Papers by Lin Ma
Hierarchy-aware Label Semantics Matching Network for Hierarchical Text Classification (2021.acl-long)
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| Challenge: | Existing methods ignore the semantic relationship between text and labels, so they cannot make full use of hierarchical information. |
| Approach: | They propose a hierarchy-aware label semantics matching network to model the semantic relationship between text and labels in a semantic matching problem. |
| Outcome: | The proposed model captures the text-label semantics matching relationship among coarse-grained labels and fine-grain labels in a hierarchy-aware manner. |
Disentangling Reasoning Capabilities from Language Models with Compositional Reasoning Transformers (2023.findings-acl)
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| Challenge: | ReasonFormer is a unified reasoning framework for complex decision-making . it is based on the dual-process theory of cognitive science, where two cognitive systems interact to form a whole reasoning process. |
| Approach: | They propose a unified reasoning framework that mirrors the modular reasoning process of humans . they decouple the representation module and the reasoning modules to capture different levels of cognition . |
| Outcome: | The proposed framework shows that humans can perform better in complex decision-making tasks. |
Tree-of-Evolution: Tree-Structured Instruction Evolution for Code Generation in Large Language Models (2025.acl-long)
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| Challenge: | Data synthesis is a key research area in large language models (LLMs). |
| Approach: | They propose a framework that models code instruction synthesis process with a tree structure and optimization-driven evolution to alleviate constraints of unidirectional synthesis and randomness-driven generation. |
| Outcome: | The proposed framework outperforms open-weight code LLMs on five widely-used benchmarks. |
Reinforcement Tuning for Detecting Stances and Debunking Rumors Jointly with Large Language Models (2024.findings-acl)
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| Challenge: | Social media has become a fertile ground for nurturing rumors and misinformation due to its lack of systematic moderation. |
| Approach: | They propose a framework to enhance the joint predictive capabilities of LLMs for stance detection and rumor verification tasks. |
| Outcome: | The proposed framework outperforms state-of-the-art methods and generalizes to non-LLMs accommodated as task models. |
Contrastive Video-Language Learning with Fine-grained Frame Sampling (2022.aacl-main)
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| Challenge: | despite recent progress in video and language representation learning, the weak or sparse correspondence between the two modalities remains a bottleneck. |
| Approach: | They propose a fine-grained contrastive objective for video frame sampling to improve cross-modal correspondence. |
| Outcome: | The proposed approach achieves state-of-the-art performance on YouCookII with long videos. |
ScaleBox: Enabling High-Fidelity and Scalable Code Verification for Large Language Models (2026.acl-demo)
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Jiasheng Zheng, Xin Zheng, Boxi Cao, Pengbo Wang, Zhengzhao Ma, Qiming Zhu, Jiazhen Jiang, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun
| Challenge: | Existing code sandboxes fail to provide accurate verification and efficiency under high-concurrency workloads. |
| Approach: | They propose a high-fidelity code verification system that provides sandbox feedback for RL training and evaluation. |
| Outcome: | The proposed system outperforms heuristic-matching baselines on LiveCodeBench and training stability on high-concurrency workloads. |
Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with Large Language Models (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) are gaining popularity due to their lack of knowledge hallucination and lack of a coherent model. |
| Approach: | They propose a self-supervised quantized representation method to compress KG structural and semantic knowledge into discrete codes that align the format of language sentences. |
| Outcome: | The proposed framework outperforms existing unsupervised methods producing more distinguishable codes on KG link prediction and triple classification tasks. |
Sarcasm-R1: Enhancing Sarcasm Detection through Focused Reasoning (2025.findings-emnlp)
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| Challenge: | Existing methods for sarcasm detection are limited by supervised learning or prompt engineering . a new approach decomposes sarcasm detection into three dimensions: language, context, and emotion . |
| Approach: | They propose a method that decomposes sarcasm detection into three dimensions: language, context, and emotion. |
| Outcome: | The proposed method outperforms state-of-the-art methods in most cases. |
SHARP: Unlocking Interactive Hallucination via Stance Transfer in Role-Playing LLMs (2025.findings-acl)
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| Challenge: | Existing studies on social interactions neglect hallucination while struggling with poor generalizability and implicit character fidelity judgments. |
| Approach: | They propose a generalizable and explicit paradigm for uncovering interactive patterns of Large Language Models across diverse worldviews by defining interactive hallucination through stance transfer and SHARP, a benchmark built by extracting relations from commonsense knowledge graphs. |
| Outcome: | The proposed paradigm is generalizable and explicit and demonstrates its effectiveness and stability. |
Beneath the Surface: Unveiling Harmful Memes with Multimodal Reasoning Distilled from Large Language Models (2023.findings-emnlp)
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| Challenge: | Existing methods for harmful meme detection ignore in-depth cognition of meme text and image . authors propose a framework for learning reasonable thoughts from LLMs for better multimodal fusion . |
| Approach: | They propose to use large language models to learn reasonable thoughts from LLMs for better multimodal fusion and lightweight fine-tuning. |
| Outcome: | The proposed approach achieves superior performance than state-of-the-art methods on the harmful meme detection task. |
Mitigating Bias for Question Answering Models by Tracking Bias Influence (2024.naacl-long)
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Mingyu Ma, Jiun-Yu Kao, Arpit Gupta, Yu-Hsiang Lin, Wenbo Zhao, Tagyoung Chung, Wei Wang, Kai-Wei Chang, Nanyun Peng
| Challenge: | Existing literature observes bias in question answering (QA) models, but there is no method to mitigate it. |
| Approach: | They propose an approach to mitigate the bias of question answering models by observing the influence of a query instance on another instance. |
| Outcome: | The proposed method reduces bias level in all 9 bias categories while maintaining comparable QA accuracy. |
Self-Evolving Multi-Agent Systems via Textual Backpropagation (2026.findings-acl)
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Xiaowen Ma, Yunpu Ma, Chenyang Lin, Sikuan Yan, Jinhe Bi, Zixuan Cao, Yijun Tian, Volker Tresp, Hinrich Schuetze
| Challenge: | Large Language Models (LLMs) have proven effective for addressing complex, high-dimensional tasks, but current approaches rely on static, manually engineered multi-agent configurations. |
| Approach: | They propose a framework that conceptualizes multi-agent collaboration as a layered neural network architecture. |
| Outcome: | The proposed framework surpasses leading multi-agent baselines under the same configurations, showing consistent performance improvements. |
ROSE: A Reward-Oriented Data Selection Framework for LLM Task-Specific Instruction Tuning (2025.findings-emnlp)
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Yang Wu, Huayi Zhang, Yizheng Jiao, Lin Ma, Xiaozhong Liu, Jinhong Yu, Dongyu Zhang, Dezhi Yu, Wei Xu
| Challenge: | Prevailing methods for task-specific instruction tuning use similarity metrics to select training data . but instruction tuning loss often fails to exhibit a monotonic relationship with actual task performance . |
| Approach: | They propose a task-specific instruction tuning method that leverages pairwise preference loss as a reward signal. |
| Outcome: | The proposed method surpasses state-of-the-art methods for task-specific instruction tuning. |
A Multimodal In-Context Tuning Approach for E-Commerce Product Description Generation (2024.lrec-main)
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| Challenge: | Existing methods for generating product descriptions from images are inaccurate and generic . e-commerce product descriptions are important for content marketing and increasing engagement . |
| Approach: | They propose a new setting for generating product descriptions from images, augmented by marketing keywords. |
| Outcome: | The proposed approach improves the accuracy and diversity of product descriptions by up to 3.3% on Rouge-L and 9.4% on D-5. |
CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal Models (2024.acl-long)
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| Challenge: | Current methods for multimodal sarcasm target identification focus on superficial indicators in an end-to-end manner, overlooking the nuanced understanding of multimodal content. |
| Approach: | They propose a multimodal sarcasm target identification framework with a coarse-to-fine paradigm by augmenting sarcasm explainability with reasoning and pre-training knowledge. |
| Outcome: | The proposed framework outperforms state-of-the-art methods and exhibits explainability in deciphering sarcasm as well. |
UFO: A UI-Focused Agent for Windows OS Interaction (2025.naacl-long)
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Chaoyun Zhang, Liqun Li, Shilin He, Xu Zhang, Bo Qiao, Si Qin, Minghua Ma, Yu Kang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
| Challenge: | UFO is a UI-Fcused agent designed to fulfill user requests tailored to Windows OS applications . it decomposes user requests using divide-and-conquer approach, enabling seamless navigation and addressing sub-tasks across multiple applications. |
| Approach: | They propose a UI-Fcused Windows OS agent that decomposes user requests using a divide-and-conquer approach and incorporates a control interaction module tailored for Windows OS. |
| Outcome: | The proposed agent decomposes user requests using divide-and-conquer approach, enabling seamless navigation and addressing sub-tasks across multiple applications. |
Knowledge-Augmented Multimodal Clinical Rationale Generation for Disease Diagnosis with Small Language Models (2025.acl-long)
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| Challenge: | Existing models struggle to balance predictive accuracy with human-understandable rationales. |
| Approach: | They propose to enhance LLMs by leveraging rationale distillation and domain knowledge injection for trustworthy multimodal rationale generation. |
| Outcome: | Experiments on real-world medical datasets show that ClinRaGen achieves state-of-the-art performance in disease diagnosis and rationale generation. |
AMO-Bench: Large Language Models Still Struggle in High School Math Competitions (2026.findings-acl)
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Junlin Liu, Shengnan An, Shuang Zhou, Dan Ma, Yehao Lin, Xinxuan Lv, Xuanlin Wang, Xiaoyu Li, Ziwen Wang, Xuezhi Cao, Xunliang Cai
| Challenge: | Existing benchmarks for mathematical reasoning are becoming less effective due to performance saturation. |
| Approach: | They propose to use a mathematical reasoning benchmark with Olympiad difficulty to evaluate top-tier LLMs. |
| Outcome: | The proposed benchmarks are cross-validated by experts to meet IMO difficulty standards and entirely original problems to prevent performance leakages from data memorization. |
CodeJudge-Eval: Can Large Language Models be Good Judges in Code Understanding? (2025.coling-main)
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| Challenge: | Recent advances in large language models (LLMs) have showcased impressive code generation capabilities, primarily evaluated through language-to-code benchmarks. |
| Approach: | They propose a benchmark to assess LLMs’ code understanding abilities from the perspective of code judging rather than code generation. |
| Outcome: | The proposed benchmark evaluates 12 well-known large language models to determine the correctness of provided code solutions. |
Mirror-Consistency: Harnessing Inconsistency in Majority Voting (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are a widely-used decoding strategy that relies on the plurality voting rule, which focuses on the most frequent answer while overlooking all other minority responses. |
| Approach: | They propose to incorporate a ‘reflective mirror’ into the self-ensemble decoding process and enables LLMs to critically examine inconsistencies among multiple generations. |
| Outcome: | The proposed method incorporates a ‘reflective mirror’ into the self-ensemble decoding process and enables LLMs to critically examine inconsistencies among multiple generations. |
GRAIT: Gradient-Driven Refusal-Aware Instruction Tuning for Effective Hallucination Mitigation (2025.findings-naacl)
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Runchuan Zhu, Xinke Jiang, Jiang Wu, Zhipeng Ma, Jiahe Song, Fengshuo Bai, Dahua Lin, Lijun Wu, Conghui He
| Challenge: | Experimental evaluations on open-ended and multiple-choice questions demonstrate GRAIT significantly outperforms existing RAIT methods in the overall performance. |
| Approach: | They propose a framework to reduce the risk of over-refusal and reduce hallucinations by rejecting unknown questions to minimize hallucinism and ensuring correct answers are not rejected. |
| Outcome: | The proposed framework outperforms existing methods on open-ended and multiple-choice questions. |
Multi-Path Transformer is Better: A Case Study on Neural Machine Translation (2022.findings-emnlp)
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| Challenge: | Extensive experiments on 12 WMT tasks show that shallower multi-path models can achieve similar or even better performance than the deeper model. |
| Approach: | They propose to use a parameter-efficient multi-path structure to fuse features extracted from different paths to achieve better performance. |
| Outcome: | The proposed model can achieve better performance with the same number of parameters than the deeper model. |
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. |
Query-as-context Pre-training for Dense Passage Retrieval (2023.emnlp-main)
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| Challenge: | Existing methods to improve passage retrieval performance by using context-supervised pre-training are weakly correlated. |
| Approach: | They propose to use query-as-context pre-training to train passage-query pairs . they evaluate the pre-trained models on large-scale passage retrieval benchmarks . |
| Outcome: | The proposed technique improves performance on large-scale passage retrieval benchmarks and out-of-domain zero-shot benchmarks. |
CartesianMoE: Boosting Knowledge Sharing among Experts via Cartesian Product Routing in Mixture-of-Experts (2025.naacl-long)
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Zhenpeng Su, Xing W, Zijia Lin, Yizhe Xiong, Minxuan Lv, Guangyuan Ma, Hui Chen, Songlin Hu, Guiguang Ding
| Challenge: | Large language models (LLMs) have been attracting much attention due to their impressive performance in all kinds of downstream tasks. |
| Approach: | They propose a mix-of-experts model that allows the model size to grow without raising training costs. |
| Outcome: | The proposed model outperforms existing models in perplexity and robustness tests. |
VISA: Retrieval Augmented Generation with Visual Source Attribution (2025.acl-long)
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| Challenge: | Existing approaches to retrieval-augmented generation primarily link generated content to document-level references, making it difficult for users to locate evidence among multiple content-rich retrieved documents. |
| Approach: | They propose a novel approach that combines answer generation with visual source attribution by leveraging large vision-language models to identify evidence and highlight exact regions that support the generated answers with bounding boxes in the retrieved document screenshots. |
| Outcome: | The proposed approach identifies evidence and highlights exact regions that support the generated answers with bounding boxes in the retrieved document screenshots. |
FACT-AUDIT: An Adaptive Multi-Agent Framework for Dynamic Fact-Checking Evaluation of Large Language Models (2025.acl-long)
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| Challenge: | Existing fact-checking evaluation methods rely on static datasets and classification metrics, which fail to evaluate justification production and uncover the nuanced limitations of LLMs. |
| Approach: | They propose a framework that adaptively and dynamically assesses LLMs’ fact-checking capabilities by incorporating justification production alongside verdict prediction. |
| Outcome: | Experiments show that the framework differentiates among state-of-the-art LLMs, providing valuable insights into model strengths and limitations in model-centric fact-checking analysis. |
Can LLMs Learn From Mistakes? An Empirical Study on Reasoning Tasks (2024.findings-emnlp)
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| Challenge: | Existing work has shown that simple learning can enhance the chain-of-thought (CoT) reasoning of large language models. |
| Approach: | They construct mistake-correction datasets to identify and correct mistakes in CoTs . they conclude that LLMs can learn from mistakes to enhance their CoT reasoning . |
| Outcome: | The proposed datasets show that LLMs can learn from mistakes to enhance their CoT reasoning performance. |
Character-level White-Box Adversarial Attacks against Transformers via Attachable Subwords Substitution (2022.emnlp-main)
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| Challenge: | Existing methods to attack transformer models are not effective at character level, but they are a natural attack scenario. |
| Approach: | They propose a character-level adversarial attack method against transformer models . they use a gradient-based method to find the most vulnerable words in a sentence . |
| Outcome: | The proposed method outperforms previous methods on sentence-level and token-level tasks. |
Found in the Middle: Permutation Self-Consistency Improves Listwise Ranking in Large Language Models (2024.naacl-long)
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| Challenge: | Large language models exhibit positional bias in how they use context, which affects listwise ranking. |
| Approach: | They propose a method to marginalize out different list orders in the prompt to produce an order-independent ranking with less positional bias. |
| Outcome: | The proposed method improves on five datasets in sorting and passage reranking by 34-52% . it marginalizes out different list orders in the prompt to produce an order-independent ranking . |
Cacheback: Speculative Decoding With Nothing But Cache (2025.emnlp-main)
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| Challenge: | a recent study shows that large language models are unable to model locality in language. |
| Approach: | They propose a training-free and model-agnostic speculative decoding method that exploits locality in language to accelerate Large Language Models. |
| Outcome: | The proposed method achieves state-of-the-art performance among comparable methods . it leverages only LRU cache tables of token n-grams to generate draft sequences . |
AdamMeme: Adaptively Probe the Reasoning Capacity of Multimodal Large Language Models on Harmfulness (2025.acl-long)
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| Challenge: | Existing models that assess mLLMs on harmful meme understanding are inaccurate and lack accuracy. |
| Approach: | They propose a framework that adaptively probes the reasoning capabilities of mLLMs . their framework systematically reveals the varying performance of different target mllms a . |
| Outcome: | The proposed framework systematically reveals the performance of different target mLLMs. |
Coarse-to-fine Few-shot Learning for Named Entity Recognition (2023.findings-acl)
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Ruotian Ma, Zhang Lin, Xuanting Chen, Xin Zhou, Junzhe Wang, Tao Gui, Qi Zhang, Xiang Gao, Yun Wen Chen
| Challenge: | Existing few-shot NER solutions do not consider sub-class discrimination and various granularity of new classes during coarse training. |
| Approach: | They propose a method that uses a cluster-based prototype loss to learn group-wise discriminative representations of coarse-grained classes and a mixture prototype loss for learning the representations. |
| Outcome: | The proposed method shows superior performance over baseline methods on in-domain and cross-domain settings with various target granularity. |
Deconvolution-Based Global Decoding for Neural Machine Translation (C18-1)
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| Challenge: | Existing models for Neural Machine Translation (NMT) use Recurrent Neural Network (RNN) to generate translation word by word following a sequential order. |
| Approach: | They propose a Neural Machine Translation (NMT) model that decodes the sequence with the guidance of its structural prediction of the target-side context. |
| Outcome: | The proposed model is more competitive compared with the state-of-the-art methods and reduces repetition with the instruction from the target-side context for decoding. |
Is LLM an Overconfident Judge? Unveiling the Capabilities of LLMs in Detecting Offensive Language with Annotation Disagreement (2025.findings-acl)
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| Challenge: | Existing studies focus on evaluating large language models' ability to handle disagreement cases. |
| Approach: | They evaluate the performance of large language models in detecting offensive language at varying levels of agreement. |
| Outcome: | The proposed model improves detection accuracy and model alignment with human judgment by using disagreement samples in training. |
M3-VQA: A Benchmark for Multimodal, Multi-Entity, Multi-Hop Visual Question Answering (2026.acl-long)
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| Challenge: | Existing knowledge-based VQA benchmarks focus on coarse-grained categories and simple reasoning over single entities. |
| Approach: | They propose a knowledge-based Visual Question Answering benchmark to enhance multimodality evaluation. |
| Outcome: | The proposed benchmark improves evaluation of multimodal large language models in fine-grained multimodal entity understanding and complex multihop reasoning. |
SciVQR: A Multidisciplinary Multimodal Benchmark for Advanced Scientific Reasoning Evaluation (2026.findings-acl)
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| Challenge: | Existing benchmarks for multimodal large language models fail to capture complexity and traceability of reasoning processes . SciVQR includes domain-specific visuals and challenges models to combine visual comprehension with reasoning. |
| Approach: | They propose a multimodal benchmark for scientific reasoning covering 54 subfields . SciVQR includes domain-specific visuals and challenges models to combine visual comprehension with reasoning . |
| Outcome: | SciVQR evaluates 54 subfields in mathematics, physics, chemistry, geography, astronomy, and biology . the results highlight the need for improved multi-step reasoning and integration of interdisciplinary knowledge . |
Gumbel Reranking: Differentiable End-to-End Reranker Optimization (2025.acl-long)
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Siyuan Huang, Zhiyuan Ma, Jintao Du, Changhua Meng, Weiqiang Wang, Jingwen Leng, Minyi Guo, Zhouhan Lin
| Challenge: | Existing distillation-based approaches suffer from training-inference misalignment and fail to capture interdependencies among candidate documents. |
| Approach: | They propose a method to optimize rerankers by learning a stochastic, document-wise Top-k attention mask using the Gumbel Trick and Relaxed Top-K Sampling. |
| Outcome: | The proposed framework minimizes the overall language loss and improves recall on hotpotQA. |
TrendSim: Simulating Trending Topics in Social Media Under Poisoning Attacks with LLM-based Multi-agent System (2025.findings-naacl)
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Zeyu Zhang, Jianxun Lian, Chen Ma, Yaning Qu, Ye Luo, Lei Wang, Rui Li, Xu Chen, Yankai Lin, Le Wu, Xing Xie, Ji-Rong Wen
| Challenge: | Trending topics bring in a new channel for poisoning attacks, resulting in negative impacts on society. |
| Approach: | They propose an LLM-based multi-agent system to simulate trending topics in social media . they propose a time-aware interaction mechanism, centralized message dissemination, and an interactive system . |
| Outcome: | The proposed system simulates trending topics under poisoning attacks on social media platforms. |
LongCLI-Bench: A Preliminary Benchmark and Study for Long-horizon Agentic Programming in Command-Line Interfaces (2026.findings-acl)
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Yukang Feng, Jianwen Sun, Zelai Yang, Jiaxin Ai, Chuanhao Li, Zizhen Li, Fanrui Zhang, Kang He, Rui Ma, Jifan Lin, Jie Sun, Yang Xiao, Sizhuo Zhou, Wenxiao Wu, Yiming Liu, Pengfei Liu, Shenglin Zhang, Kaipeng Zhang
| Challenge: | Existing benchmarks for agentic programming in long-horizon command-line interface tasks are limited by short task horizons, data contamination from GitHub scraping, and a lack of fine-grained evaluation metrics. |
| Approach: | They propose a benchmark to evaluate agentic capabilities across long-horizon command-line interface tasks. |
| Outcome: | The proposed benchmarks cover four engineering categories: from scratch, feature addition, bug fixing, and refactoring. |
PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) excel in zero-shot document ranking tasks. |
| Approach: | They propose a prompt-based re-ranking method that requires no further training but is only feasible for reranking a handful of candidates due to computational costs. |
| Outcome: | The proposed method can retrieve documents from the entire corpus without training and with a large amount of paired text data. |
Text-Guided Multi-Scale Frequency Representation Adaptation (2026.acl-long)
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| Challenge: | Existing methods for fine-tuning visual signals are limited by their size and complexity. |
| Approach: | They propose a multi-scale frequency-based fine-tuning method that integrates textual information and performs multi-level fine- tuning of visual signals in the frequency domain. |
| Outcome: | Extensive experiments on multimodal models, including CLIP and LLaVA, demonstrate that the proposed method significantly improves performance and efficiency with minimal cost and fast convergence within one epoch. |
LongFaith: Enhancing Long-Context Reasoning in LLMs with Faithful Synthetic Data (2025.findings-acl)
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| Challenge: | Long-context processing ability has emerged as a significant challenge for large language models. |
| Approach: | They propose a pipeline for synthesizing faithful long-context reasoning instruction datasets . they integrate ground truth and citation-based reasoning prompts integrating them . |
| Outcome: | The proposed pipeline eliminates distractions and improves reasoning chains. |
WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdom (2023.emnlp-main)
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| Challenge: | Existing methods for debunking fake news rely on blending of authentic and fabricated content by creators. |
| Approach: | They propose a model that detects misinformation at sentence-level using social media conversations . they use a bag-level annotation system to train the model . |
| Outcome: | The proposed model outperforms existing state-of-the-art models on three real-world benchmarks and outperformed existing state of the art models in debunking fake news at sentence and article levels. |
Weakly-Supervised Spatio-Temporally Grounding Natural Sentence in Video (P19-1)
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| Challenge: | Existing techniques for weakly-supervised spatio-temporally grounding natural sentence in video are lacking . |
| Approach: | They propose a weakly-supervised task for spatially grounding sentences in video . they extract instances from video and encode them using attentive interactor . results demonstrate superiority of their proposed task over baseline approaches . |
| Outcome: | The proposed model outperforms baseline approaches in a weakly-supervised task . it can characterize reliable instance-sentence pairs and penalize unreliable ones . |
InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model (2025.findings-acl)
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Yuhang Zang, Xiaoyi Dong, Pan Zhang, Yuhang Cao, Ziyu Liu, Shengyuan Ding, Shenxi Wu, Yubo Ma, Haodong Duan, Wenwei Zhang, Kai Chen, Dahua Lin, Jiaqi Wang
| Challenge: | Despite the promising performance of Large Vision Language Models, they sometimes generate incorrect outputs. |
| Approach: | They propose a multi-modal reward model that aligns LVLMs with human preferences. |
| Outcome: | The proposed model achieves excellent results on the latest multi-modal reward model benchmark and shows competitive performance on text-only reward model. |
ORANGE: Text-video Retrieval via Watch-time-aware Heterogeneous Graph Contrastive Learning (2023.emnlp-industry)
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Yucheng Lin, Tim Chang, Yaning Chang, Jianqiang Ma, Donghui Li, Ting Peng, Zang Li, Zhiyi Zhou, Feng Wang
| Challenge: | Existing methods for text-video retrieval focus on informative representations and delicate matching mechanisms, but real-world scenarios often involve brief, ambiguous queries and low-quality videos. |
| Approach: | They propose a novel method to learn informative embeddings for queries and videos . they use a watch-time-aware contrastive learning paradigm to capture dependencies . |
| Outcome: | The proposed method is effective in a real-world video-search service. |
Timely Machine: Awareness of Time Makes Test-Time Scaling Agentic (2026.acl-long)
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| Challenge: | Large language models (LLMs) tackle complex reasoning tasks, but test-time scaling is becoming expensive. |
| Approach: | They propose to redefine test-time as wall-clock time, where models dynamically adjust strategies based on time budgets. |
| Outcome: | The proposed model improves time budget awareness and boosts performance across Timely-Eval. |
RealMedDial: A Real Telemedical Dialogue Dataset Collected from Online Chinese Short-Video Clips (2022.coling-1)
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| Challenge: | Existing medical dialogue systems are limited by the lack of corpora and data from real scenarios. |
| Approach: | They construct a Chinese medical dialogue dataset based on real medical consultations. |
| Outcome: | The proposed dataset is applicable to a wide range of NLP tasks with respect to medical dialogue. |
WarriorCoder: Learning from Expert Battles to Augment Code Large Language Models (2025.acl-long)
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Huawen Feng, Pu Zhao, Qingfeng Sun, Can Xu, Fangkai Yang, Lu Wang, Qianli Ma, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
| Challenge: | Recent code large language models have demonstrated impressive performance on code-related tasks. |
| Approach: | They propose a paradigm that learns from expert battles to address these limitations . they create an arena where leading LLMs challenge each other with evaluations . |
| Outcome: | The proposed model improves on existing models by leveraging expert battles . it achieves state-of-the-art performance even without relying on proprietary models . |
PLAWBENCH: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice (2026.acl-long)
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Yuzhen Shi, Huanghai Liu, Yiran HU, Song Gaojie, Xu Xinran, Yubo Ma, Tianyi Tang, Li Zhang, Qingjing Chen, Feng Di, Wenbo Lv, Weiheng Wu, Kexin Yang, Sen Yang, Wei Wang, Rongyao Shi, Qiu Yuanyang, Yuemeng Qi, Zhang Jingwen, Sui Xiaoyu, Yifan Chen, Zhang Yi, An Yang, Bowen Yu, Dayiheng Liu, Junyang Lin, Weixing Shen, Bing Zhao, Charles L. A. Clarke, HU Wei
| Challenge: | Existing benchmarks for large language models (LLMs) are coarse, single-dimensional metrics and do not explicitly assess fine-grained legal reasoning. |
| Approach: | They propose a Practical Law Benchmark to evaluate large language models in real-world legal practice scenarios. |
| Outcome: | The proposed model is based on 850 questions and 13 scenarios with expert-designed evaluation rubrics. |
Dialectical Structured Reasoning for Explainable Multimodal Fake News Detection (2026.findings-acl)
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Ruichao Yang, Yufan Bian, Wei Gao, Bo-Wen Zhang, Jing Ma, Hongzhan Lin, Ziyang Luo, Xiaobin Zhu, Xu-Cheng Yin
| Challenge: | Existing fake news detection models are opaque and lack deductive transparency . a framework for dialectical structured reasoning is proposed to address this limitation . |
| Approach: | They propose a framework that model fake news detection as an explicit dialectical process over multimodal social context. |
| Outcome: | The proposed framework achieves state-of-the-art while producing transparent explanations that mirror human reasoning process. |
Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion (2025.acl-long)
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Jianqing Zhu, Huang Huang, Zhihang Lin, Juhao Liang, Zhengyang Tang, Khalid Almubarak, Mosen Alharthi, Bang An, Juncai He, Xiangbo Wu, Fei Yu, Junying Chen, Ma Zhuoheng, Yuhao Du, He Zhang, Saied Alshahrani, Emad A. Alghamdi, Lian Zhang, Ruoyu Sun, Haizhou Li, Benyou Wang, Jinchao Xu
| Challenge: | In the evolving landscape of large language models, the predominant focus has been on English and Chinese. |
| Approach: | They propose to utilize Arabic-specific vocabulary in the tokenizer to accelerate decoding. |
| Outcome: | The proposed model achieves decent performance comparable to the best Arabic LLMs across various Arabic benchmarks. |
SMARTMiner: Extracting and Evaluating SMART Goals from Low-Resource Health Coaching Notes (2025.findings-emnlp)
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Iva Bojic, Qi Chwen Ong, Stephanie Hilary Xinyi Ma, Lin Ai, Zheng Liu, Ziwei Gong, Julia Hirschberg, Andy Hau Yan Ho, Andy W. H. Khong
| Challenge: | SMARTMiner extracts specific, measurable, attainable, relevant, time-bound (SMART) goals from unstructured health coaching notes. |
| Approach: | They propose a framework for extracting and evaluating specific, measurable, attainable, relevant, time-bound (SMART) goals from unstructured health coaching notes. |
| Outcome: | The framework extracts behavior change goal spans and categorizes their SMARTness. |
PersLEARN: Research Training through the Lens of Perspective Cultivation (2023.acl-demo)
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Yu-Zhe Shi, Shiqian Li, Xinyi Niu, Qiao Xu, Jiawen Liu, Yifan Xu, Shiyu Gu, Bingru He, Xinyang Li, Xinyu Zhao, Zijian Zhao, Yidong Lyu, Zhen Li, Sijia Liu, Lin Qiu, Jinhao Ji, Lecheng Ruan, Yuxi Ma, Wenjuan Han, Yixin Zhu
| Challenge: | PersLEARN is a tool designed to facilitate the cultivation of scientific perspectives . junior researchers struggle to identify the perspectives reflected in the literature and struggle to develop their own viewpoints. |
| Approach: | They propose a tool to facilitate the cultivation of scientific perspectives by interacting with a prompt-based model and allowing students to develop their own perspectives explicitly. |
| Outcome: | The proposed tool outperforms baseline approaches across multiple domains of literature from different perspectives. |
REFLEX: Self-Refining Explainable Fact-Checking via Verdict-Anchored Style Control (2026.acl-long)
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| Challenge: | Existing methods for automated fact-checking often overlook deceptive misinformation styles in generated explanations. |
| Approach: | They propose a framework that explicitly controls reasoning style by anchoring explanations to the predicted verdict. |
| Outcome: | The proposed framework achieves state-of-the-art under LLaMA-series models with 465 samples. |
MemeArena: Automating Context-Aware Unbiased Evaluation of Harmfulness Understanding for Multimodal Large Language Models (2025.emnlp-main)
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| Challenge: | Existing evaluation approaches focus on mLLMs’ detection accuracy for binary classification tasks, which often fail to reflect the in-depth interpretive nuance of harmfulness across diverse contexts. |
| Approach: | They propose an agent-based arena-style evaluation framework that provides context-aware and unbiased assessment for mLLMs’ understanding of multimodal harmfulness. |
| Outcome: | The proposed framework reduces evaluation biases of judge agents and provides unbiased comparisons of mLLMs’ abilities to interpret multimodal harmfulness. |
Speech-Text Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment (2023.acl-long)
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Tianshu Yu, Haoyu Gao, Ting-En Lin, Min Yang, Yuchuan Wu, Wentao Ma, Chao Wang, Fei Huang, Yongbin Li
| Challenge: | Existing speech-text pre-training methods are limited to one or two specific tasks, despite their success in speech-language processing tasks. |
| Approach: | They propose a temporal position prediction task to capture the speech-text alignment . they use a textual dialog pre-training task to generalize a response selection task . |
| Outcome: | The proposed model is superior in learning speech-text alignment and multi-turn dialog context. |
CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models (2024.findings-acl)
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Yizhi Li, Ge Zhang, Xingwei Qu, Jiali Li, Zhaoqun Li, Noah Wang, Hao Li, Ruibin Yuan, Yinghao Ma, Kai Zhang, Wangchunshu Zhou, Yiming Liang, Lei Zhang, Lei Ma, Jiajun Zhang, Zuowen Li, Wenhao Huang, Chenghua Lin, Jie Fu
| Challenge: | a recent study shows that large language models have limited generalization in low-resource languages like Chinese. |
| Approach: | They propose to evaluate the zero-shot generalizability of large language models to the Chinese language . they release only half of the dataset publicly, with the remainder kept private . |
| Outcome: | The Chinese Instruction-Following Benchmark evaluates the generalizability of LLMs to the Chinese language. |
SwarmAgentic: Towards Fully Automated Agentic System Generation via Swarm Intelligence (2025.emnlp-main)
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| Challenge: | Existing agentic system generation frameworks lack autonomy, autonomy, and functionality . current frameworks are too rigid, limiting adaptability and scalability. |
| Approach: | They propose a framework that fully automates agentic system generation, optimization, and collaboration . they construct agents from scratch and jointly refine functionality and coordination . |
| Outcome: | The proposed framework outperforms ADAS on six real-world, open-ended, and exploratory tasks on the TravelPlanner benchmark. |
SCALAR: Scientific Citation-based Live Assessment of Long-context Academic Reasoning (2026.eacl-long)
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| Challenge: | Long-context understanding is a critical capability for large language models . evaluating this capability requires extensive human annotation, which is time-consuming and costly. |
| Approach: | They propose a benchmark to assess citation-grounded long-context reasoning in academic writing. |
| Outcome: | The proposed benchmark compares state-of-the-art models with human experts on two tasks . human experts achieve 90% accuracy, but most models struggle with the cloze-style task . |
Temporally Grounding Natural Sentence in Video (D18-1)
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| Challenge: | Existing methods for grounding natural sentences in video are limited to a single pass. |
| Approach: | They propose a Temporal GroundNet (TGN) method that captures the evolving fine-grained frame-by-word interactions between video and sentence to ground the segment corresponding to the sentence. |
| Outcome: | The proposed method significantly improves on the state-of-the-art methods on three public datasets and shows significant improvements in performance. |
Generating Attractive and Authentic Copywriting from Customer Reviews (2024.naacl-long)
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| Challenge: | Typical approaches to copywriting focus on product attributes, leading to dull and repetitive content. |
| Approach: | They propose to generate copywriting based on customer reviews as they provide firsthand practical experiences with products, offering a richer source of information than just product attributes. |
| Outcome: | The proposed framework outperforms baseline and zero-shot large language models in terms of both attractiveness and faithfulness. |
A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection (2022.coling-1)
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| Challenge: | Existing methods for fake news detection focus on fact-checked reports, resulting in limited coverage and debunking delays. |
| Approach: | They propose a Coarse-to-fine Cascaded Evidence-Distillation neural network for explainable fake news detection based on raw reports . they use hierarchical encoders and cascaded selectors to select most explainable sentences for verdicts on top of selected top-K reports based upon raw reports. |
| Outcome: | The proposed model outperforms baseline detection methods and generates high-quality explanations from diverse evaluation perspectives. |
CATE: A Contrastive Pre-trained Model for Metaphor Detection with Semi-supervised Learning (2021.emnlp-main)
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| Challenge: | Existing models for metaphor detection require a large amount of labeled data and are not linguistically-based. |
| Approach: | They propose a ContrAstive pre-Trained modEl (CATE) for metaphor detection with semi-supervised learning using a pre-trained model to obtain a contextual representation of target words. |
| Outcome: | The proposed model outperforms existing models on several benchmark datasets and achieves better performance against state-of-the-art models. |
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation (2021.naacl-demos)
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Qingyun Wang, Manling Li, Xuan Wang, Nikolaus Parulian, Guangxing Han, Jiawei Ma, Jingxuan Tu, Ying Lin, Ranran Haoran Zhang, Weili Liu, Aabhas Chauhan, Yingjun Guan, Bangzheng Li, Ruisong Li, Xiangchen Song, Yi Fung, Heng Ji, Jiawei Han, Shih-Fu Chang, James Pustejovsky, Jasmine Rah, David Liem, Ahmed ELsayed, Martha Palmer, Clare Voss, Cynthia Schneider, Boyan Onyshkevych
| Challenge: | a new framework to digest relevant biomedical knowledge is needed to combat COVID-19 . quantity of research results is a bottleneck, and false information promoted in publications . |
| Approach: | a team of researchers has developed a framework to extract multimedia knowledge elements from scientific literature to combat COVID-19. |
| Outcome: | a new framework extracts fine-grained multimedia knowledge elements from scientific literature . it provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence . the framework is based on a case study of drug repurposing . |
Source-free Domain Adaptation for Aspect-based Sentiment Analysis (2024.lrec-main)
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| Challenge: | Unsupervised Domain Adaptation (UDA) of the Aspect-based Sentiment Analysis task is a data mining technique that involves aspect extraction and aspect sentiment classification subtasks. |
| Approach: | They propose a framework that allows model parameter transfer, not data transfer, between different domains. |
| Outcome: | The proposed framework performs competitively with traditional unsupervised domain adaptation methods under privacy conditions. |
ALGOGEN: Tool-Generated Verifiable Traces for Reliable Algorithm Visualization (2026.findings-acl)
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| Challenge: | Recent LLM-based systems require simulation of algorithm flow and video rendering constraints. |
| Approach: | They propose a paradigm that decouples algorithm execution from rendering. |
| Outcome: | The proposed paradigm reduces execution success rates, element overlap, and inter-frame inconsistencies. |
A Span-based Dynamic Local Attention Model for Sequential Sentence Classification (2021.acl-short)
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| Challenge: | Existing methods for sentence classification ignore latent segment structure of document, in which contiguous sentences have coherent semantics. |
| Approach: | They propose a span-based dynamic local attention model that captures structural information by supervised dynamic local focus. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets. |
TIARA: Multi-grained Retrieval for Robust Question Answering over Large Knowledge Base (2022.emnlp-main)
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| Challenge: | KBQA is a challenging area for pre-trained language models due to its extensive space and complexity. |
| Approach: | They propose a model that uses multi-grained retrieval to focus on most relevant KB contexts . constrained decoding is used to control output space and reduce generation errors . |
| Outcome: | The proposed model outperforms existing models on GrailQA and WebQuestionsSP. |
On the Effectiveness of Sentence Encoding for Intent Detection Meta-Learning (2022.naacl-main)
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| Challenge: | Recent studies on few-shot intent detection have attempted to formulate the task as a meta-learning problem. |
| Approach: | They propose to modify a few-shot intent detection task to produce a non-trivially strong performance without further domain-specific adaptation. |
| Outcome: | The proposed model improves on the prototypical network variants with task-specific fine-tuning. |
Global Encoding for Abstractive Summarization (P18-2)
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| Challenge: | Existing models for abstractive summarization suffer from repetition and semantic irrelevance. |
| Approach: | They propose a global encoding framework which controls the information flow from the encoder to the decoder based on the global information of the source context. |
| Outcome: | The proposed model outperforms baseline models on the LCSTS and English Gigaword and can generate summary of higher quality and reduce repetition. |
Retrieve-and-Sample: Document-level Event Argument Extraction via Hybrid Retrieval Augmentation (2023.acl-long)
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| Challenge: | Recent studies show the effectiveness of retrieval augmentation in many generative NLP tasks. |
| Approach: | They investigate retrieval settings from the input and label distribution views . they further augment document-level EAE with pseudo demonstrations sampled from event semantic regions . |
| Outcome: | The proposed methods can augment document-level EAE with pseudo demonstrations . the methods can be used in generative NLP tasks such as dialogue response generation . |
ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)
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Ruibin Yuan, Hanfeng Lin, Yi Wang, Zeyue Tian, Shangda Wu, Tianhao Shen, Ge Zhang, Yuhang Wu, Cong Liu, Ziya Zhou, Liumeng Xue, Ziyang Ma, Qin Liu, Tianyu Zheng, Yizhi Li, Yinghao Ma, Yiming Liang, Xiaowei Chi, Ruibo Liu, Zili Wang, Chenghua Lin, Qifeng Liu, Tao Jiang, Wenhao Huang, Wenhu Chen, Jie Fu, Emmanouil Benetos, Gus Xia, Roger Dannenberg, Wei Xue, Shiyin Kang, Yike Guo
| Challenge: | Despite LLMs' impressive capabilities in musical knowledge, music reasoning remains an unsolved task. |
| Approach: | They propose an open-source large language model (LLM) that integrates intrinsic musical abilities into LLaMA2 and GPT-3.5. |
| Outcome: | The proposed model can understand and generate music with a pure text tokenizer without external multi-modal neural structures or tokenizers. |
Automatic Scene-based Topic Channel Construction System for E-Commerce (2022.emnlp-industry)
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| Challenge: | Recent scene marketing has proved effective for offline shopping. |
| Approach: | They propose a novel product form, scene-based topic channel, which consists of a list of diverse products belonging to the same usage scenario and a topic title that describes the scenario with marketing words. |
| Outcome: | The proposed system can be automated and tested on a real-world e-commerce recommendation platform. |
WebUIBench: A Comprehensive Benchmark for Evaluating Multimodal Large Language Models in WebUI-to-Code (2025.findings-acl)
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| Challenge: | Existing benchmarks for large language models focus on webpage generation outcomes. |
| Approach: | They propose a multi-view evaluation framework to evaluate MLLMs in four key areas: WebUI Perception, HTML Programming, WebUI-HTML Understanding, and WebUI to code. |
| Outcome: | The proposed framework evaluates MLLMs in four key areas: WebUI Perception, HTML Programming, WebUI-HTML Understanding, and WebUI to code. |
TriPlay-RL: Tri-Role Self-Play Reinforcement Learning for LLM Safety Alignment (2026.acl-long)
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Zhewen Tan, Wenhan Yu, Jianfeng Si, Tongxin Liu, Kaiqi Guan, Huiyan Jin, Jiawen Tao, Xiaokun Yuan, Xiangzheng Zhang, Duohe Ma, Tong Yang, Lin Sun
| Challenge: | Existing approaches to safety alignment of large language models rely on costly manual annotations or human review. |
| Approach: | They propose a closed-loop reinforcement learning framework called TriPlay-RL that enables iterative collaboration among three roles with near-zero manual annotation. |
| Outcome: | The proposed framework achieves 20%–50% improvement in adversarial effectiveness while preserving high output diversity while achieving 10%–30% gains in safety performance without degrading general reasoning capability. |
CAST: Corpus-Aware Self-similarity Enhanced Topic modelling (2025.naacl-long)
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Yanan Ma, Chenghao Xiao, Chenhan Yuan, Sabine N Van Der Veer, Lamiece Hassan, Chenghua Lin, Goran Nenadic
| Challenge: | Existing topic modelling methods encode contextual information of documents while ignoring contextual details of candidate centroid words. Existing methods are limited by the contextualization gap. |
| Approach: | They propose a topic modelling method that builds upon candidate centroid word embeddings contextualized on the dataset and a self-similarity-based method to filter out less meaningful tokens. |
| Outcome: | The proposed method significantly enhances the coherence and diversity of generated topics, and handles noisy data, outperforming strong baselines. |
Memory as Action: Autonomous Context Curation for Long-Horizon Agentic Tasks (2026.findings-acl)
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| Challenge: | Existing approaches to managing working memory are based on external mechanisms that lack awareness of the agent’s reasoning state, leading to suboptimal decisions. |
| Approach: | They propose a framework that treats working memory management as learnable policy actions and enables joint optimization of information retention and task performance through end-to-end reinforcement learning. |
| Outcome: | The proposed framework matches models 16 larger while reducing average context length by 51%, with learned strategies that adapt to model capabilities and generalize across task complexities. |
Not All Directions Matter: Towards Structured and Task-Aware Low-Rank Model Adaptation (2026.acl-long)
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Xi Xiao, Chenrui Ma, Yunbei Zhang, Chen Liu, Zhuxuanzi Wang, Yanshu Li, Lin Zhao, Guosheng Hu, Tianyang Wang, Hao Xu
| Challenge: | Low-Rank Adaptation (LoRA) is a key parameter-efficient fine-tuning method . however, its effectiveness is hampered by semantic drift and structural incoherence . |
| Approach: | They propose a low-rank Adaptation framework that tackles semantic drift and structural incoherence by pruning task-irrelevant directions. |
| Outcome: | Experiments on large language models, vision models, and vision models show that the proposed framework outperforms LoRA and advanced dynamic rank allocation and sparsity-based methods. |
MM-CRITIC: A Holistic Evaluation of Large Multimodal Models as Multimodal Critique (2025.findings-emnlp)
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| Challenge: | e MM-CRITIC is a holistic benchmark for evaluating the critique ability of Large Multimodal Models (LMMs) covering 8 main task types and over 500 tasks, covering 4471 samples. |
| Approach: | They introduce a holistic benchmark for evaluating the critique ability of Large Multimodal Models across multiple dimensions: basic, correction, and comparison. |
| Outcome: | The proposed benchmark covers 8 main task types and over 500 tasks and is composed of 4471 samples. |
Towards Low-Resource Harmful Meme Detection with LMM Agents (2024.emnlp-main)
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| Challenge: | Existing methods for harmful meme detection are limited due to the dynamic nature of memes . eliciting knowledge-revising behavior within the LMM agent is a key factor in achieving this goal . |
| Approach: | They propose an agency-driven framework for low-resource harmful meme detection . they use annotated memes to leverage label information as auxiliary signals for model . |
| Outcome: | The proposed framework achieves superior performance than state-of-the-art methods on the low-resource harmful meme detection task. |
AMR-Evol: Adaptive Modular Response Evolution Elicits Better Knowledge Distillation for Large Language Models in Code Generation (2024.emnlp-main)
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| Challenge: | proprietary large language models (LLMs) have demonstrated impressive code generation performance. |
| Approach: | They propose an adaptive module-based model that refines the direct response distillation process by modular decomposition and adaptive response evolution. |
| Outcome: | The proposed framework outperforms baseline model and code generation methods on three popular benchmarks. |
LongWeave: A Long-Form Generation Benchmark Bridging Real-World Relevance and Verifiability (2025.findings-emnlp)
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Zikai Xiao, Fei Huang, Jianhong Tu, Jianhui Wei, Wen Ma, Yuxuan Zhou, Jian Wu, Bowen Yu, Zuozhu Liu, Junyang Lin
| Challenge: | Existing benchmarks for long-form generation assess real-world queries with hard-to-verify metrics or use synthetic setups that overlook real-life intricacies. |
| Approach: | They propose a new approach that balances verifiable and real-world assessment with Target-Anchored Evaluation. |
| Outcome: | The proposed model balances real-world and verifiable assessment with Target-Anchored Evaluation (TAE) it generates queries, textual materials, and anchors based on verifier targets within real-life scenarios . |
Unifying Multimodal Retrieval via Document Screenshot Embedding (2024.emnlp-main)
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| Challenge: | Existing document retrieval pipelines require document parsing and content extraction to prepare input for indexing. |
| Approach: | They propose a retrieval paradigm that regards document screenshots as a unified input format . they leverage a large vision-language model to directly encode document screenshot into dense representations . |
| Outcome: | The proposed method outperforms existing retrieval pipelines in a text-intensive context. |
Improving Factual Consistency of News Summarization by Contrastive Preference Optimization (2024.findings-emnlp)
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Huawen Feng, Yan Fan, Xiong Liu, Ting-En Lin, Zekun Yao, Yuchuan Wu, Fei Huang, Yongbin Li, Qianli Ma
| Challenge: | Recent advances in news summarization have created problems with “hallucinations” that are factually inconsistent with the source text. |
| Approach: | They propose to disentangle LLMs’ propensities to generate faithful and fake content by adopting a probing-based specific training method to improve their capacity of distinguishing two types of propensity. |
| Outcome: | The proposed method disentangles LLMs’ propensities to generate faithful and fake content and improves their ability to distinguish between two types of propensity. |
From Behavioral Performance to Internal Competence: Interpreting Vision-Language Models with VLM-Lens (2025.emnlp-demos)
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Hala Sheta, Eric Haoran Huang, Shuyu Wu, Ilia Alenabi, Jiajun Hong, Ryker Lin, Ruoxi Ning, Daniel Wei, Jialin Yang, Jiawei Zhou, Ziqiao Ma, Freda Shi
| Challenge: | Existing vision-language models are based on exactmatch based accuracy and its derivations to evaluate performance. |
| Approach: | They propose a toolkit that supports systematic benchmarking, analysis, and interpretation of vision-language models by extracting intermediate outputs from any layer during the forward pass of open-source VLMs. |
| Outcome: | The proposed toolkit supports 16 state-of-the-art base VLMs and their over 30 variants and is extensible to accommodate new models without changing the core logic. |
Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation (2024.findings-acl)
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Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang
| Challenge: | Recent advances in Large Language Models (LLMs) have greatly advanced problem solving in diverse domains such as mathematical reasoning and knowledge reasoning. |
| Approach: | They propose a thought prompting approach called 'Everything of Thoughts' it leverages pretrained reinforcement learning and Monte Carlo Tree Search to incorporate external domain knowledge and planning capability into thoughts. |
| Outcome: | The proposed approach outperforms existing approaches on game of 24, 8-Puzzle, and Pocket Cube. |
Mem2Evolve: Towards Self-Evolving Agents via Co-Evolutionary Capability Expansion and Experience Distillation (2026.acl-long)
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Zihao Cheng, Zeming Liu, Yingyu Shan, Xinyi Wang, Xiangrong Zhu, Yunpu Ma, Hongru Wang, Yuhang Guo, Wei Lin, Yunhong Wang
| Challenge: | Existing frameworks that focus on static tools and static assets are ineffective for self-evolving agents. |
| Approach: | They propose a paradigm of co-evolutionary Capability Expansion and Experience Distillation that leverages accumulated experience to guide dynamic creation of assets. |
| Outcome: | The proposed framework improves performance in single-task and cross-task settings by 18.53% over standard LLMs, 11.80% over agents evolving solely through experience, and 6.46% over those evolving solelly through asset creation. |
Learning “O” Helps for Learning More: Handling the Unlabeled Entity Problem for Class-incremental NER (2023.acl-long)
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Ruotian Ma, Xuanting Chen, Zhang Lin, Xin Zhou, Junzhe Wang, Tao Gui, Qi Zhang, Xiang Gao, Yun Wen Chen
| Challenge: | Existing Named Entity Recognition systems are typically trained on a large-scale dataset with predefined entity classes, then deployed for entity recognition on the test data without further adaptation or refinement. |
| Approach: | They propose a representation learning method that adaptively detects entity clusters in "O" and two effective distance-based relabeling strategies for better learning the old classes. |
| Outcome: | The proposed method achieves 10.62% improvement over the baseline methods. |
ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models (2024.findings-acl)
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Haoran Luo, Haihong E, Zichen Tang, Shiyao Peng, Yikai Guo, Wentai Zhang, Chenghao Ma, Guanting Dong, Meina Song, Wei Lin, Yifan Zhu, Anh Tuan Luu
| Challenge: | Existing KBQA methods address inefficient knowledge retrieval and semantic parsing errors. |
| Approach: | They propose a generatethen-retrieve KBQA framework that generates logical form and replaces entities and relations with an unsupervised retrieval method to improve both generation and retrieval more directly. |
| Outcome: | Experimental results show that ChatKBQA achieves new state-of-the-art performance on standard KBQA datasets, WebQSP, and CWQ. |
Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks (2021.emnlp-main)
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| Challenge: | Existing methods for rumor detection are limited to the strict relation of user responses or oversimplify the conversation structure. |
| Approach: | They propose a method that reinforces interaction of user opinions while reducing negative impact imposed by irrelevant posts. |
| Outcome: | The proposed method improves performance on three Twitter datasets and can detect rumors at early stages. |
CoLaDa: A Collaborative Label Denoising Framework for Cross-lingual Named Entity Recognition (2023.acl-long)
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| Challenge: | Existing approaches to named entity recognition (NER) are limited by the cost of labeling and labeling, especially for low-resource languages. |
| Approach: | They propose a model-collaboration-based denoising scheme that enables models trained on different data sources to collaboratively denoise pseudo labels used by each other. |
| Outcome: | The proposed framework achieves superior results on benchmark datasets and can generalize to distant languages. |
CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization (2021.emnlp-main)
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| Challenge: | Existing summarization methods are prone to generate redundant and incoherent summaries, causing the performance to be worse. |
| Approach: | They propose a Chinese dataset for Customer Service Dialogue Summarization (CSDS) that provides role-oriented summaries to acquire different speakers' viewpoints. |
| Outcome: | The proposed dataset improves the abstractive summaries in two aspects . it also provides role-oriented summary to acquire different speakers’ viewpoints . |
Decomposed Meta-Learning for Few-Shot Named Entity Recognition (2022.findings-acl)
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| Challenge: | Named entity recognition systems aim at recognizing unseen entity types based on a few labeled examples. |
| Approach: | They propose a decomposed meta-learning approach to solve few-shot span detection and few- shot entity typing problems by introducing a model-agnostic meta-loop algorithm. |
| Outcome: | The proposed approach achieves superior performance over prior methods on benchmarks. |
Tables as Texts or Images: Evaluating the Table Reasoning Ability of LLMs and MLLMs (2024.findings-acl)
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| Challenge: | Recent years have witnessed an explosion of Large Language Models (LLMs), with impressive performance on various NLP tasks. |
| Approach: | They propose to use image-based representations to compare LLMs' performance on table-related tasks such as question-answering and fact-checking to determine their effectiveness. |
| Outcome: | The proposed model performs better on image-based representations than on text-based models. |
Retrieved Sequence Augmentation for Protein Representation Learning (2024.emnlp-main)
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Chang Ma, Haiteng Zhao, Lin Zheng, Jiayi Xin, Qintong Li, Lijun Wu, Zhihong Deng, Yang Lu, Qi Liu, Sheng Wang, Lingpeng Kong
| Challenge: | Using multiple sequence alignments (MSA) to extract evolutionary knowledge is limited. |
| Approach: | They propose to use multiple sequence alignments to augment protein representations . they propose to employ Retrieved Sequence Augmentation to enhance protein representation learning . |
| Outcome: | The proposed method surpasses MSA Transformer by 5% in structural and property prediction tasks while being 373 times faster. |
ShredBench: Evaluating the Semantic Reasoning Capabilities of Multimodal LLMs in Document Reconstruction (2026.findings-acl)
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Zichun Guo, Yuling Shi, Wenhao Zeng, Chao Hu, Haotian Lin, Terry Yue Zhuo, Jiawei Chen, Xiaodong Gu, Wenping Ma
| Challenge: | Empirical evaluations on state-of-the-art MLLMs reveal a significant performance gap . ML models lack the fine-grained cross-modal reasoning required to bridge visual discontinuities. |
| Approach: | They propose a benchmark that renders fragmented documents directly from Markdown to facilitate evaluation of VRDU tasks. |
| Outcome: | The proposed benchmark renders fragmented documents directly from Markdown. |
DiffCoT: Diffusion-styled Chain-of-Thought Reasoning in LLMs (2026.findings-acl)
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| Challenge: | Chain-of-Thought (CoT) reasoning improves multi-step mathematical problem solving in large language models but is vulnerable to exposure bias and error accumulation. |
| Approach: | They propose a diffusion-styled CoT framework that reformulates CoT reasoning as an iterative denoising process. |
| Outcome: | The proposed framework outperforms existing methods on three multi-step CoT reasoning benchmarks. |
Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification (D18-1)
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| Challenge: | a novel model for multi-label text classification is proposed for the task of assigning multiple labels for a given text. |
| Approach: | They propose a novel model for multi-label text classification based on sequence-to-sequence learning and a hybrid attention mechanism that extracts both the word-level and the semantic unit. |
| Outcome: | The proposed model is competitive to the baseline models and more robust to classifying low-frequency labels. |
Bag-of-Words as Target for Neural Machine Translation (P18-2)
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| Challenge: | Existing neural machine translation models only use one correct sentence as the target, and the other correct sentences are punished as the incorrect ones. |
| Approach: | They propose an approach that uses both the sentences and the bag-of-words as targets in the training stage to encourage the model to generate the potentially correct sentences that are not appeared in the train set. |
| Outcome: | The proposed model outperforms baseline models on a Chinese-English translation dataset by the BLEU score of 4.55. |
Improving Contextual Representation with Gloss Regularized Pre-training (2022.findings-naacl)
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| Challenge: | Experimental results show that the gloss regularizer module enhances word semantic similarity in pre-training. |
| Approach: | They propose an auxiliary gloss regularizer module to BERT pre-training to enhance word semantic similarity. |
| Outcome: | The proposed model improves word similarity in word-level and sentence-level representation. |
CLIO: Role-interactive Multi-event Head Attention Network for Document-level Event Extraction (2022.coling-1)
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| Challenge: | Existing methods for document-level event extraction struggle due to two intrinsic challenges: nested arguments and multiple events. |
| Approach: | They propose a role-interactive multi-event head attention network to solve two challenges . they map different events to multiple subspaces and then determine whether the current event exists . |
| Outcome: | The proposed model improves on two widely used DEE datasets on the Internet. |
A Law Reasoning Benchmark for LLM with Tree-Organized Structures including Factum Probandum, Evidence and Experiences (2025.findings-acl)
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Jiaxin Shen, Jinan Xu, Huiqi Hu, Luyi Lin, Guoyang Ma, Fei Zheng, Fandong Meng, Jie Zhou, Wenjuan Han
| Challenge: | a recent study focuses on generating impartial and interpretable judicial judgments based on established criminal fact. |
| Approach: | They propose a law reasoning schema enriched with hierarchical factum probandum, evidence, and implicit experience that enables public scrutiny and preventing bias. |
| Outcome: | The proposed schema enables public scrutiny and prevents bias in the "Intelligent Court" it employs a suite of legal analysis tools to address the challenge task. |
Disentangling Reasoning Tokens and Boilerplate Tokens For Language Model Fine-tuning (2025.findings-acl)
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| Challenge: | Existing approaches to enhance agent capabilities for Large Language Models treat all tokens equally . however, reasoning tokens versus boilerplate tokens differ in importance and learning complexity . recent research has focused on enhancing agent capabilities in large language models . |
| Approach: | They propose a Shuffle-Aware Discriminator (SHAD) for adaptive token discrimination . they propose SHAD method which adaptively emphasizes reasoning tokens during fine-tuning . |
| Outcome: | The proposed method improves performance over standard fine-tuning methods. |
Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages (2023.acl-long)
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Ayyoob ImaniGooghari, Peiqin Lin, Amir Hossein Kargaran, Silvia Severini, Masoud Jalili Sabet, Nora Kassner, Chunlan Ma, Helmut Schmid, André Martins, François Yvon, Hinrich Schütze
| Challenge: | Lack of LLMs supporting low-resource languages is a serious impediment to bringing NLP to all of the world. |
| Approach: | They create a model that scales LLMs horizontally and a corpus that covers 511 low-resource languages. |
| Outcome: | The proposed model improves on five diverse tasks across low- and high-resource languages. |
UniCorn: Towards Self-Improving Unified Multimodal Models through Self-Generated Supervision (2026.acl-long)
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Zhen Fang, Ruiyan Han, XinYu Sun, Yuchen Ma, Ziheng Wang, Yu Zeng, Zehui Chen, Lin Chen, Wenxuan Huang, Wei-Jie Xu, Yi Cao, Feng Zhao
| Challenge: | Unified Multimodal Models have achieved remarkable success in cross-modal comprehension, but a gap persists in their ability to translate internal knowledge into faithful and controllable synthesis. |
| Approach: | They propose a self-improvement framework that partitions a single UMM into three collaborative roles: Proposer, Solver, and Judge. |
| Outcome: | The proposed framework improves on TIIF, DPG, CompBench and UniCycle benchmarks. |
Precise Zero-Shot Dense Retrieval without Relevance Labels (2023.acl-long)
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| Challenge: | Existing dense retrieval systems that use semantic embedding similarities can be effective across tasks and languages. |
| Approach: | They propose to pivot through Hypothetical Document Embeddings (HyDE) given a query, HyDE first zero-shot prompts an instruction-following language model to generate a hypothetical document. |
| Outcome: | The proposed method significantly outperforms the state-of-the-art unsupervised dense retriever Contriever and shows strong performance comparable to fine-tuned retrievers across tasks and languages. |
A Deep Reinforced Sequence-to-Set Model for Multi-Label Classification (P19-1)
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| Challenge: | Multi-label classification (MLC) aims to assign multiple labels to each sample. |
| Approach: | They propose a sequence-to-set model that is trained via reinforcement learning and rewards feedback independent of the label order. |
| Outcome: | The proposed model outperforms baseline models and reduces sensitivity to label order. |
Judging with Many Minds: Do More Perspectives Mean Less Prejudice? On Bias Amplification and Resistance in Multi-Agent Based LLM-as-Judge (2025.findings-emnlp)
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Chiyu Ma, Enpei Zhang, Yilun Zhao, Wenjun Liu, Yaning Jia, Peijun Qing, Lin Shi, Arman Cohan, Yujun Yan, Soroush Vosoughi
| Challenge: | LLM-as-Judge frameworks provide scalable alternative to human evaluation . but the question of how intrinsic biases manifest in these settings remains unexplored . |
| Approach: | They conduct systematic analysis of four bias types in multi-agent LLM-as-Judge frameworks . they find debate framework amplifies biases sharply after initial debate . |
| Outcome: | The proposed frameworks amplify biases after debate and show they are stronger in meta-judge scenarios. |
BrowseComp-Plus: A Fair and Disentangled Evaluation Benchmark for Deep Search Agents (2026.acl-long)
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Zijian Chen, Xueguang Ma, Shengyao Zhuang, Ping Nie, Kai Zou, Sahel Sharifymoghaddam, Andrew Liu, Joshua Green, Kshama Patel, Ruoxi Meng, Mingyi Su, Yanxi Li, Haoran Hong, Xinyu Shi, Xuye Liu, Hosna Oyarhoseini, Nandan Thakur, Crystina Zhang, Luyu Gao, Wenhu Chen, Jimmy Lin
| Challenge: | Existing benchmarks for deep search agents rely on blackbox web search APIs . dynamic and opaque web APIs hinder reproducibility and fair comparisons - authors . |
| Approach: | They propose a benchmark that employs a fixed corpus for controlled retrieval for deep search agents. |
| Outcome: | The new benchmark shows that agents that combine large language models with retrieval tools excel at complex, reasoning-intensive queries. |
SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval (2024.findings-acl)
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Siwei Wu, Yizhi Li, Kang Zhu, Ge Zhang, Yiming Liang, Kaijing Ma, Chenghao Xiao, Haoran Zhang, Bohao Yang, Wenhu Chen, Wenhao Huang, Noura Al Moubayed, Jie Fu, Chenghua Lin
| Challenge: | Multi-modal information retrieval (MMIR) is a rapidly evolving field . current benchmarks for image-text pairings overlook the scientific domain . |
| Approach: | They develop a scientific domain-specific MMIR benchmark to evaluate image-text pairings using open-access research paper corpora. |
| Outcome: | The proposed benchmarks are based on 530K image-text pairs extracted from scientific documents with detailed captions. |
Combating Label Sparsity in Short Text Topic Modeling via Nearest Neighbor Augmentation (2024.findings-acl)
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| Challenge: | Existing topic models suffer from poor performance when applied to short text contents due to the limited length of a single topic. |
| Approach: | They propose a neural short text topic model that augments reconstruction labels with k-nearest documents to complement relevant but unobserved words. |
| Outcome: | The proposed model outperforms the state-of-the-art models on multiple public short-text datasets and can derive high-quality topics and document representations. |
MODE-LSTM: A Parameter-efficient Recurrent Network with Multi-Scale for Sentence Classification (2020.emnlp-main)
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| Challenge: | Existing models for sentence classification use linear convolution, which may not be sufficient to model the non-consecutive dependency of the phrase and may overfit the sequential information. |
| Approach: | They propose a model that extracts multi-scale n-gram features for understanding the semantic meaning of sentences by some key-phrases located at different positions. |
| Outcome: | The proposed model outperforms existing models on eight benchmark datasets and is competitive against state-of-the-art models. |
SPEAK: Spiking Neurons as an Entropy-Aware Tokenizer for Large Language Models (2026.acl-long)
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| Challenge: | Existing tokenizers fail to explicitly leverage historical tokenization results . large language models (LLMs) have demonstrated remarkable effectiveness across NLP tasks . |
| Approach: | They propose a tokenizer that integrates spiking neurons to explicitly leverage historical tokenization results. |
| Outcome: | The proposed tokenizer leverages historical tokenization results, but does not selectively leverage history based on contextual relevance. |
Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models (2024.lrec-main)
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| Challenge: | Recent advances in large language models (LLMs) have achieved great success in various NLP tasks, but the vast model parameters pose challenges in downstream fine-tuning. |
| Approach: | They propose a task-agnostic prompting strategy that analyzes each dialogue utterance before task execution to enhance LLMs' comprehension in multi-turn dialogues. |
| Outcome: | The proposed strategy outperforms other zero-shot prompts and matches or exceeds efficacy of few-shot ones. |
A Multi-Modal Context Reasoning Approach for Conditional Inference on Joint Textual and Visual Clues (2023.acl-long)
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| Challenge: | Existing methods for conditional inference on joint textual and visual clues lack multimodal context reasoning capability. |
| Approach: | They propose a multi-modal context reasoning approach that embeds textual semantics and objective image information into the pretrained language model to perform context reasoning. |
| Outcome: | The proposed approach improves on two data sets and shows 4.8% gain on the PMR. |
Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have produced significant advances in the field of recommender systems. |
| Approach: | They propose to retrieve up-to-date structure information from the knowledge graph to augment recommendations by leveraging external knowledge sources. |
| Outcome: | Experiments on a large dataset show that the proposed method is effective in enhancing LLM-based recommendations. |
Benchmarking Automated Clinical Language Simplification: Dataset, Algorithm, and Evaluation (2022.coling-1)
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| Challenge: | Existing studies to translate medical jargon into layperson-understandable language focus on accuracy and readability aspects of clinical language. |
| Approach: | They propose to construct a dataset to support automated clinical language simplification and propose a model that mimics the human annotation procedure. |
| Outcome: | The proposed model matches human annotation procedures and achieves state-of-the-art performance compared with baselines. |
DRAMA: Diverse Augmentation from Large Language Models to Smaller Dense Retrievers (2025.acl-long)
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| Challenge: | Large language models (LLMs) have shown strong effectiveness and robustness when fine-tuned as dense retrievers. |
| Approach: | They propose a training framework that leverages pruned LLMs to train smaller generalizable dense retrievers. |
| Outcome: | The proposed training framework offers better multilingual and long-context capabilities than traditional encoder-based retrievers and achieves strong performance across multiple tasks and languages. |
From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms (2026.findings-acl)
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Jinghao Luo, Yuchen Tian, Chuxue Cao, Ziyang Luo, Hongzhan Lin, Kaixin Li, Chuyi Kong, Ruichao Yang, Jing Ma
| Challenge: | Large Language Models (LLMs)-based agents have fundamentally reshaped artificial intelligence . however, the inherent statelessness of LLMs hinders their ability to maintain logical consistency across complex, multi-step tasks . |
| Approach: | They propose a framework for LLM agent memory mechanisms that formalizes the development process into three stages: storage, reflection, and experience. |
| Outcome: | The proposed framework breaks the development process into three stages . it analyzes the need for long-range consistency, challenges in dynamic environments, and the ultimate goal of continual learning. |
Cognitive Policy-Driven LLM for Diagnosis and Intervention of Cognitive Distortions in Emotional Support Conversation (2026.acl-long)
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| Challenge: | Existing models for ESC ignore cognitive distortions in help-seekers' expressions . current models provide basic emotional comfort, rather than helping help- seekers address psychological distress at a deeper cognitive level. |
| Approach: | They propose a Large Language Model framework to enhance LLMs' ability to diagnose and intervene cognitive distortions in help-seekers. |
| Outcome: | The proposed framework outperforms 15 state-of-the-art baselines in terms of distortion diagnosis accuracy, intervention strategy effectiveness, and safety risk control. |
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. |
ProMedTS: A Self-Supervised, Prompt-Guided Multimodal Approach for Integrating Medical Text and Time Series (2025.findings-acl)
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Shuai Niu, Jing Ma, Hongzhan Lin, Liang Bai, Zhihua Wang, V. W., Richard Yi Da Xu, Guo Li, Xian Yang
| Challenge: | Large language models excel at processing unstructured data, but integrating time series data with text remains a challenge. |
| Approach: | They propose a self-supervised multimodal framework that uses prompt-guided learning to unify heterogeneous data types. |
| Outcome: | The proposed framework outperforms state-of-the-art approaches on disease diagnosis tasks using real-world datasets. |
Adapt Once, Thrive with Updates: Transferable Parameter-Efficient Fine-Tuning on Evolving Base Models (2025.acl-long)
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| Challenge: | Parameter-efficient fine-tuning (PEFT) is a common method for fine- tuning large language models . however, once updated, PEFT modules suffer performance degradation on newer versions . |
| Approach: | They propose a method that enhances the PEFT module by focusing on the task-specific pattern while reducing its dependence on certain knowledge in the base model. |
| Outcome: | Experiments show that PEFT modules can maintain performance on updated models without re-tuning . the proposed approach can be used in real-world applications with large model sizes . |
HCRE: LLM-based Hierarchical Classification for Cross-Document Relation Extraction with a Prediction-then-Verification Strategy (2026.findings-acl)
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| Challenge: | Existing approaches to cross-document relation extraction (RE) focus on identifying relations between head and tail entities from single sentence or document. |
| Approach: | They propose a hierarchical relation tree-based LLM-based hierarchic classification model for cross-document relation extraction (HCRE) based on predefined relations, the model can perform hierarchically classification level by level. |
| Outcome: | The proposed model outperforms existing baselines and validates its effectiveness. |
Label Words as Local Task Vectors in In-Context Learning (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable abilities, one of the most important being in-context learning (ICL). |
| Approach: | They hypothesized that the network creates a task vector in specific positions during ICL, which can be computed by averaging across the dataset. |
| Outcome: | The proposed model can achieve zero-shot performance with dummy inputs comparable to few-shot learning by patching the global task vector. |
TKGT: Redefinition and A New Way of Text-to-Table Tasks Based on Real World Demands and Knowledge Graphs Augmented LLMs (2024.emnlp-main)
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| Challenge: | Existing studies focus on text-to-table tasks that ignore domain structures and use simple datasets to extract structured information from unstructured text. |
| Approach: | They propose a new text-to-table task that generates domain knowledge graphs from raw text using a mixed-IE method and a hybrid retrieval augmented generation method. |
| Outcome: | The proposed dataset improves compatibility with long text-processing tasks by incorporating domain knowledge graphs (KGs) classes into tables. |
Autoencoder as Assistant Supervisor: Improving Text Representation for Chinese Social Media Text Summarization (P18-2)
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| Challenge: | Existing abstractive text summarization models learn a semantic representation of the source text and the summaries from it. |
| Approach: | They evaluate the model on a popular Chinese social media dataset and compare it to other models. |
| Outcome: | The proposed model achieves state-of-the-art performance on a popular Chinese social media dataset. |
Simple and Effective Unsupervised Redundancy Elimination to Compress Dense Vectors for Passage Retrieval (2021.emnlp-main)
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| Challenge: | Dense passage retrieval improves ranking accuracy in open-domain question answering but at the cost of large space and memory requirements. |
| Approach: | They propose a simple unsupervised pipeline that includes principal component analysis (PCA), product quantization, and hybrid search to improve space efficiency. |
| Outcome: | The proposed pipeline achieves good accuracy–space trade-offs, for example, 48 compression with less than 3% drop in top-100 retrieval accuracy on average or 96 compression without drop in space requirements. |
STAND-Guard: A Small Task-Adaptive Content Moderation Model (2025.coling-industry)
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| Challenge: | Content moderation is important for developing welcoming online platforms and responsible large language models. |
| Approach: | They propose a small task-adaptive coNtent moDeration model that can be easily adapted to new or customized content moderation tasks without extensive model tuning. |
| Outcome: | The proposed model is comparable to GPT-3.5-Turbo on unseen English binary classification tasks. |
From Completion to Editing: Unlocking Context-Aware Code Infilling via Search-and-Replace Instruction Tuning (2026.acl-long)
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Jiajun Zhang, Zeyu Cui, Jiaxi Yang, Lei Zhang, Yuheng Jing, Zeyao Ma, Tianyi Bai, Zilei Wang, Qiang Liu, Liang Wang, Binyuan Hui, Junyang Lin
| Challenge: | Fill-in-the-Middle (FIM) models suffer from performance degradation and prohibitive latency. |
| Approach: | They propose a search-and-replace infilling framework that integrates agentic verification and editing into a single-pass inference process. |
| Outcome: | The proposed framework harmonizes completion tasks with the instruction-following priors of Chat LLMs, extending the paradigm from static infilling to dynamic context-aware editing. |
ScratchEval: Are GPT-4o Smarter than My Child? Evaluating Large Multimodal Models with Visual Programming Challenges (2025.naacl-short)
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| Challenge: | Recent advances in large multimodal models (LMMs) have demonstrated impressive code generation capabilities, primarily evaluated through image-to-code benchmarks. |
| Approach: | They propose a visual programming reasoning benchmark based on Scratch, a block-based visual programming language widely used in children’s programming education. |
| Outcome: | The proposed framework evaluates the visual programming ability of large multimodal models by integrating visual elements and embedded programming logic. |
COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning (2025.findings-naacl)
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Yuelin Bai, Xeron Du, Yiming Liang, Leo Jin, Junting Zhou, Ziqiang Liu, Feiteng Fang, Mingshan Chang, Tianyu Zheng, Xincheng Zhang, Nuo Ma, Zekun Moore Wang, Ruibin Yuan, Haihong Wu, Hongquan Lin, Wenhao Huang, Jiajun Zhang, Chenghua Lin, Jie Fu, Min Yang, Shiwen Ni, Ge Zhang
| Challenge: | Existing datasets for Chinese instruction tuning are not well-aligned with Chinese users’ interaction patterns. |
| Approach: | They propose to use Chinese instruction tuning datasets to improve instruction fine-tuning for Chinese users. |
| Outcome: | The proposed dataset shows that Chinese models achieve competitive performance in diverse benchmarks. |
Hard Negatives, Hard Lessons: Revisiting Training Data Quality for Robust Information Retrieval with LLMs (2025.findings-emnlp)
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| Challenge: | Using LLMs to identify false negatives improves retrieval and reranker models by 0.7-1.4 points on BEIR and by 1.7-1.8 points on AIR-Bench evaluation. |
| Approach: | They use a simple, cost-effective approach to identify and relabel false negatives in training datasets. |
| Outcome: | The proposed approach improves retrieval models by 0.7-1.4 points on BEIR and by 1.7-1.8 points on AIR-Bench evaluation. |
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. |
Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learning (2022.findings-naacl)
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| Challenge: | Existing rumor detection methods are poor at detecting false rumors about breaking news or trending topics due to the lack of training data and prior knowledge. |
| Approach: | They propose an adversarial contrastive learning framework to detect false rumors by adapting features learned from well-resourced rumor data to that of the low-resource. |
| Outcome: | The proposed framework improves on two low-resource datasets and shows superior performance . it overcomes restriction of domain and/or language usage and improves robustness . |
Choosing Transfer Languages for Cross-Lingual Learning (P19-1)
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Yu-Hsiang Lin, Chian-Yu Chen, Jean Lee, Zirui Li, Yuyan Zhang, Mengzhou Xia, Shruti Rijhwani, Junxian He, Zhisong Zhang, Xuezhe Ma, Antonios Anastasopoulos, Patrick Littell, Graham Neubig
| Challenge: | Cross-lingual transfer is a useful tool for improving performance of natural language processing (NLP) on low-resource languages. |
| Approach: | They propose to use cross-lingual transfer to improve accuracy of low-resource languages . they build models that consider features to perform prediction on such languages based on ranking problem . |
| Outcome: | The proposed model predicts good transfer languages much better than baselines considering single features in isolation. |
Issues with Entailment-based Zero-shot Text Classification (2021.acl-short)
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| Challenge: | Pre-trained BERT models with no fine-tuning can yield competitive performance against BERT fine- tuned for NLI. |
| Approach: | They propose to use any target label into a sentence of hypothesis and verify whether it could be entailed by the input. |
| Outcome: | The proposed models perform better than models fine-tuned for BERT, but the results are in general negative. |
Case2Code: Scalable Synthetic Data for Code Generation (2025.coling-main)
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Yunfan Shao, Linyang Li, Yichuan Ma, Peiji Li, Demin Song, Qinyuan Cheng, Shimin Li, Xiaonan Li, Pengyu Wang, Qipeng Guo, Hang Yan, Xipeng Qiu, Xuanjing Huang, Dahua Lin
| Challenge: | Large Language Models (LLMs) have shown outstanding breakthroughs in code generation. |
| Approach: | They propose a case-to-code induction task that exploits the expressiveness and correctness of programs by incorporating LLMs into their training. |
| Outcome: | The proposed task improves distribution case-to-code induction and various coding generation tasks. |
A Neural Divide-and-Conquer Reasoning Framework for Image Retrieval from Linguistically Complex Text (2023.acl-long)
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| Challenge: | Pretrained Vision-Language Models (VLMs) have achieved remarkable performance in image retrieval from text, but their performance drops drastically when confronted with linguistically complex texts. |
| Approach: | They propose an end-to-end Neural Divide-and-Conquer Reasoning framework for linguistically complex texts that they struggle to comprehend. |
| Outcome: | The proposed framework significantly improves performance in complex image-text reasoning problem. |
CuBridge: An LLM-Based Framework for Understanding and Reconstructing High-Performance Attention Kernels (2026.acl-long)
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Xing Ma, Yangjie Zhou, Wu Sun, Zihan Liu, Jingwen Leng, Yun Lin, Shixuan Sun, Minyi Guo, Jin Song Dong
| Challenge: | Existing approaches to support diverse attention variants trade performance for flexibility . expert-written kernels achieve high efficiency but are difficult to adapt . |
| Approach: | They propose a framework that adapts expert-written attention kernels to GPUs . they use a structured lift–transfer–lower workflow to make execution explicit . |
| Outcome: | The proposed framework outperforms existing frameworks and compilers on diverse variants and GPU platforms. |