The proposed method improves model performance over strong baselines without using any task-specific techniques and significantly speed up convergence. |
WavAlign: Enhancing Intelligence and Expressiveness in Spoken Dialogue Models via Adaptive Hybrid Post-Training (2026.findings-acl)
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Yifu Chen, Shengpeng Ji, Qian Chen, Tianle Liang, Yangzhuo Li, Ziqing Wang, Wen Wang, Jingyu Lu, Haoxiao Wang, Xueyi Pu, Fan Zhuo, Zhou Zhao
| Challenge: | End-to-end spoken dialogue models have higher potential ceiling in expressiveness and perceptual ability than cascaded systems. |
| Approach: | They propose a modality-aware adaptive post-training recipe that constrains preference updates to the semantic channel and improves acoustic behavior via explicit anchoring. |
| Outcome: | The proposed model improves speech quality and expressiveness across spoken dialogue benchmarks and architectures. |
Stance Detection on Social Media with Background Knowledge (2023.emnlp-main)
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| Challenge: | Existing studies of stance detection focus on learning stance information about specific targets from context, but in real-world scenarios, we usually have a certain understanding of a target when we express our stance on it. |
| Approach: | They propose to take the background knowledge of the target into account for better stance detection by categorizing it into episodic and discourse knowledge categories and a heuristic retrieval algorithm based on the topic to retrieve the Wikipedia documents relevant to the sample. |
| Outcome: | The proposed framework achieves state-of-the-art on four benchmark datasets showing that the proposed framework is able to detect stances in-target and zero-shot scenarios. |
Self-supervised Product Title Rewrite for Product Listing Ads (2022.naacl-industry)
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| Challenge: | Existing work has investigated the title optimization for Product Listing Ads (PLAs) however, little work has examined the effectiveness of this method. |
| Approach: | They propose a method to rewrite product listing ads titles without considering the fluency and information priority. |
| Outcome: | The proposed solution reduces the cost and improves CTR in the offline test and real-world online test by a large amount. |
ABSEval: An Agent-based Framework for Script Evaluation (2024.emnlp-main)
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| Challenge: | Existing studies on script evaluation of large language models (LLMs) have not evaluated scripts generated by LLMs due to their logical structure, sequential organization, and open-ended nature. |
| Approach: | They propose to use a script evaluation dataset to evaluate LLM scripts . they propose to develop an agent-based script evaluation framework ABSEval to evaluate scripts. |
| Outcome: | The proposed framework provides superior accuracy and relevance, aligning closely with human evaluation. |
CAMERO: Consistency Regularized Ensemble of Perturbed Language Models with Weight Sharing (2022.acl-long)
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| Challenge: | Existing work has resorted to sharing weights among models, but results are not affordable for real-world deployment. |
| Approach: | They propose a consistency-regularized ensemble learning approach based on perturbed models to retain ensemble benefits while maintaining a low memory cost. |
| Outcome: | The proposed approach outperforms the standard ensemble of 8 BERT-base models on the GLUE benchmark by 0.7 with a significantly smaller model size. |
AGR: Reinforced Causal Agent-Guided Self-explaining Rationalization (2024.acl-short)
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| Challenge: | Existing rationalization approaches are susceptible to degeneration due to lack of effective control over the learning direction of the model during training. |
| Approach: | They propose an agent-guided rationalization approach that guides the next step of the model based on its current training state. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on BeerAdvocate and HotelReview datasets. |
Advancing Large Language Model Attribution through Self-Improving (2024.emnlp-main)
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Lei Huang, Xiaocheng Feng, Weitao Ma, Liang Zhao, Yuchun Fan, Weihong Zhong, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin
| Challenge: | Teaching large language models to generate text with citations to evidence sources requires high-quality attribution data, which is costly and labor-intensive. |
| Approach: | They propose a framework for iteratively improving the attribution capability of large language models (LLMs) by attributing output to verifiable sources. |
| Outcome: | Experiments on three open-domain question-answering datasets show that START improves in aggregating information across multiple sources. |
Multi-Domain Neural Machine Translation with Word-Level Adaptive Layer-wise Domain Mixing (2020.acl-main)
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| Challenge: | Existing multi-domain neural machine translation models lack adaptation to individual domains. |
| Approach: | They propose a multi-domain neural machine translation model with individual modules for each domain . they use word-level, adaptive and layer-wise domain mixing to achieve this . |
| Outcome: | The proposed model outperforms existing models in several NMT tasks. |
GraphNarrator: Generating Textual Explanations for Graph Neural Networks (2025.acl-long)
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| Challenge: | Graph representation learning has garnered significant attention due to its broad applications in various domains, such as recommendation systems and social network analysis. |
| Approach: | They propose to use a generative language model to map input-output pairs to explanations reflecting the model’s decision-making process to generate a model that generates pseudo-labels that capture the model's decisions from saliency-based explanations. |
| Outcome: | Extensive experiments show that GraphNarrator produces human-preferred explanations that are faithful, concise, and human-like. |
MA-DPR: Manifold-aware Distance Metrics for Dense Passage Retrieval (2025.emnlp-main)
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| Challenge: | Empirical evidence suggests that manifold-aware distance allows DPR to leverage context from related neighboring passages. |
| Approach: | They propose a "manifold-aware" distance metric that measures query–passage distance . they propose to model the intrinsic manifold structure of passages using a nearest-neighbor graph . |
| Outcome: | Empirical evidence suggests MA-DPR outperforms Euclidean and cosine distances on OOD . it can be applied to a wide range of dense embedding and retrieval tasks . |
UNIVID: Unified Vision-Language Model for Video Moderation (2026.acl-industry)
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Kejuan Yang, Yizhuo Zhang, Mingyuan Du, Yue Zhang, Dixin Zheng, Kaili Zhao, Yang Xiao, Hanzhong Liang, Kenan Xiao
| Challenge: | Existing video moderation systems rely on fragmented black-box classification models that are difficult to maintain and lack transparency. |
| Approach: | They propose a Unified Vision-Language model for Video Moderation that generates policy-aware captions that serve as an interpretable intermediate representation. |
| Outcome: | The proposed model reduces violation leakage and overkill rate by 42.7% while reducing maintenance costs. |
Compositional Generalization for Primitive Substitutions (D19-1)
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| Challenge: | Existing approaches to encoding compositional generalization are lacking . et al., 2017) argue that neural networks lack compositional ability . |
| Approach: | They propose a method to encode compositionality in neural networks using two representations . they reduce the entropy in each representation to improve generalization . |
| Outcome: | The proposed approach improves performance on five NLP tasks including instruction learning and machine translation. |
Signer Diversity-driven Data Augmentation for Signer-Independent Sign Language Translation (2024.findings-naacl)
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| Challenge: | Existing methods for sign language translation (SLT) rely on signer identity labels, which is often impractical and costly in real-world applications. |
| Approach: | They propose a signer diversity-driven data augmentation method that can generalize to signers not encountered during training. |
| Outcome: | The proposed method achieves state-of-the-art results without relying on signer identity labels. |
TED-TTS: Training-Free Intra-Utterance Emotion and Duration Control for Text-to-Speech Synthesis (2026.acl-long)
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| Challenge: | Existing controllable Text-to-Speech methods limited to inter-utterance-level control . utterance expressiveness remains a challenge in building human-like TTS synthesis systems . |
| Approach: | They propose a training-free controllable framework for pretrained zero-shot TTS to enable intra-utterance emotion and duration expression. |
| Outcome: | The proposed framework achieves state-of-the-art intra-utterance consistency while maintaining baseline-level speech quality. |
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. |
MMEvalPro: Calibrating Multimodal Benchmarks Towards Trustworthy and Efficient Evaluation (2025.naacl-long)
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Jinsheng Huang, Liang Chen, Taian Guo, Fu Zeng, Yusheng Zhao, Bohan Wu, Ye Yuan, Haozhe Zhao, Zhihui Guo, Yichi Zhang, Jingyang Yuan, Wei Ju, Luchen Liu, Tianyu Liu, Baobao Chang, Ming Zhang
| Challenge: | Large Multimodal Models (LMMs) exhibit impressive cross-modal understanding and reasoning abilities, but many benchmarks suffer from systematic biases. |
| Approach: | They propose a benchmark to avoid Type-I errors by creating one perception question and one knowledge anchor question through a meticulous annotation process. |
| Outcome: | The proposed benchmark avoids Type-I errors while maintaining reliability of MCQ evaluations. |
SafeRAG: Benchmarking Security in Retrieval-Augmented Generation of Large Language Model (2025.acl-long)
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Xun Liang, Simin Niu, Zhiyu Li, Sensen Zhang, Hanyu Wang, Feiyu Xiong, Zhaoxin Fan, Bo Tang, Jihao Zhao, Jiawei Yang, Shichao Song, Mengwei Wang
| Challenge: | Existing approaches to integrating external knowledge into large language models (LLMs) however, the incorporation of external knowledge increases the vulnerability of LLMs . |
| Approach: | They propose a benchmark to evaluate the RAG security using a dataset . they classify attack tasks into silver noise, inter-context conflict, soft ad, and white Denial-of-Service . |
| Outcome: | The proposed benchmark evaluates the security of RAG against 14 representative RAG components. |
MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL (2025.coling-main)
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Bing Wang, Changyu Ren, Jian Yang, Xinnian Liang, Jiaqi Bai, LinZheng Chai, Zhao Yan, Qian-Wen Zhang, Di Yin, Xing Sun, Zhoujun Li
| Challenge: | Recent LLM-based Text-to-SQL methods suffer from performance degradation on “huge” databases and complex user questions that require multi-step reasoning. |
| Approach: | They propose a framework that integrates a decomposer agent and auxiliary agents to generate SQL queries from natural language text. |
| Outcome: | The proposed framework achieves comparable execution accuracy on SQL-Llama tasks compared to the baseline model. |
Multimodal Item Scoring for Natural Language Recommendation via Gaussian Process Regression with LLM Relevance Judgments (2026.findings-acl)
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Yifan Simon Liu, Qianfeng Wen, Jiazhou Liang, Mark Zhao, Justin Cui, Anton Korikov, Armin Toroghi, Junyoung Kim, Scott Sanner
| Challenge: | Existing NLRec approaches use Dense Retrieval to compute item relevance scores . DR views the request as the sole relevance label, leading to a weak proxy for query relevance. |
| Approach: | They propose to use Gaussian Process Regression to model item relevance . they propose to combine LLM with a Gauss-based kernel to model multimodal relevance judging . |
| Outcome: | The proposed approach outperforms simpler unimodal kernels and baseline methods by up to 65% on four NLRec datasets and two LLM backbones. |
Infrared-LLaVA: Enhancing Understanding of Infrared Images in Multi-Modal Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing methods for infrared modeling ignore supervisory signals of infra-modality-specific attributes, which may lead to biased understanding of in-frarea images. |
| Approach: | They propose a multi-agent generation system which transfers knowledge from visible images to generate infrared image-text pairs and infra-instructional data. |
| Outcome: | The proposed system generates infrared image-text pairs and infra-response data and is able to answer common infreas tasks with the proposed model. |
Multi-Perspective Context Aggregation for Semi-supervised Cloze-style Reading Comprehension (C18-1)
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| Challenge: | Recent studies have shown that cloze-style reading comprehension is a popular task for measuring the progress of natural language understanding. |
| Approach: | They propose a multi-perspective framework which can be seen as joint training of heterogeneous experts and aggregate context information from different perspectives. |
| Outcome: | The proposed framework achieves new state-of-the-art over previous strong baselines on a recently released cloze-test dataset. |
Memory-augmented Query Reconstruction for LLM-based Knowledge Graph Reasoning (2025.findings-acl)
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| Challenge: | Existing methods that confuse tool utilization with knowledge reasoning harm readability and give rise to tool invocation hallucinations. |
| Approach: | They propose to decouple LLM from tool invocation tasks by establishing a memory module with explicit descriptions of query statements and a query memory module to facilitate the KGQA process. |
| Outcome: | The proposed method achieves state-of-the-art on WebQSP and CWQ benchmarks. |
Mitigating Biases of Large Language Models in Stance Detection with Counterfactual Augmented Calibration (2025.naacl-long)
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Ang Li, Jingqian Zhao, Bin Liang, Lin Gui, Hui Wang, Xi Zeng, Xingwei Liang, Kam-Fai Wong, Ruifeng Xu
| Challenge: | Large language models generate biased stances due to spurious correlations and preference towards certain individuals and topics. |
| Approach: | They propose a counterfactual Augmented Calibration Network to calibrate potential bias in stance detection of large language models. |
| Outcome: | The proposed calibration network can mitigate biases of large language models, achieving state-of-the-art results. |