Papers by Ma Jun
Static Models, Dynamic World: A Unified Perspective on Temporal Perception in Large Language Models (2026.findings-acl)
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Chenhao Li, Dandan Song, Changzhi Zhou, Jun Yang, Yuhang Tian, Huipeng Ma, Guangyuan Feng, Luan Zhang, Xudong Li, Ke Duan
| Challenge: | Large language models are trained on static corpora but deployed in a dynamic world . a foundational tension remains between time and the ability to understand it . |
| Approach: | They formalize temporal queries in an information-theoretic framework based on parametric reachability of temporal premises and answers. |
| Outcome: | The proposed framework formalizes temporal queries in an information-theoretic framework based on parametric reachability of temporal premises and answers . the framework induces four temporal information regimes corresponding to internal reasoning, answer recency, premise anchoring, and genuine world indeterminacy . |
Unified Thinker: A General Reasoning Core for Image Generation (2026.acl-long)
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Sashuai Zhou, Qiang Zhou, Jijin Hu, Hanqing Yang, Yue Cao, Junpeng Ma, Yinchao Ma, Jun Song, Tiezheng Ge, Cheng Yu, Bo Zheng, Zhou Zhao
| Challenge: | generative models struggle with logic-intensive instruction following, exposing a persistent reasoning–execution gap. |
| Approach: | They propose a task-agnostic reasoning architecture for general image generation . they propose pixel-level feedback to ground the Thinker's policy in pixel feedback . |
| Outcome: | The proposed system significantly improves image reasoning and generation quality. |
MenatQA: A New Dataset for Testing the Temporal Comprehension and Reasoning Abilities of Large Language Models (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) have shown nearly saturated performance on many NLP tasks. |
| Approach: | They construct multiple sensitive factors time QA which encompasses three temporal factors . they test current mainstream LLMs with different parameter sizes . |
| Outcome: | The proposed model incorporates three temporal factors with 2,853 samples . the results show that LLMs fall behind smaller models on these factors . |
Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypes (2022.coling-1)
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| Challenge: | Existing prototypical networks for named entity recognition suffer from label dependency and tightly distributed prototypes, thus causing misclassifications. |
| Approach: | They propose an Entity-level Prototypical Network enhanced by dispersedly distributed prototypes to build entity-level prototypes and distribute them dispersionally. |
| Outcome: | The proposed system outperforms the previous models on two evaluation tasks and the Few-NERD settings in terms of overall performance. |
GeoLaux: A Benchmark for Evaluating MLLMs’ Geometry Performance on Long-Step Problems Requiring Auxiliary Lines (2026.acl-long)
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| Challenge: | Existing benchmarks for Geometry problem solving lack fine-grained evaluation for long-step problems necessitating auxiliary line construction. |
| Approach: | They present a fine-grained annotated dataset with long-step reasoning and auxiliary line construction that provides a detailed evaluation of 23 leading MLLMs. |
| Outcome: | The proposed model performs significantly worse on long-step problems than short-step ones, with 18 models showing a performance drop of over 50%. |
DB-Explore: Automated Database Exploration and Instruction Synthesis for Text-to-SQL (2025.findings-emnlp)
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| Challenge: | Recent text-to-SQL systems that use large language models struggle with complex database structures and domain-specific queries. |
| Approach: | a framework that aligns large language models with database knowledge is proposed . DB-Explore constructs database graphs to capture complex relational schemas . |
| Outcome: | a new framework outperforms existing text-to-SQL systems by outperforming existing systems. |
LaMP-Val: Large Language Models Empower Personalized Valuation in Auction (2025.findings-emnlp)
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Jie Sun, Tianyu Zhang, Houcheng Jiang, Kexin Huang, Xiang Shu, Zhibo Zhu, Lintao Ma, Xingyu Lu, Jun Zhou, Junkang Wu, Chi Luo, An Zhang, Jiancan Wu, Xiang Wang
| Challenge: | Currently, most research focuses on the bidding algorithms used within auction mechanisms. |
| Approach: | They propose a personalized valuation framework that integrates Large Language Models to incorporate personalized semantic preference into users valuation process. |
| Outcome: | The proposed framework incorporates Large Language Models to incorporate personalized semantic preference into users valuation process. |
Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset (2022.emnlp-main)
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Haolin Deng, Yanan Zhang, Yangfan Zhang, Wangyang Ying, Changlong Yu, Jun Gao, Wei Wang, Xiaoling Bai, Nan Yang, Jin Ma, Xiang Chen, Tianhua Zhou
| Challenge: | Existing EE datasets define fixed event types and design specific schemas for each of them, failing to cover diverse events emerging from the online text. |
| Approach: | They propose to use a sentence-level dataset to benchmark Open Event Extraction without restricting event types. |
| Outcome: | The proposed dataset contains more than 42,000 news titles in 34 topics collected from Chinese web pages. |
Pause or Fabricate? Training Language Models for Grounded Reasoning (2026.findings-acl)
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Yiwen Qiu, Linjuan Wu, Yizhou Liu, Yuchen Yan, Jin Ma, Xu Tan, Yao Hu, Daoxin Zhang, Wenqi Zhang, Weiming Lu, Jun Xiao, Yongliang Shen
| Challenge: | Large language models implicitly fabricate information when inputs are incomplete, causing confidence but unreliable conclusions. |
| Approach: | They propose a framework for grounded reasoning under incomplete information that decomposes reasoning into two stages . they propose stage-specific rewards to penalize hallucinations, enabling models to detect gaps, stop proactively, and resume reasoning after clarification. |
| Outcome: | The proposed framework improves premise detection and task success by 30% . it also reduces average response length by over 20% . |
Robust Preference Optimization via Dynamic Target Margins (2025.findings-acl)
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| Challenge: | Direct Preference Optimization (DPO) is an efficient method for ensuring safety and reliability in practical applications. |
| Approach: | They propose a dynamic target margin preference optimization algorithm that adjusts reward margins at the pairwise level. |
| Outcome: | The proposed method achieves an average 4.4% improvement over baselines, setting new benchmarks for state-of-the-art performance. |
End-to-End Conversational Search for Online Shopping with Utterance Transfer (2021.emnlp-main)
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Liqiang Xiao, Jun Ma, Xin Luna Dong, Pascual Martínez-Gómez, Nasser Zalmout, Chenwei Zhang, Tong Zhao, Hao He, Yaohui Jin
| Challenge: | a new study proposes a conversational search system that integrates product attributes and dialog with search . but it faces two real world challenges: imperfect product schema/knowledge and lack of training dialog data . |
| Approach: | They propose an end-to-end conversational search system that integrates search with text . they propose an utterance transfer approach that generates dialogue utterations from other domains . |
| Outcome: | The proposed system outperforms the best tested baseline in a conversational search dataset for online shopping. |
Multi-Hop Knowledge Editing via Critic-Guided Multi-Agent Reasoning (2026.findings-acl)
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Xudong Li, Yuhang Tian, Dandan Song, Zhijing Wu, Shuhao Zhang, Jun Yang, Yongyu Huo, Changzhi Zhou, Xinyu Zhang, Chenhao Li, Huipeng Ma, Luan Zhang, Yan Xu, Qian Liu
| Challenge: | Existing knowledge editing methods rely on unidirectional, feed-forward pipelines . a minor retrieval error or logical mismatch at an early hop can become a silent failure . |
| Approach: | They propose a framework for closed-loop post-edit reasoning that uses a Critic agent to verify coherence and step-wise correctness. |
| Outcome: | Experiments on MQuAKE-2002 and MQuADE-hard show that CARE effectively mitigates error propagation . a minor retrieval error or logical mismatch at an early hop can become a silent failure . |
TXtract: Taxonomy-Aware Knowledge Extraction for Thousands of Product Categories (2020.acl-main)
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| Challenge: | State-of-the-art methods for knowledge extraction are designed for a single category of product, but do not apply to real-life e-Commerce scenarios. |
| Approach: | They propose a taxonomy-aware knowledge extraction model that applies to thousands of categories organized in a hierarchical taxonomies. |
| Outcome: | The proposed model outperforms state-of-the-art methods on 4,000 categories in F1 and 15% across all categories. |
UniTabNet: Bridging Vision and Language Models for Enhanced Table Structure Recognition (2024.findings-emnlp)
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| Challenge: | Table structure recognition technology is a critical tool for processing and analyzing large volumes of tabular data. |
| Approach: | They propose a framework for table structure parsing based on the image-to-text model and a vision guider to refine the model’s capability to understand textual semantics in table images. |
| Outcome: | The proposed framework improves on a dataset of PubTabNet, PubTables1M, WTW, and iFLYTAB and will be made publicly available. |
Beyond Unimodal Shortcuts: MLLMs as Cross-Modal Reasoners for Grounded Named Entity Recognition (2026.findings-acl)
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| Challenge: | Existing approaches to GMNER use MLLMs as auxiliary tools, causing cumulative error propagation and a lack of rigorous cross-modal verification. |
| Approach: | They propose a model that enforces structured cross-modal reasoning through Multi-style Reasoning Schema Injection and Constraint-guided Verifiable Optimization. |
| Outcome: | The proposed model enforces structured cross-modal reasoning through multi-style Reasoning Schema Injection and Constraint-guided Verifiable Optimization. |
Genius: A Generalizable and Purely Unsupervised Self-Training Framework For Advanced Reasoning (2025.acl-long)
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Fangzhi Xu, Hang Yan, Chang Ma, Haiteng Zhao, Qiushi Sun, Kanzhi Cheng, Junxian He, Jun Liu, Zhiyong Wu
| Challenge: | Existing methods for enhancing LLM reasoning rely on supervisory signals . current methods rely heavily on outcome supervision and auxiliary reward models . |
| Approach: | They propose a gen-eralizable and purely unsupervised self-training framework to enhance LLM reasoning without supervision. |
| Outcome: | The proposed framework improves LLM reasoning without supervision without external supervision. |
PivotFEC: Enhancing Few-shot Factual Error Correction with a Pivot Task Approach using Large Language Models (2023.findings-emnlp)
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| Challenge: | Existing methods for Factual Error Correction (FEC) use mask-then-correct paradigms . however, the lack of datasets containing false claims has impeded progress . |
| Approach: | They propose a method that enhances few-shot FEC with a pivot task approach using large language models. |
| Outcome: | The proposed method outperforms its few-shot counterpart by 7.9 points in SARI . it improves widely-adopted SARI metrics by 11.3 compared to the best-performing methods . |
LinkNBed: Multi-Graph Representation Learning with Entity Linkage (P18-1)
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| Challenge: | Knowledge graphs have emerged as an important model for studying complex multi-relational data. |
| Approach: | They propose a deep relational learning framework that learns entity and relationship representations across multiple graphs. |
| Outcome: | The proposed framework improves on the state-of-the-art relational learning approaches and identifies entity linkage across graphs. |
Agent-Dice: Disentangling Knowledge Updates via Geometric Consensus for Agent Continual Learning (2026.findings-acl)
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| Challenge: | Large Language Model (LLM)-based agents extend the utility of LLMs by interacting with dynamic environments. |
| Approach: | They propose a parameter fusion framework based on directional consensus evaluation that disentangles knowledge updates through a two-stage process. |
| Outcome: | The proposed framework disentangles knowledge updates through a two-stage process with minimal computational overhead and parameter updates. |
MobiLoRA: Accelerating LoRA-based LLM Inference on Mobile Devices via Context-aware KV Cache Optimization (2025.acl-long)
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| Challenge: | MobiLoRA focuses on optimizing the key-value (KV) caches due to the limited computing and memory resources of mobile devices. |
| Approach: | They propose to optimize the key-value caches due to limited computing resources . they propose similarity-aware delta encoding for semantic-level contexts . |
| Outcome: | The proposed model accelerates LoRA-based LLM inference by 57.6% on mobile devices. |
Fine-Mem: Fine-Grained Feedback Alignment for Long-Horizon Memory Management (2026.acl-long)
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Weitao Ma, Xiaocheng Feng, Lei Huang, Xiachong Feng, Zhanyu Ma, Jun Xu, Jiuchong Gao, Jinghua Hao, Renqing He, Bing Qin
| Challenge: | Existing approaches to memory management rely on final task performance as the primary reward, resulting in severe reward sparsity and ineffective credit assignment. |
| Approach: | They propose a framework for fine-grained feedback alignment using a Chunk-level step reward and Evidence-Anchored Reward Attribution to redistribute global rewards based on memory items utilized as evidence in reasoning. |
| Outcome: | The proposed framework outperforms baselines and supports generalization across different model configurations and backbones. |
Interleaved Tool-Call Reasoning for Protein Function Understanding (2026.acl-long)
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| Challenge: | Recent advances in large language models have highlighted the effectiveness of chain-of-thought reasoning in symbolic domains such as mathematics and programming. |
| Approach: | They propose a tool-augmented protein reasoning agent that unifies problem decomposition, tool invocation, and grounded answer generation. |
| Outcome: | The proposed protein function understanding agent outperforms text-only reasoning models with an average performance improvement of 103%. |
Making MLLMs Blind: Adversarial Smuggling Attacks in MLLM Content Moderation (2026.findings-acl)
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Zhiheng Li, Zongyang Ma, Yuntong Pan, Ziqi Zhang, Xiaolei Lv, Bo Li, Jun Gao, Jianing Zhang, Chunfeng Yuan, Bing Li, Weiming Hu
| Challenge: | Multimodal Large Language Models (MLLMs) are increasingly being deployed as content moderators . however, they exploit the Human-AI capability gap and create adversarial environments . smuggling attacks exploit the human-AI gap and exploit the vulnerability . |
| Approach: | They construct a benchmark to evaluate the vulnerability of MLLMs as content moderators . they identify three root causes: limited capabilities of vision encoders, robustness gap in OCR . |
| Outcome: | The proposed model exploits the Human-AI capability gap and is vulnerable to smuggling attacks. |
UI-Copilot: Advancing Long-Horizon GUI Automation via Tool-Integrated Policy Optimization (2026.acl-long)
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Zhengxi Lu, Fei Tang, Guangyi Liu, Jin Ma, Kaitao Song, Xu Tan, Wenqi Zhang, Weiming Lu, Jun Xiao, Yueting Zhuang, Yongliang Shen
| Challenge: | Experimental results show that UI-Copilot-7B achieves state-of-the-art performance on challenging MemGUI-Bench, outperforming strong 7B-scale GUI agents such as GUI-Owl-7B and UITARS-1.5-7B. |
| Approach: | They propose a collaborative framework where the GUI agent focuses on task execution while a lightweight copilot provides on-demand assistance for memory retrieval and numerical computation. |
| Outcome: | The proposed framework outperforms GUI-Owl-7B and UI-TARS-1.5-7B on MemGUI-Bench and delivers 17.1% improvement on AndroidWorld over the base Qwen model. |
CIF-PT: Bridging Speech and Text Representations for Spoken Language Understanding via Continuous Integrate-and-Fire Pre-Training (2023.findings-acl)
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| Challenge: | Speech-to-text training and language model distillation are used to bridge the representations between speech and text. |
| Approach: | They propose a pre-training paradigm that integrates speech and text into a single frame-to-token alignment. |
| Outcome: | The proposed paradigm outperforms the state-of-the-art model on intent classification and slot filling tasks. |
Efficient Paths and Dense Rewards: Probabilistic Flow Reasoning for Large Language Models (2026.acl-long)
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Yan Liu, Feng Zhang, Zhanyu Ma, Jun Xu, Jiuchong Gao, Jinghua Hao, Renqing He, Han Liu, Yangdong Deng
| Challenge: | Existing approaches to mitigate inference inefficiency and optimization difficulty are fragmented and constrained by inherent trade-offs. |
| Approach: | They propose a framework that reconceptualizes discrete reasoning steps as a continuous probabilistic flow, quantifying the contribution of each step toward the ground-truth answer. |
| Outcome: | The proposed framework achieves a superior balance between inference efficiency and reasoning performance on challenging benchmarks. |
Subgraph-Guided Executable Logical Form Generation for Knowledge Base Question Answering (2026.findings-acl)
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Yuhang Tian, Dandan Song, Zhijing Wu, Changzhi Zhou, Jun Yang, Huipeng Ma, Chenhao Li, Luan Zhang, Yading Li, Xudong Li, Shenxi Liu, Jing Jiang
| Challenge: | Existing retrieval-augmented approaches focus on ignoring the structural information of the Knowledge Base (KB) and the question. |
| Approach: | They propose a structure-aware subgraph retrieval stage that ranks candidate subgraphs by aligning them with the question’s structure, along with semantic relevance. |
| Outcome: | Experiments on GrailQA, WebQSP, and GraphQuestions show that the proposed framework achieves state-of-the-art performance. |
LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement (2025.acl-long)
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Boyi Kang, Xinfa Zhu, Zihan Zhang, Zhen Ye, Mingshuai Liu, Ziqian Wang, Yike Zhu, Guobin Ma, Jun Chen, Longshuai Xiao, Chao Weng, Wei Xue, Lei Xie
| Challenge: | Recent advances in language models have demonstrated strong capabilities in semantic understanding and contextual modeling. |
| Approach: | They propose a LLaMA-based language model that incentivizes generalization capabilities for speech enhancement. |
| Outcome: | The proposed language model outperforms prior task-specific discriminative and generative models in acoustic enhancement tasks. |
3AM: An Ambiguity-Aware Multi-Modal Machine Translation Dataset (2024.lrec-main)
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Xinyu Ma, Xuebo Liu, Derek F. Wong, Jun Rao, Bei Li, Liang Ding, Lidia S. Chao, Dacheng Tao, Min Zhang
| Challenge: | Existing studies have shown that visual information in existing MMT datasets is insufficient, causing models to disregard it and overestimate their capabilities. |
| Approach: | They propose to use 3AM to create an ambiguity-aware multimodal machine translation dataset. |
| Outcome: | The proposed dataset includes more ambiguity and a greater variety of captions and images than other MMT datasets. |
Train in Vain: Functionality-Preserving Poisoning to Prevent Unauthorized Use of Code Datasets (2026.findings-acl)
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Yuan Xiao, Jiaming Wang, Yuchen Chen, Wei Song, Jun Sun, Shiqing Ma, Yanzhou Mu, Juan Zhai, Chunrong Fang, Jin Song Dong, Zhenyu Chen
| Challenge: | Existing methods for dataset poisoning require full-dataset poison, which breaks code compilability. |
| Approach: | They propose a functionality-preserving poisoning approach that injects short, compilable weak-use fragments into executed code paths. |
| Outcome: | The proposed method contaminates 10% of the dataset while maintaining 100% compilability and functional correctness. |
PILE: Pairwise Iterative Logits Ensemble for Multi-Teacher Labeled Distillation (2022.emnlp-industry)
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Lianshang Cai, Linhao Zhang, Dehong Ma, Jun Fan, Daiting Shi, Yi Wu, Zhicong Cheng, Simiu Gu, Dawei Yin
| Challenge: | Pre-trained language models have been a key part of ranking systems . knowledge distillation is widely used to maintain high performance while keeping efficient computations. |
| Approach: | They propose an algorithm to combine knowledge from multi-teachers and label information to achieve competitive performance in offline and online experiments. |
| Outcome: | The proposed method has been deployed in a real-world commercial search system. |
A Wolf in Sheep’s Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily (2024.naacl-long)
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| Challenge: | Existing methods for generating 'jailbreaks' suffer from manual design or require optimization on other white-box models, which compromises either generalization or efficiency. |
| Approach: | They propose a framework that leverages LLMs to generate effective jailbreak prompts and a generalized framework that can be used to generate prompts. |
| Outcome: | The proposed framework improves the attack success rate while reducing the time cost compared to baselines. |
UEGP: Unified Expert-Guided Pre-training for Knowledge Rekindle (2024.findings-naacl)
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Yutao Mou, Kexiang Wang, Jianhe Lin, Dehong Ma, Jun Fan, Daiting Shi, Zhicong Cheng, Gu Simiu, Dawei Yin, Weiran Xu
| Challenge: | Existing paradigms for pre-training and fine-tuning have limitations . knowledge rekindle aims to break through performance upper bounds of experts without introducing additional annotated data. |
| Approach: | They propose a new paradigm for pre-training and fine-tuning that aims to re-incorporate the fine- tuned expert model into the training cycle and break through performance upper bounds of experts. |
| Outcome: | The proposed model breaks through performance upper bounds of experts without additional annotated data. |
Stand on The Shoulders of Giants: Building JailExpert from Previous Attack Experience (2025.emnlp-main)
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Xi Wang, Songlei Jian, Shasha Li, Xiaopeng Li, Bin Ji, Ma Jun, Xiaodong Liu, Jing Wang, Jianfeng Zhang, Jie Yu, Feilong Bao, null Wangbaosheng
| Challenge: | Existing methods to generate human-aligned content with a “jailbreak prompt” are inefficient and repetitive, causing inefficiency and a lack of experience. |
| Approach: | They propose a framework that integrates past attack experiences to aid current jailbreak attempts. |
| Outcome: | The proposed framework improves both attack effectiveness and efficiency compared to the current black-box jailbreak method. |
𝜙-Decoding: Adaptive Foresight Sampling for Balanced Inference-Time Exploration and Exploitation (2025.acl-long)
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| Challenge: | Existing inference-time optimization strategies address the shortsightedness of auto-regressive generation, but the vast search space leads to excessive exploration and insufficient exploitation. |
| Approach: | They propose a decoding strategy that approximates two distributions via foresight and clustering to provide an efficient estimation of step value. |
| Outcome: | The proposed decoding strategy outperforms strong baselines in performance and efficiency. |
SepSeq: A Training-Free Framework for Long Numerical Sequence Processing in LLMs (2026.findings-acl)
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Jie Sun, Yu Liu, Lu Han, Qiwen Deng, Xiang Shu, Yang Xiao, Lintao Ma, Xingyu Lu, Jun Zhou, Pengfei Liu, Jiancan Wu, Xiang Wang
| Challenge: | Existing large-scale large-context models suffer from performance degradation when processing long numerical sequences. |
| Approach: | They propose a framework to mitigate attention dispersion by strategically inserting separator tokens into the model to recalibrat attention to local segments while preserving global context. |
| Outcome: | The proposed framework improves accuracy and reduces inference token consumption by 16.4% on 9 widely-adopted LLMs. |
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)
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Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang
| Challenge: | Existing approaches to textual robustness evaluation focus on slightly modifying the input data, which maintains the original meaning and results in a different prediction. |
| Approach: | They propose a multilingual robustness evaluation toolkit for NLP that integrates universal text transformations, task-specific transformations and adversarial attack. |
| Outcome: | The toolkit includes universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses. |
Span-based Joint Entity and Relation Extraction with Attention-based Span-specific and Contextual Semantic Representations (2020.coling-main)
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| Challenge: | Existing methods treat each span token equally important, ignoring significant features. |
| Approach: | They propose a span-based joint extraction framework with attention-based semantic representations that utilizes span-specific and contextual representations. |
| Outcome: | The proposed model outperforms existing models on ACE2005, CoNLL2004 and ADE. |
CompKBQA: Component-wise Task Decomposition for Knowledge Base Question Answering (2025.emnlp-main)
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Yuhang Tian, Dandan Song, Zhijing Wu, Pan Yang, Changzhi Zhou, Jun Yang, Hao Wang, Huipeng Ma, Chenhao Li, Luan Zhang
| Challenge: | Existing knowledge base question answering methods struggle with complex queries. |
| Approach: | They propose a framework that optimizes the process of fine-tuning a LLM for generating logical forms by enabling it to learn relevant sub-tasks like skeleton generation, topic entity generation, and relevant relations generation. |
| Outcome: | The proposed framework achieves state-of-the-art on two benchmark KBQA datasets, WebQSP and CWQ. |