Papers by Min Wang
Team-Based Self-Play With Dual Adaptive Weighting for Fine-Tuning LLMs (2026.acl-long)
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| Challenge: | Recent self-training approaches have reduced reliance on human-labeled data, which limits their scalability. |
| Approach: | They propose a team-based self-play algorithm that iteratively refines alignment without additional human supervision. |
| Outcome: | The proposed algorithm outperforms baselines and LLM benchmarks in the self-supervised setting. |
DiffuSpec: Unlocking Diffusion Language Models for Speculative Decoding (2026.findings-acl)
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| Challenge: | Autoregressive (AR) decoding in large language models is latency-bounded by strictly sequential token generation. |
| Approach: | They propose a diffusion-based drafter that proposes multi-token candidates and then verifies them in parallel by the target model. |
| Outcome: | The proposed drafter generates multi-token proposals in a single forward pass while remaining compatible with standard AR verifiers. |
MTSA: Multi-turn Safety Alignment for LLMs through Multi-round Red-teaming (2025.acl-long)
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| Challenge: | Existing jailbreak techniques rely on single-round interactions, pro-Corresponding author. |
| Approach: | They propose a multi-turn safety alignment framework to address the challenge of securing large language models in multi-round interactions. |
| Outcome: | The proposed framework exhibits state-of-the-art attack capabilities while improving safety performance on safety benchmarks. |
Improving In-Context Learning with Prediction Feedback for Sentiment Analysis (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have achieved promising results in sentiment analysis through the in-context learning paradigm. |
| Approach: | They propose a framework that incorporates prior predictions and feedback to improve sentiment understanding by incorporating prior feedback and leveraging a feedback-driven prompt. |
| Outcome: | The proposed framework improves on nine sentiment analysis datasets with an average improvement of 5.95% over conventional methods. |
CHIFRAUD: A Long-term Web Text Dataset for Chinese Fraud Detection (2025.coling-main)
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| Challenge: | Detecting fraudulent online text is essential as they exploit human greed and deceive individuals. |
| Approach: | They propose to build a long-term dataset of Chinese fraudulent texts collected over 12 months. |
| Outcome: | The proposed dataset includes 59,106 entries extracted from billions of web pages and includes large language model-based detectors and pre-trained language model approaches. |
An In-depth Study on Internal Structure of Chinese Words (2021.acl-long)
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Chen Gong, Saihao Huang, Houquan Zhou, Zhenghua Li, Min Zhang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan
| Challenge: | Unlike English letters, Chinese characters have rich and specific meanings. |
| Approach: | They propose to model Chinese words' internal structures as dependency trees with 11 labels for distinguishing syntactic relationships. |
| Outcome: | The proposed model of Chinese word-internal structures shows it can be used to parse sentences . it shows that the model can be applied to a sentence-level task with a competitive dependency parser. |
Achieving Stronger Generation via Simple Contrastive Tuning (2024.findings-emnlp)
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| Challenge: | Recent years have witnessed remarkable progress in large language models (LLMs). |
| Approach: | They propose a framework for contrastive decoding to enhance instruction-tuned models. |
| Outcome: | The proposed framework improves model performance without additional data or computational resources. |
WikiDiverse: A Multimodal Entity Linking Dataset with Diversified Contextual Topics and Entity Types (2022.acl-long)
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| Challenge: | Multimodal Entity Linking (MEL) is an essential task for many multimodal applications. |
| Approach: | They propose to use a human-annotated Wikipedia-based multimodal entity linking dataset to improve the quality of existing MEL models. |
| Outcome: | The proposed model uses the visual information of images more effectively than existing models. |
Towards Better Hierarchical Text Classification with Data Generation (2023.findings-acl)
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| Challenge: | Existing methods to improve hierarchical text classification are expensive and lack high-quality labeled data. |
| Approach: | They propose a hierarchical text classification framework that can achieve both label controllability and text diversity by extracting high-quality hierarchic label information. |
| Outcome: | The proposed method can achieve label controllability and text diversity by extracting high-quality hierarchical label information. |
Multimodal Reasoning with Multimodal Knowledge Graph (2024.acl-long)
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| Challenge: | Multimodal reasoning with large language models (LLMs) often suffers from hallucinations and the presence of deficient or outdated knowledge within LLMs. |
| Approach: | They propose a multimodal reasoning method that leverages multimodal knowledge graphs to learn rich and semantic knowledge across modalities. |
| Outcome: | The proposed method outperforms state-of-the-art models on multimodal question answering and multimodal analogy reasoning tasks while training on only a small fraction of parameters. |
Exploring the Impact of Personality Traits on LLM Toxicity and Bias (2025.emnlp-main)
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| Challenge: | anthropomorphic LLMs are being developed to serve diversified roles, but content safety concerns remain regarding their toxicity and toxicity. |
| Approach: | They propose to assign personality traits to large language models (LLMs) to reduce toxic language and social biases in their outputs by using the widely accepted HEXACO personality framework developed in social psychology. |
| Outcome: | The proposed model is able to perform on three toxic and bias benchmarks and shows that assigning personality traits reduces bias and toxicity similar to humans’ correlations between personality traits and toxic behaviors. |
Your Language Model Can Secretly Write Like Humans: Contrastive Paraphrase Attacks on LLM-Generated Text Detectors (2025.emnlp-main)
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Hao Fang, Jiawei Kong, Tianqu Zhuang, Yixiang Qiu, Kuofeng Gao, Bin Chen, Shu-Tao Xia, Yaowei Wang, Min Zhang
| Challenge: | Existing methods to detect large language models (LLMs) generated for plagiarism use paraphrases to rewrite them to evade detection. |
| Approach: | They propose a training-free method that effectively fools text detectors using off-the-shelf LLMs by rewriting them to evade detection. |
| Outcome: | The proposed method deceives text detectors using off-the-shelf LLMs by rewriting them to produce human-like sentences that are less discernible by detectors. |
A Unified Span-Based Approach for Opinion Mining with Syntactic Constituents (2021.naacl-main)
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| Challenge: | Existing methods for fine-grained opinion mining (OM) are based on span-based annotations, but they are not effective. |
| Approach: | They propose a unified span-based approach for the end-to-end OM setting using syntactic constituents and multi-task learning to integrate them into the proposed model. |
| Outcome: | The proposed approach achieves significant improvements over previous work on the MPQA 2.0 dataset and reduces the number of wrongly-predicted opinion expressions and roles. |
Reducing Spurious Correlations in Aspect-based Sentiment Analysis with Explanation from Large Language Models (2023.findings-emnlp)
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| Challenge: | Aspect-based sentiment analysis models are susceptible to learning spurious correlations between words . a recent study shows that feature engineering is time-consuming and costly . |
| Approach: | They propose to use a template to prompt LLMs to generate an appropriate explanation for the sentiment polarity of each aspect to reduce spurious correlations. |
| Outcome: | The proposed methods improve ABSA models and their generalization ability. |
A User-Centric Multi-Intent Benchmark for Evaluating Large Language Models (2024.emnlp-main)
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| Challenge: | Existing benchmarks focus on specific predefined model abilities, such as world knowledge, reasoning, etc., making it difficult for users to determine which LLM best suits their particular needs. |
| Approach: | They propose to evaluate large language models from a user-centric perspective and use real-world use cases to identify their effectiveness under distinct intents. |
| Outcome: | The proposed benchmarks achieve a correlation between human preference and the user-reported scenarios and human intents. |
Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing studies focus on how to effectively exploit bidirectional global contexts in neural machine translation models. |
| Approach: | They propose a Confidence Based Bidirectional Global Context Aware training framework for NMT . they incorporate bidirectional global context to the NMT model on unconfidently-predicted target words . |
| Outcome: | The proposed framework improves the NMT model on three large-scale translation datasets by +1.02, +0.57 BLEU scores. |
From Charts to Code: A Hierarchical Benchmark for Multimodal Models (2026.acl-long)
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Jiahao Tang, Henry Hengyuan Zhao, Lijian Wu, Zijian Zhang, Yifei Tao, Dongxing Mao, Yang Wan, Jingru Tan, Min Zeng, Min Li, Alex Jinpeng Wang
| Challenge: | Chart2Code is a new benchmark for evaluating the natural language to chart code generation capabilities of large multimodal models. |
| Approach: | They introduce Chart2Code, a new benchmark for evaluating the natural language to chart code generation capabilities of large multimodal models. |
| Outcome: | The proposed benchmark is the first to scale task complexity while capturing diverse scenarios. |
Reflection on Knowledge Graph for Large Language Models Reasoning (2025.findings-acl)
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Yigeng Zhou, Wu Li, Yifan Lu, Jing Li, Fangming Liu, Meishan Zhang, Yequan Wang, Daojing He, Honghai Liu, Min Zhang
| Challenge: | Existing methods for supplementing Large Language Models (LLMs) with knowledge graphs often introduce noise in the retrieval and reasoning pipeline, hindering their ability to integrate external knowledge for complex multi-hop question answering. |
| Approach: | They propose a framework to enhance LLMs' reasoning capabilities through reflective engagement with knowledge graphs by Query Decoupling, LLM-Driven Knowledge Graph Exploration, and Inference with Knowledge Reconstruction. |
| Outcome: | The proposed framework integrates external knowledge into LLMs and trains them to leverage this knowledge for answering questions. |
SMiLE: Schema-augmented Multi-level Contrastive Learning for Knowledge Graph Link Prediction (2022.findings-emnlp)
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| Challenge: | Existing methods for linking knowledge graphs lack contextual information in entity neighborhoods, which leads to false prediction results. |
| Approach: | They propose a Schema-augmented Multi-level contrastive LEarning framework to conduct knowledge graph link prediction using a knowledge graph schema. |
| Outcome: | The proposed framework is based on a knowledge graph schema and is compared against state-of-the-art datasets. |
Rethinking Document-Level Relation Extraction: A Reality Check (2023.findings-acl)
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| Challenge: | Recent efforts push up performance boundaries of document-level relation extraction (DocRE) but these efforts are not promising. |
| Approach: | They construct four types of entity mention attacks to examine model robustness . they also have a close check on model usability in a more realistic setting . |
| Outcome: | The proposed model is based on a strong or untenable assumption in common . the model is robust under four types of mention attacks and usable in a realistic setting . |
ZoFia: Zero-Shot Fake News Detection with Entity-Guided Retrieval and Multi-LLM Interaction (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are limited by knowledge cutoff and can generate factual hallucinations when handling time-sensitive news. |
| Approach: | They propose a two-stage zero-shot fake news detection framework that uses a hierarchical salience and saliency-calibrated minimum margin of relevance algorithm to extract core entities accurately. |
| Outcome: | The proposed framework outperforms existing zero-shot baselines and even most few-shot methods on two public datasets. |
Towards Robust Neural Machine Translation with Iterative Scheduled Data-Switch Training (2022.coling-1)
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Zhongjian Miao, Xiang Li, Liyan Kang, Wen Zhang, Chulun Zhou, Yidong Chen, Bin Wang, Min Zhang, Jinsong Su
| Challenge: | Existing methods on robust neural machine translation (NMT) construct adversarial examples by injecting noise into authentic examples and indiscriminately exploit two types of examples. |
| Approach: | They propose an iterative scheduled data-switch training framework to mitigate this problem by injecting noise into authentic examples and indiscriminately exploiting two types of examples. |
| Outcome: | The proposed model outperforms several competitive benchmarks on four translation benchmarks. |
Multiple Knowledge-Enhanced Interactive Graph Network for Multimodal Conversational Emotion Recognition (2024.findings-emnlp)
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| Challenge: | Multimodal Emotion Recognition in Conversations models struggle due to lack of Common Sense Knowledge (CSK). |
| Approach: | They propose a multimodal approach to integrate multiple knowledge into the edge representations by integrating textual and visual CSK. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two popular datasets. |
Data Interpreter: An LLM Agent for Data Science (2025.findings-acl)
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Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Robert Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Yongxin Ni, Zhibin Gou, Zongze Xu, Yuyu Luo, Chenglin Wu
| Challenge: | Large Language Models (LLMs) excel in various domains but face challenges when applied to data science workflows due to their complex, multi-stage nature. |
| Approach: | They propose a hierarchical graph-based agent that represents complexity and a progressive strategy for step-by-step verification, refinement, and consistent context management. |
| Outcome: | The proposed agent surpasses state-of-the-art baselines on the MATH dataset and performs better on InfiAgent-DABench. |
An Evaluation Resource for Grounding Translation Errors (2025.findings-emnlp)
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| Challenge: | Current fine-grained error analyses do not ground the errors to the reasons why the annotated text spans are erroneous. |
| Approach: | They use a bi-directional grounding scheme to ground erroneous text in two directions . if the error spans of both directions are consistent, the explanation is valid . |
| Outcome: | The proposed grounding process improves translation error detection significantly. |
Enhancing Chain-of-Thought Reasoning via Neuron Activation Differential Analysis (2025.emnlp-main)
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| Challenge: | Existing studies focus on optimizing external components of CoT, but lack internal explanations for the quality of the model's outputs. |
| Approach: | They propose an efficient method to identify reasoning-critical neurons by analyzing their activation patterns under reasoning chains of varying quality. |
| Outcome: | The proposed method shows that neurons in the feed-forward layers are critical in the generation of high-quality reasoning chains. |
AgentInit: Initializing LLM-based Multi-Agent Systems via Diversity and Expertise Orchestration for Effective and Efficient Collaboration (2025.findings-emnlp)
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| Challenge: | Existing MAS initialization methods do not fully account for the collaborative needs of the generated agents in subsequent stages. |
| Approach: | They propose to use a Natural Language to Format mechanism to optimize the structure of agent teams and incorporate a natural language to format mechanism to ensure consistency and standardization. |
| Outcome: | The proposed method outperforms state-of-the-art initialization methods and pre-defined strategies across various frameworks and tasks while reducing token consumption. |
Tailoring Diagnostic Modeling to Individual Learners: Personalized Distractor Generation via MCTS-Guided Reasoning Reconstruction (2026.acl-long)
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| Challenge: | Current distractor generation methods produce shared distractors for all students, ignoring individual variations in reasoning, which limits their diagnostic effectiveness. |
| Approach: | They propose a method which tailors distractors to each student’s specific cognitive flaws, inferred from their past question-answering (QA) history. |
| Outcome: | The proposed framework outperforms existing methods in generating plausible distractors and adapts to group-level settings. |
Sentence-Level Agreement for Neural Machine Translation (P19-1)
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| Challenge: | Empirical results show that a sentence-level agreement module can significantly improve the performance of neural machine translation (NMT) |
| Approach: | They propose a sentence-level agreement module to minimize the difference between the representation of source and target sentences. |
| Outcome: | Empirical results show the proposed agreement module significantly improves translation performance. |
Just Like a Human Would, Direct Access to Sarcasm Augmented with Potential Result and Reaction (2023.acl-long)
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| Challenge: | sarcasm is a form of irony conveying mockery and contempt . social media has become increasingly popular for identifying sarcasm . |
| Approach: | They develop a method to detect sarcasm from social media using augmented potentials. |
| Outcome: | The proposed method outperforms baselines on benchmark datasets. |
ComfyUI-R1: Exploring Reasoning Models for Workflow Generation (2026.findings-acl)
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| Challenge: | ComfyUI-R1 is the first large reasoning model for automated workflow generation. |
| Approach: | They propose a large reasoning model for automated workflow generation that builds on curated knowledge bases and a two-stage framework to fine-tune models for cold start and reinforcement learning for incentivizing reasoning capability. |
| Outcome: | The proposed model achieves 97% format validity rate, high pass rate, node-level and graph-level F1 scores, surpassing prior state-of-the-art methods that employ leading closed-source models such as GPT-4o and Claude series. |
Early Exit with Disentangled Representation and Equiangular Tight Frame (2023.findings-acl)
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| Challenge: | Existing early exit paradigm relies on training parametrical internal classifiers to complete specific tasks. |
| Approach: | They propose a method to decouple two distinct types of representation and introduce a non-parametric tight frame classifier for improvement. |
| Outcome: | Experiments on monolingual and multilingual tasks show that the proposed method improves over existing methods. |
CentaurTA: A Self-Improving Human-Agents Collaboration Framework for Thematic Analysis (2026.findings-acl)
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| Challenge: | Existing large language model approaches for qualitative analysis are labor-intensive and costly. |
| Approach: | They propose an iterative human–agent framework for scalable thematic analysis that integrates structured human feedback with rubric-based evaluation. |
| Outcome: | The proposed framework improves coding alignment and transparency across multiple datasets, baselines, and LLM families. |
MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct (2025.findings-acl)
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Run Luo, Haonan Zhang, Longze Chen, Ting-En Lin, Xiong Liu, Yuchuan Wu, Min Yang, Yongbin Li, Minzheng Wang, Pengpeng Zeng, Lianli Gao, Heng Tao Shen, Yunshui Li, Hamid Alinejad-Rokny, Xiaobo Xia, Jingkuan Song, Fei Huang
| Challenge: | a new framework for image-text instruction data evolution improves MLLM performance . lack of high-quality instruction data remains a major bottleneck in ML modeling . |
| Approach: | They propose a multimodal instruction data evolution framework that iteratively enhances data quality through fine-grained perception, cognitive reasoning, and interaction evolution. |
| Outcome: | The proposed approach improves MLLM performance in nine vision-language tasks while using significantly less data. |
Calibrated Speculative Decoding: Frequency-Guided Candidate Selection for Efficient Inference (2026.acl-long)
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| Challenge: | Speculative decoding (SD) is a powerful and efficient way to accelerate autoregressive generation. |
| Approach: | They propose a training-free framework that recovers valid tokens discarded by standard verification . they use online correction memory and Semantic Consistency Gating to analyze rejections . |
| Outcome: | The proposed framework outperforms existing methods and achieves peak throughput speedup of 2.33x. |
Can Diffusion Model Achieve Better Performance in Text Generation ? Bridging the Gap between Training and Inference ! (2023.findings-acl)
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| Challenge: | Existing models for text generation use a discrete data embedding module to map the data into the continuous space. |
| Approach: | They propose two methods to bridge the gap between training and inference by mapping the discrete text into the continuous space. |
| Outcome: | The proposed methods can achieve 100 200 speedup with better performance on 6 generation tasks. |
AgentDropout: Dynamic Agent Elimination for Token-Efficient and High-Performance LLM-Based Multi-Agent Collaboration (2025.acl-long)
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| Challenge: | Existing methods for MAS suffer from high token consumption and inefficiency due to frequent generation and communication among multiple agents. |
| Approach: | They propose a multi-agent system based on large language models that identifies redundant agents and communication across different communication rounds by optimizing the adjacency matrices of the communication graphs and eliminates them to enhance both token efficiency and task performance. |
| Outcome: | The proposed method reduces prompt token consumption and completion token consumption by 18.4% and improves task performance by 1.14. |
DeReA: Improving Idiom Translation with Detect-Retrieve-Arbitrate Reasoning (2026.acl-long)
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| Challenge: | Existing approaches to idiom translation are limited by the constraints of static parametric memory and retrieval noise . idiomatic expressions are non-compositional units where figurative meanings diverge from literal interpretations . |
| Approach: | They propose a detect-retrieve-arbitrate framework that detects idiomatic spans by reasoning over semantic conflicts between literal and contextual meanings. |
| Outcome: | The proposed framework improves GPT-5-mini and Emerging Slang datasets on various model scales. |
PITA: Prompting Task Interaction for Argumentation Mining (2024.acl-long)
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| Challenge: | Argumentation mining (AM) aims to detect arguments and their inherent relations from textual compositions. |
| Approach: | They propose a method to model the inter-relationships among three subtasks within a generative framework. |
| Outcome: | The proposed method achieves state-of-the-art performance on two AM benchmarks. |
Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters (2023.acl-long)
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| Challenge: | Chain-of-Thought (CoT) prompting can dramatically improve the multi-step reasoning abilities of large language models (LLMs). |
| Approach: | They propose to use Chain-of-Thought (CoT) prompting to encourage the LLM to generate intermediate rationales for solving a problem by providing a series of reasoning steps in the demonstrations. |
| Outcome: | The proposed model can generate coherent lines of reasoning even with invalid demonstrations while still generating coherent lines during inference. |
SEMGraph: Incorporating Sentiment Knowledge and Eye Movement into Graph Model for Sentiment Analysis (2022.emnlp-main)
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| Challenge: | Existing research on sentiment analysis based on eye movement signals has been attributed importance. |
| Approach: | They propose a linguistic probing eye movement paradigm to extract eye movement features based on the relationship between linguistic features and human reading behavior. |
| Outcome: | The proposed graph architecture achieves state-of-the-art performance on two sentiment analysis datasets with eye movement signals and three sentiment analysis data without eye movement signal. |
MEVTR: A Multilingual Model Enhanced with Visual Text Representations (2024.lrec-main)
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| Challenge: | Existing models that generate multilingual text representations perform poorly on low-resource languages due to lack of representation space and model capacity. |
| Approach: | They propose a multilingual model enhanced with visual text representations which complements textual representations and extends multilingual representation space with visual representations. |
| Outcome: | The proposed model outperforms state-of-the-art models on zero-shot cross-lingual transfer tasks without the target language adapter. |
DUAL RM: Beyond Rule-based Preference Reward Modeling via Meta-Reward (2026.acl-long)
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Xiaobo Liang, Wanfu Wang, Qipeng Huang, Yuyang Ding, Zecheng Tang, Yixin Ji, Qianben Chen, Zhe Zhao, Kehai Chen, Juntao Li, Min Zhang
| Challenge: | Existing preference-based reward modeling methods face a recursive dependency where each verifier requires a meta-verifier, leading to continuous and costly dependence on human annotation. |
| Approach: | They propose a dual RM that couples discriminative and generative reward models under a non-parametric meta-reward. |
| Outcome: | The proposed model achieves strong performance across major preference benchmarks and even when trained exclusively on language modality, it exhibits robust cross-modal transfer on Omni-RewardBench. |
MSVBench: Towards Human-Level Evaluation of Multi-Shot Video Generation (2026.findings-acl)
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| Challenge: | Existing evaluation methods for complex multi-shot video are anchored to single-shot paradigms, lacking comprehensive story assets and cross-shot metrics. |
| Approach: | They propose a framework that synergizes the high-level semantic reasoning of Large Multimodal Models with the fine-grained perceptual rigor of domain-specific expert models. |
| Outcome: | The proposed framework synergizes the high-level semantic reasoning of Large Multimodal Models with the fine-grained perceptual rigor of domain-specific expert models. |
MTLS: Making Texts into Linguistic Symbols (2024.emnlp-main)
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| Challenge: | In linguistics, all languages can be considered as symbolic systems . most work overlooks the properties of languages as symbol systems - aaron et al., 1989). |
| Approach: | They propose a method to make texts into linguistic symbols to improve multilingual capability . they use a pre-training method to replace pre-trained language models with a vocabulary map . |
| Outcome: | The proposed method improves multilingual capabilities on multilingual tasks using BERT and RoBERTa as the backbone. |
ReasoningGuard: Safeguarding Large Reasoning Models with Inference-time Safety Aha Moments (2026.acl-long)
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| Challenge: | Existing defenses for Large Reasoning Models (LRMs) depend on costly fine-tuning and additional expert knowledge, which limits their scalability. |
| Approach: | They propose an inference-time safeguard for Large Reasoning Models that injects safety aha moments into the reasoning process to guide the model towards harmless yet helpful reasoning. |
| Outcome: | The proposed safeguard outperforms nine existing safeguards while avoiding common exaggerated safety issues. |
WR-One2Set: Towards Well-Calibrated Keyphrase Generation (2022.emnlp-main)
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| Challenge: | Experimental results show that keyphrase generation has serious calibration errors . ONE2SET generates short phrases summarizing an input document . |
| Approach: | They propose a paradigm for keyphrase generation that generates short phrases summarizing an input document. |
| Outcome: | The proposed model over-estimates tokens and makes it well-calibrated on common datasets. |
TaxPraBen: A Scalable Benchmark for Structured Evaluation of LLMs in Chinese Real-World Tax Practice (2026.acl-long)
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| Challenge: | Large Language Models excel in general domains but lack real-world practical capabilities. |
| Approach: | They propose a benchmark for Chinese taxation practice that combines 10 traditional application tasks with 3 pioneering real-world scenarios. |
| Outcome: | The proposed benchmark combines 10 traditional tasks with 3 pioneering real-world scenarios. |
Accurate KV Cache Quantization with Outlier Tokens Tracing (2025.acl-long)
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Yi Su, Yuechi Zhou, Quantong Qiu, Juntao Li, Qingrong Xia, Ping Li, Xinyu Duan, Zhefeng Wang, Min Zhang
| Challenge: | Large Language Models (LLMs) require substantial computational resources during deployment. |
| Approach: | They propose a method to identify outlier tokens and exclude them from quantization . they find that the method can deliver a 6.4 times reduction in memory usage and a 2.5 times increase in throughput . |
| Outcome: | The proposed method delivers a 6.4 times reduction in memory usage and a 2.5 times increase in throughput under 2-bit quantization. |
The Rise of Darkness: Safety-Utility Trade-Offs in Role-Playing Dialogue Agents (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) demonstrate their utility in character simulations, but they pose a risk of generating unsafe content. |
| Approach: | They propose a method which dynamically adjusts safety-utility preferences based on the degree of risk coupling and guides the model to generate responses biased toward utility or safety. |
| Outcome: | The proposed method improves safety metrics while maintaining utility. |
TROVE: A Challenge for Fine-Grained Text Provenance via Source Sentence Tracing and Relationship Classification (2025.acl-long)
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| Challenge: | Large language models have demonstrated great potential in natural language generation, but their widespread adoption has raised concerns regarding content reliability and accountability. |
| Approach: | They propose a challenge to trace each sentence of a target text back to specific source sentences within potentially lengthy or multi-document inputs. |
| Outcome: | The proposed challenge traces each sentence of a target text back to specific source sentences . the dataset includes 11 scenarios covering QA and summarization in english and Chinese . |
How Far Can LLMs Improve from Experience? Measuring Test-Time Learning Ability in LLMs with Human Comparison (2025.emnlp-main)
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| Challenge: | Existing benchmarks primarily assess static knowledge, while intelligence also entails the ability to rapidly learn from experience. |
| Approach: | They propose to use semantic games to evaluate test-time learning . they recruit eight human participants to complete the same task . |
| Outcome: | The proposed framework compares model performance under limited and cumulative experience settings and contains four forms of experience representation. |
Efficient Reasoning for LLMs through Speculative Chain-of-Thought (2026.findings-acl)
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| Challenge: | Existing methods for efficient reasoning focus on reducing the number of model parameters or shortening the chain-of-thought length. |
| Approach: | They propose a speculative chain-of-thought (SCoT) method to reduce reasoning latency by accelerating average reasoning speed through large and small model collaboration. |
| Outcome: | The proposed method reduces reasoning latency by 48%66% and 21%49% on GSM8K, MATH, GaoKao, CollegeMath and Olympiad datasets. |
Adaptive Feature-based Low-Rank Compression of Large Language Models via Bayesian Optimization (2024.findings-emnlp)
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Yixin Ji, Yang Xiang, Juntao Li, Qingrong Xia, Zi Ye, Xinyu Duan, Zhefeng Wang, Kehai Chen, Min Zhang
| Challenge: | Large language models require a balance between efficiency and performance. |
| Approach: | They propose a low-rank compression technique that reduces non-essential parameters by decomposing weight matrices into products of two low-ranked matrici. |
| Outcome: | The proposed method outperforms existing pruning and low-rank compression techniques in maintaining model performance at the same compression ratio. |
Semantic Role Labeling with Heterogeneous Syntactic Knowledge (2020.coling-main)
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| Challenge: | Recent work on incorporating syntactic knowledge into neural semantic role labeling has gained much attention . incorporating heterogeneous syntaktic knowledge brings significant improvements over strong baselines . |
| Approach: | They propose to encode heterogeneous syntactic knowledge for SRL from explicit and implicit representations from heterogenous treebanks. |
| Outcome: | The proposed approaches improve on two widely-used benchmark datasets. |
Unimodal and Crossmodal Refinement Network for Multimodal Sequence Fusion (2021.emnlp-main)
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| Challenge: | Existing approaches to modulate one modal feature to another are lacking in multimodal representation learning. |
| Approach: | They propose to use unimodal and crossmodal refinement networks to enhance uni and cross-modal representations by iterative updating of distributions with transformer-based attention layers to refine modality-specific learning. |
| Outcome: | The proposed network outperforms state-of-the-art techniques on MOSI and MOSEI datasets. |
Covariance Matrix-Driven Image Channel Allocation for Multimodal Fake News Detection (2026.findings-acl)
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| Challenge: | Existing methods focus on designing efficient multimodal fusion frameworks to bridge the semantic gap between images and texts. |
| Approach: | They propose a covariance matrix-driven image channel allocation method that expands the number of original channel maps and assigns importance scores to the expanded channel maps. |
| Outcome: | The proposed method achieves state-of-the-art on three public multimodal fake news detection benchmark datasets. |
BrainECHO: Semantic Brain Signal Decoding through Vector-Quantized Spectrogram Reconstruction for Whisper-Enhanced Text Generation (2025.findings-acl)
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| Challenge: | Current EEG/MEG-to-text decoding systems rely on teacher-forcing methods . pre-trained large language models are over-dominant in decoding text from brain activity . |
| Approach: | They propose a framework that employs decoupled representation learning to achieve state-of-the-art performance on EEG and MEG datasets. |
| Outcome: | The proposed framework achieves state-of-the-art performance on EEG and MEG datasets. |
Multimodal Invariant Sentiment Representation Learning (2025.findings-acl)
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| Challenge: | Existing methods for multimodal sensing ignore significant sentiment distribution imbalances and cross-modal sentiment conflicts, hindering performance improvement. |
| Approach: | They propose a method to learn stable multimodal invariant sentiment representations by incorporating distributional discrepancies and sentiment conflicts into the model training. |
| Outcome: | The proposed method improves MSA performance and achieves new state-of-the-art. |
CINO: A Chinese Minority Pre-trained Language Model (2022.coling-1)
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| Challenge: | Existing multilingual pre-trained language models do not perform well on some low-resource languages. |
| Approach: | They propose a multilingual pre-trained language model for Chinese minority languages . they collect documents from Wikipedia and construct two classification datasets . |
| Outcome: | The proposed model outperforms baseline models on various classification tasks. |
HiMATE: A Hierarchical Multi-Agent Framework for Machine Translation Evaluation (2025.findings-emnlp)
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| Challenge: | Existing LLM-based evaluation methods fail to accurately identify error spans and assess their severity. |
| Approach: | They propose a Hierarchical Multi-Agent Framework for Machine Translation Evaluation based on the MQM error typology and a hierarchical multi-agent system enabling granular evaluation of subtype errors. |
| Outcome: | The proposed framework outperforms baselines in error span detection and severity assessment. |
Beyond Quantity: Trajectory Diversity Scaling for Code Agents (2026.findings-acl)
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Guhong Chen, Chenghao Sun, Cheng Fu, Qiyao Wang, Zhihong Huang, ChaoPeng Wei, Guangxu Chen, Feiteng Fang, Ahmadreza Argha, Bing Zhao, Xander Xu, Qi Han, Hamid Alinejad-Rokny, Qiang Qu, Binhua Li, Shiwen Ni, Min Yang, HU Wei, Yongbin Li
| Challenge: | Code large language models (LLMs) are becoming tool-interactive agents . quantity-centric scaling exhibits an early bottleneck that underutilizes trajectory data . et al.: a new approach to scale trajectory diversity improves tool-use generalization . |
| Approach: | They propose a Trajectory Diversity Scaling-based data synthesis framework for code agents that scales performance through diversity rather than raw volume. |
| Outcome: | Experiments on general tool-use benchmarks and code agent tasks show that TDScaling improves tool-user generalization and inherent coding proficiency. |
CLaSp: In-Context Layer Skip for Self-Speculative Decoding (2025.acl-long)
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Longze Chen, Renke Shan, Huiming Wang, Lu Wang, Ziqiang Liu, Run Luo, Jiawei Wang, Hamid Alinejad-Rokny, Min Yang
| Challenge: | Existing methods for drafting Large Language Models require additional modules to be trained, which can be challenging to implement and ensure compatibility across various LLMs. |
| Approach: | They propose an in-context layer-skipping strategy for self-speculative decoding that uses a plug-and-play mechanism to skip intermediate layers of the verify model to construct a compressed draft model. |
| Outcome: | The proposed method achieves a speedup of 1.3 1.7 on LLaMA3 series models without altering the original distribution of the generated text. |
Beyond Unimodal Shortcuts: MLLMs as Cross-Modal Reasoners for Grounded Named Entity Recognition (2026.findings-acl)
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| Challenge: | Existing approaches to GMNER use MLLMs as auxiliary tools, causing cumulative error propagation and a lack of rigorous cross-modal verification. |
| Approach: | They propose a model that enforces structured cross-modal reasoning through Multi-style Reasoning Schema Injection and Constraint-guided Verifiable Optimization. |
| Outcome: | The proposed model enforces structured cross-modal reasoning through multi-style Reasoning Schema Injection and Constraint-guided Verifiable Optimization. |
Towards IP Intelligence: Benchmarking Large Language Models on Intellectual Property Knowledge and Practice (2026.findings-acl)
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Qiyao Wang, Guhong Chen, Hongbo Wang, Huaren Liu, Minghui Zhu, Zhifei Qin, Li Linwei, Yilin Yue, Shiqiang Wang, Jiayan Li, Wu Yihang, Ziqiang Liu, Longze Chen, Run Luo, Liyang Fan, Jiaming Li, Lei Zhang, Kan Xu, Hamid Alinejad-Rokny, Chengming Li, Shiwen Ni, Yuan Lin, Min Yang
| Challenge: | Existing datasets and benchmarks focus only on patents or cover limited aspects of the IP field, lacking alignment with real-world scenarios. |
| Approach: | They propose a bilingual IP task taxonomy and a large-scale bilingual benchmark to evaluate LLMs in real-world IP practice. |
| Outcome: | The proposed model achieves only 75.8% accuracy, indicating room for improvement . open-source IP and law-oriented models lag behind closed-source general-purpose models . |
Pay More Attention to Relation Exploration for Knowledge Base Question Answering (2023.findings-acl)
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| Challenge: | Existing approaches focus on entity representation and final answer reasoning, which results in limited supervision for this task. |
| Approach: | They propose a framework that utilizes relations to enhance entity representation and introduce additional supervision. |
| Outcome: | The proposed framework improves the F1 score on two benchmark datasets by 5.8% . it improves by 6.7% on WebQSP, better than state-of-the-art methods . |
Capture Human Disagreement Distributions by Calibrated Networks for Natural Language Inference (2022.findings-acl)
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| Challenge: | Previously, it's common to disregard it as noise or as a sign of poor-quality data, as their annotations are heavily based on personal experience and opinions. |
| Approach: | They propose to capture the human disagreement distribution from the perspective of model calibration. |
| Outcome: | The proposed model can achieve competitive performance when well-calibrated, on divergence scores between predictive probability and the true human opinion distribution, and the accuracy. |
Alignment-Augmented Speculative Decoding with Alignment Sampling and Conditional Verification (2025.emnlp-main)
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| Challenge: | Existing methods to accelerate autoregressive generation of large language models require training costs. |
| Approach: | They propose a training-free alignment-augmented speculative decoding algorithm . it leverages the output distribution obtained in the prefilling phase to provide more aligned draft candidates . |
| Outcome: | The proposed method increases the average generation score by 3.3 points for the LLaMA3 model. |
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. |
Breaking the Representation Bottleneck of Chinese Characters: Neural Machine Translation with Stroke Sequence Modeling (2022.emnlp-main)
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| Challenge: | Existing research treats Chinese character as a minimum unit for representation . however, such representation suffers from two bottlenecks: 1) learning bottleneck; 2) parameter bottleneck, each individual character has to be represented by a unique vector. |
| Approach: | They propose a representation method for Chinese characters to break the representation bottlenecks . they map each stroke to a specific Latin character, thus allowing similar Chinese characters . |
| Outcome: | The proposed representation method breaks two representation bottlenecks in Chinese character representation . it maps each stroke to a specific Latin character, thus allowing similar Chinese characters to have similar representations . |
iAgent: LLM Agent as a Shield between User and Recommender Systems (2025.findings-acl)
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Wujiang Xu, Yunxiao Shi, Zujie Liang, Xuying Ning, Kai Mei, Kun Wang, Xi Zhu, Min Xu, Yongfeng Zhang
| Challenge: | Traditional recommender systems focus on the user-platform paradigm, where users are directly exposed under the control of the platform's recommendation algorithms. |
| Approach: | They propose a user-agent-platform paradigm where agent serves as the protective shield between user and recommender system that enables indirect exposure. |
| Outcome: | The proposed model improves 16.6% over baselines on four datasets and mitigates echo chamber effects and reduces model bias in disadvantaged users. |
Adaptive Detoxification: Safeguarding General Capabilities of LLMs through Toxicity-Aware Knowledge Editing (2025.findings-acl)
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Yifan Lu, Jing Li, Yigeng Zhou, Yihui Zhang, Wenya Wang, Xiucheng Li, Meishan Zhang, Fangming Liu, Jun Yu, Min Zhang
| Challenge: | Existing knowledge editing methods for large language models (LLMs) suffer from over-editing, where detoxified models reject legitimate queries, compromising overall performance. |
| Approach: | They propose a toxicity-aware knowledge editing approach that dynamically detects toxic activation patterns during forward propagation and then routes computations through adaptive inter-layer pathways to mitigate toxicity effectively. |
| Outcome: | The proposed method outperforms existing methods on large language models and enhances the SafeEdit benchmark. |
Domain-Aware k-Nearest-Neighbor Knowledge Distillation for Machine Translation (2024.findings-acl)
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| Challenge: | Existing methods to transfer knowledge from kNN datastore into new models are expensive and arbitrarily transfer knowledge. |
| Approach: | They propose a domain-aware method which filters out domain-relevant neighborhood knowledge for learning in the distillation process. |
| Outcome: | The proposed method achieves state-of-the-art on four domain translation tasks. |
CMD: a framework for Context-aware Model self-Detoxification (2024.emnlp-main)
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| Challenge: | Existing methods of text detoxification fail to achieve a decent balance between effectiveness and generation quality. |
| Approach: | They propose a text detoxification framework that pays attention to both context and detoxification process. |
| Outcome: | Experiments on various LLMs show that the proposed framework can yield the best performance compared to baselines. |
SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers (2022.coling-1)
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Bowen Qin, Lihan Wang, Binyuan Hui, Bowen Li, Xiangpeng Wei, Binhua Li, Fei Huang, Luo Si, Min Yang, Yongbin Li
| Challenge: | Existing methods that learn from multiple semantically-equivalent questions are limited to one-to-one mapping . |
| Approach: | They propose a constraint to explore the underlying complementary semantic information among multiple semantically-equivalent questions and learn robust feature representations with reduced spurious associations. |
| Outcome: | The proposed method outperforms strong competitors and achieves state-of-the-art results on five benchmark datasets. |
ComfyFlow: Benchmarking LLMs for AIGC Workflow Generation (2026.findings-acl)
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Zhenran Xu, Yiyu Wang, Yunxin li, Muyang Ye, null Yangxue, Kai Chen, Longyue Wang, Weihua Luo, Baotian Hu, Min Zhang
| Challenge: | Large language models (LLMs) have shown promising advances in tackling human-level tasks, but generating workflows for collaborative AI systems remains a critical and challenging step. |
| Approach: | They propose a benchmark to evaluate LLMs’ ability to generate executable and instruction-following AIGC workflows in ComfyUI. |
| Outcome: | The proposed benchmarks show that LLMs can generate executable and instruction-following AIGC workflows in ComfyUI. |
Think Faster Than Words: Efficient LLM Chain-of-Thought Reasoning via Dynamic Shortcut Decoding (2026.acl-long)
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| Challenge: | Existing methods that prune or employ early stopping to reduce latency often compromise reasoning reliability. |
| Approach: | They propose a shortcut decoding framework that integrates probes over internal hidden states with step-level entropy to detect convergence of reasoning during generation and adaptively selects between a fast-exit path and a stability-verified path to remove redundant steps while preserving answer correctness. |
| Outcome: | The proposed framework reduces token usage by approximately 35% and maintains accuracy comparable to full CoT decoding. |
Diagnosing and Remedying Representation Deficiencies for Deterministic Reasoning in KGQA (2026.acl-long)
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| Challenge: | Existing approaches to solve non-deterministic reasoning problems in large language models are limited by their complexity and lack of a clear understanding of the problem. |
| Approach: | They propose a method to diagnose and correct non-deterministic reasoning behaviors in large language models. |
| Outcome: | The proposed method outperforms baselines and WebQSP benchmarks on the widely used WebQ SP and CWQ benchmarks. |
Concise and Precise Context Compression for Tool-Using Language Models (2024.findings-acl)
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Yang Xu, Yunlong Feng, Honglin Mu, Yutai Hou, Yitong Li, Xinghao Wang, Wanjun Zhong, Zhongyang Li, Dandan Tu, Qingfu Zhu, Min Zhang, Wanxiang Che
| Challenge: | Existing methods suffer from key information loss and difficulty in adjusting the length of compressed sequences based on documentation lengths. |
| Approach: | They propose two strategies for compressing tool documentation into concise and precise summary sequences for tool-using language models. |
| Outcome: | The proposed approach achieves comparable performance to the upper-bound baseline under 16x compression ratio. |
From Prediction to Intervention: Personalized Meal-Level Glucose Regulation via an LLM Agent (2026.findings-acl)
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| Challenge: | Existing approaches to individualized glucose regulation are generic and do not account for individual-specific glucose dynamics. |
| Approach: | They propose a physio-feedback agentic loop that integrates individualized absorption modeling with dietary intervention to regulate glucose response. |
| Outcome: | The proposed system improves prediction accuracy and reduces glucose excursions. |
PTQ1.61: Push the Real Limit of Extremely Low-Bit Post-Training Quantization Methods for Large Language Models (2025.acl-long)
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| Challenge: | Existing methods for sub 2-bit quantization introduce an extra 1-bit or more per weight. |
| Approach: | They propose a sub 2-bit post-training quantization method that enables weight quantization to 1.61-bit for the first time. |
| Outcome: | The proposed method reduces the upper bound of quantization error to 1.61-bit for the first time. |
PiKGL: Leveraging Pruned Knowledge Graphs for Explainable Stance Detection (2026.tacl-1)
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Bingbing Wang, Jingjie Lin, Zhixin Bai, Xintong Song, Qianlong Wang, Min Yang, Xi Zeng, Jing Li, Ruifeng Xu
| Challenge: | Experimental results demonstrate that a Pruned interpretable knowledge Graph Learning framework for explainable stance detection is state-of-the-art for social media stance prediction. |
| Approach: | They propose a Pruned interpretable knowledge Graph Learning framework for explainable stance detection that incorporates commonsense knowledge and prunes redundant information to ensure precision and minimize noise. |
| Outcome: | The proposed framework achieves state-of-the-art on three public datasets. |
LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding (2021.acl-long)
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Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou
| Challenge: | Existing pre-training tasks for text and layout are effective in visually-rich document understanding tasks. |
| Approach: | They propose to combine pre-training tasks with a multi-modal model to model interaction between text, layout and image in a single multi-module framework. |
| Outcome: | The proposed model outperforms LayoutLM by a large margin on visual-rich document understanding tasks. |
AHVE-CNER: Aligned Hanzi Visual Encoding Enhance Chinese Named Entity Recognition with Multi-Information (2025.coling-main)
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| Challenge: | Existing glyph-based models neglect the relationship between pictorial elements and radicals for Named Entity Recognition (NER) tasks. |
| Approach: | They propose a model that integrates multi-source visual and phonetic information of Hanzi . they propose combining pictographic features with radicals to facilitate integration . |
| Outcome: | The proposed model improves performance on benchmark datasets. |
OPT-Tree: Speculative Decoding with Adaptive Draft Tree Structure (2025.tacl-1)
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| Challenge: | Autoregressive language models generate one token in one step, limiting inference efficiency . Existing methods do not adapt to different situations to maximize acceptance length . speculative decoding has shown great potential for lossless acceleration . |
| Approach: | They propose an algorithm to construct adaptive and scalable draft trees for autoregressive language models. |
| Outcome: | Experimental results show that OPT-Tree outperforms existing draft trees and achieves speed-up ratio of up to 3.2 compared with autoregressive decoding. |
MIND Your Reasoning: A Meta-Cognitive Intuitive-Reflective Network for Dual-Reasoning in Multimodal Stance Detection (2026.acl-long)
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| Challenge: | Existing methods operate by learning to fuse modalities, leading to frequent misjudgments. |
| Approach: | They propose a paradigm shift from *learning to fuse* to *learning the reason's process' inspired by the dual-process theory of human cognition, MIND operationalizes a self-improving loop. |
| Outcome: | The proposed model significantly outperforms baseline models and exhibits strong generalization. |
Towards More Realistic Chinese Spell Checking with New Benchmark and Specialized Expert Model (2024.lrec-main)
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Yue Wang, Zilong Zheng, Juntao Li, Zhihui Liu, Jinxiong Chang, Qishen Zhang, Zhongyi Liu, Guannan Zhang, Min Zhang
| Challenge: | Large Language Models (LLMs) have been gaining attention for their ability to perform a wide range of open-domain tasks . however, the performance of LLMs has yet to be comprehensively evaluated in realistic scenarios . |
| Approach: | They propose a task to evaluate the performance of Large Language Models (LLMs) they propose RCSC task to convert Chinese text into correct text . |
| Outcome: | The proposed task evaluates the performance of existing methods in Chinese text . the realistic Chinese spell checker can achieve state-of-the-art performance on the task . |
Identifying Chinese Opinion Expressions with Extremely-Noisy Crowdsourcing Annotations (2022.acl-long)
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| Challenge: | Recent works of opinion expression identification (OEI) rely heavily on the quality and scale of the manually-constructed training corpus. |
| Approach: | They propose to use crowdsourcing annotations to build a large-scale but quality-unguaranteed corpus for opinion expression identification in Chinese. |
| Outcome: | The proposed model can be trained with a synthetic expert and is highly consistent with the training and testing phase. |
Emotion Detection with Neural Personal Discrimination (D19-1)
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| Challenge: | Existing approaches to automatically predict the emotions of posts consider each post individually and predict their emotions independently. |
| Approach: | They propose a Neural Personal Discrimination approach to identify personal attributes from posts and connect relevant posts with similar attributes to jointly learn their emotions. |
| Outcome: | The proposed approach improves on existing models by capturing attributes-aware words and predicting emotions among relevant posts. |
Sticker-TTS: Learn to Utilize Historical Experience with a Sticker-driven Test-Time Scaling Framework (2025.emnlp-main)
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| Challenge: | Large reasoning models have exhibited strong performance on complex reasoning tasks, but current test-time scaling methods rely on redundant sampling and ignore historical experience utilization. |
| Approach: | They propose a test-time scaling framework that coordinates three collaborative LRMs to iteratively explore and refine solutions guided by historical attempts. |
| Outcome: | The proposed framework surpasses strong baselines on three mathematical reasoning benchmarks, including AIME-24, AIME-25, and OlymMATH. |
Efficient Safety Alignment of Large Language Models via Preference Re-ranking and Representation-based Reward Modeling (2025.acl-long)
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| Challenge: | Existing safety alignment methods for Large Language Models (LLMs) face the distribution shift issue, which requires significant computational resources. |
| Approach: | They propose a framework that leverages the model’s intrinsic safety judgment capability to extract reward signals, which are then used to calculate label confidence for preference reordering. |
| Outcome: | The proposed framework improves safety performance while avoiding 300x computational overheads. |
MoDification: Mixture of Depths Made Easy (2025.naacl-long)
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Chen Zhang, Meizhi Zhong, Qimeng Wang, Xuantao Lu, Zheyu Ye, Chengqiang Lu, Yan Gao, Yao Hu, Kehai Chen, Min Zhang, Dawei Song
| Challenge: | Long-context efficiency is a trending topic in large language model (LLM) serving. |
| Approach: | They propose a method to combine long-context efficiency and mixture of depths to bring down both latency and memory. |
| Outcome: | The proposed method achieves 1.2 speedup in latency and 1.8 reduction in memory compared to original LLMs especially in long-context applications. |
Aspect Sentiment Classification with Document-level Sentiment Preference Modeling (2020.acl-main)
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| Challenge: | Existing studies consider Aspect Sentiment Classification (ASC) as an independent sentence-level classification problem aspect by aspect. |
| Approach: | They propose a Cooperative Graph Attention Networks approach for cooperatively learning aspect-related sentence representation. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods in document-level sentiment classification. |
Revisiting Commonsense Reasoning in Machine Translation: Training, Evaluation and Challenge (2023.acl-long)
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| Challenge: | CR is the ability to understand and navigate the world using basic knowledge and understanding shared by most people. |
| Approach: | They propose to incorporate pretrained knowledge into NMT models and use them as robust testbeds for investigating CR in NMT. |
| Outcome: | The proposed method improves the training of NMT models with high CR abilities and provides accurate evaluation metrics. |
Can MLLMs Understand the Deep Implication Behind Chinese Images? (2025.acl-long)
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Chenhao Zhang, Xi Feng, Yuelin Bai, Xeron Du, Jinchang Hou, Kaixin Deng, Guangzeng Han, Qinrui Li, Bingli Wang, Jiaheng Liu, Xingwei Qu, Yifei Zhang, Qixuan Zhao, Yiming Liang, Ziqiang Liu, Feiteng Fang, Min Yang, Wenhao Huang, Chenghua Lin, Ge Zhang, Shiwen Ni
| Challenge: | MLLMs perform poorly on traditional culture images, indicating limitations in understanding high-level semantics and lacking a deep knowledge base of Chinese traditional culture. |
| Approach: | They propose to use Chinese images to assess MLLMs' higher-order perception and understanding of Chinese visual content. |
| Outcome: | The proposed model incorporates images that represent Chinese traditional culture, such as famous Chinese traditional paintings, to ensure the authenticity of the Chinese context. |
Stacked AMR Parsing with Silver Data (2021.findings-emnlp)
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| Challenge: | Lack of large-scale annotated data is one main challenge for abstract meaning representation (AMR) parsing. |
| Approach: | They propose to use silver data to train a pre-trained abstract meaning representation model. |
| Outcome: | The proposed model outperforms previous models on the AMR2.0 dataset and is faster than the SOTA model. |
Assessing Robustness of Text Classification through Maximal Safe Radius Computation (2020.findings-emnlp)
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| Challenge: | Neural network NLP models are vulnerable to small modifications of the input that maintain the original meaning but result in a different prediction. |
| Approach: | They propose to provide a measure of robustness against word substitutions by computing a safe radius for a given input text. |
| Outcome: | The proposed methods are compared with LIME and CNN-Cert and show that they perform well on sentiment analysis and news classification models. |
Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems (2020.coling-main)
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| Challenge: | Existing task-oriented dialog systems struggle to dynamically model long dialog context for interactions and effectively incorporate knowledge base (KB) information into dialog generation. |
| Approach: | They propose a dual dynamic memory network for multi-turn dialog generation . the model dynamically expands the dialog memory turn by turn and keeps track of dialog history . |
| Outcome: | The proposed model outperforms baseline models on three benchmark datasets on human evaluation and automatic evaluation. |
Exploring All-In-One Knowledge Distillation Framework for Neural Machine Translation (2023.emnlp-main)
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| Challenge: | Existing knowledge distillation methods only obtain one lightweight student each time . this could be resource-intensive and resulting in multiple students not being optimally utilized . |
| Approach: | They propose a knowledge distillation framework which generates multiple satisfactory students at once. |
| Outcome: | The proposed framework generates multiple satisfactory students at once. |
Mirror: A Universal Framework for Various Information Extraction Tasks (2023.emnlp-main)
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Tong Zhu, Junfei Ren, Zijian Yu, Mengsong Wu, Guoliang Zhang, Xiaoye Qu, Wenliang Chen, Zhefeng Wang, Baoxing Huai, Min Zhang
| Challenge: | Recent studies often formulate IE tasks as a triplet extraction problem, but this paradigm does not support multi-span and n-ary extraction, leading to weak versatility. |
| Approach: | They propose a multi-span cyclic graph extraction problem and a non-autoregressive graph decoding algorithm to extract all spans in a single step. |
| Outcome: | The proposed model outperforms or reaches competitive performance with SOTA systems under few-shot and zero-shot settings and it is compatible with 57 datasets. |
Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning (2023.acl-long)
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Alexander Hanbo Li, Mingyue Shang, Evangelia Spiliopoulou, Jie Ma, Patrick Ng, Zhiguo Wang, Bonan Min, William Yang Wang, Kathleen McKeown, Vittorio Castelli, Dan Roth, Bing Xiang
| Challenge: | Existing methods for data-to-text generation focus on specific types of structured data. |
| Approach: | They propose a method that provides a unified representation that can handle various forms of structured data such as tables, knowledge graph triples, and meaning representations. |
| Outcome: | The proposed method improves zero-shot and few-shot scenarios and can adapt to new structured data. |
Recognizing Everything from All Modalities at Once: Grounded Multimodal Universal Information Extraction (2024.findings-acl)
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| Challenge: | Existing studies on IE tasks have focused on recognizing and analyzing cross-modal information . a multimodal large language model (MLLM) is developed to analyze IE across modalities . |
| Approach: | They propose a multimodal large language model (MLLM) capable of grounding information from all modalities. |
| Outcome: | The proposed framework provides a framework to analyze IE tasks over various modalities and their fine-grained groundings. |
Attention Optimization for Abstractive Document Summarization (D19-1)
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| Challenge: | Abstractive summarization models require attention to reproduce the most salient information. |
| Approach: | They propose to use local and global variances to augment the vanilla attention model to reproduce the most salient information and avoid repetitions. |
| Outcome: | The proposed attention refinement unit can reproduce the most salient information and avoid repetitions on CNN/Daily Mail dataset. |
Learning to Describe Implicit Changes: Noise-robust Pre-training for Image Difference Captioning (2025.findings-emnlp)
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Zixin Guo, Jiayang Sun, Tzu-Jui Julius Wang, Abduljalil Radman, Selen Pehlivan, Min Cao, Jorma Laaksonen
| Challenge: | Large Multimodal Models (LMMs) are used to capture subtle differences between images but are noisy and coarse summaries. |
| Approach: | They propose a noise-robust approach to image difference capture using large multimodal models . they use LMMs with structured prompts to generate fine-grained change descriptions . |
| Outcome: | The proposed model outperforms streamlined architectures and improves inference efficiency. |
Towards Effective and Efficient Continual Pre-training of Large Language Models (2025.acl-long)
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Jie Chen, Zhipeng Chen, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Yingqian Min, Xin Zhao, Zhicheng Dou, Jiaxin Mao, Yankai Lin, Ruihua Song, Jun Xu, Xu Chen, Rui Yan, Zhewei Wei, Di Hu, Wenbing Huang, Ji-Rong Wen
| Challenge: | Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks. |
| Approach: | They propose a Continual pre-training method that can greatly improve Chinese language ability and scientific reasoning ability of LLMs. |
| Outcome: | The proposed method can greatly improve Chinese language ability and scientific reasoning ability of LLMs. |
Fair-CCD: Mitigating Bias in Large Language Models for Tabular Classification Through Context-Contrastive Decoding (2026.acl-long)
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| Challenge: | Prior work to mitigate fairness issues often employs subjective demonstration selection, leading to low controllability and limited stability across different models and tasks. |
| Approach: | They propose to use in-context learning to insert social biases into large language models to create a structured and controllable representation of the relationship between sensitive attributes and predicted labels. |
| Outcome: | Extensive experiments show that Fair-CCD consistently improves fairness metrics without degrading task accuracy. |
Multi-Modality Expansion and Retention for LLMs through Parameter Merging and Decoupling (2025.acl-long)
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Junlin Li, Guodong Du, Jing Li, Sim Kuan Goh, Wenya Wang, Yequan Wang, Fangming Liu, Ho-Kin Tang, Saleh Alharbi, Daojing He, Min Zhang
| Challenge: | Large Language Models (LLMs) are a cornerstone in artificial intelligence due to their exceptional performance. |
| Approach: | They propose a training-free approach that integrates existing MLLMs for effective multimodal expansion while retaining their original performance. |
| Outcome: | The proposed approach can expand LLMs' multimodal capabilities while retaining original performance. |
OpenResearcher: Unleashing AI for Accelerated Scientific Research (2024.emnlp-demo)
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Yuxiang Zheng, Shichao Sun, Lin Qiu, Dongyu Ru, Cheng Jiayang, Xuefeng Li, Jifan Lin, Binjie Wang, Yun Luo, Renjie Pan, Yang Xu, Qingkai Min, Zizhao Zhang, Yiwen Wang, Wenjie Li, Pengfei Liu
| Challenge: | Global scientific publications are growing annually by about 4%-5% (Pinedo et al., 2024). |
| Approach: | They introduce an AI-assisted platform that answers diverse questions from researchers using Retrieval-Augmented Generation (RAG) they develop various tools to understand queries, search from the scientific literature, filter retrieved information, provide accurate and comprehensive answers, and self-refine answers. |
| Outcome: | OpenResearcher is built on Retrieval-Augmented Generation (RAG) to integrate Large Language Models (LLMs) with up-to-date, domain-specific knowledge. |
𝒜3: Automatic Alignment Framework for Attributed Text Generation (2025.acl-long)
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| Challenge: | Existing approaches of aligning large language models to follow user instructions can lead to undue emphasis on irrelevant documents, which in turn reduces the quality of responses. |
| Approach: | They propose to use a framework to automatically generate high-quality attributed query-response pairs for both supervised fine-tuning and preference optimization stages without human annotation. |
| Outcome: | The proposed framework can generate high-quality attributed query-response pairs without human annotation without human intervention. |
DataSciBench: An LLM Agent Benchmark for Data Science (2026.findings-acl)
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Dan Zhang, Sining Zhoubian, Min Cai, Fengzu Li, Lekang Yang, Wei Wang, Tianjiao Dong, Ziniu Hu, Jie Tang, Yisong Yue
| Challenge: | Existing benchmarks focus on single task, simple evaluation metrics, and readily available ground truth (GT) DataSciBench is built on curated, natural, and challenging prompts with complex evaluation criteria and uncertain GT. |
| Approach: | They propose a benchmark for evaluating Large Language Models in data science that integrates LLM-based self-consistency and human verification to ensure accuracy. |
| Outcome: | The proposed framework outperforms open-source models in all metrics and offers rigorous insights into LLM strengths and weaknesses. |
Aligning to Constraints for Data-Efficient Language Model Customization (2025.findings-naacl)
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Fei Wang, Chao Shang, Shuai Wang, Sarthak Jain, Qiang Ning, Bonan Min, Vittorio Castelli, Yassine Benajiba, Dan Roth
| Challenge: | General-purpose language models (LMs) are aligned to diverse user intents, but fall short when it comes to specific applications. |
| Approach: | They propose a framework that uses constraints to automatically produce supervision signals for user alignment with constraints. |
| Outcome: | The proposed framework can produce supervision signals for user alignment with constraints. |
NUMCoT: Numerals and Units of Measurement in Chain-of-Thought Reasoning using Large Language Models (2024.findings-acl)
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| Challenge: | Existing LLMs are not able to handle numerals and units of measurement, but they can be improved by introducing perturbations. |
| Approach: | They propose to analyze existing LLMs on processing numerals and units of measurement by perturbing their datasets. |
| Outcome: | The proposed model improves on ancient Chinese arithmetic problems and can handle numeral and measurement conversions. |
XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages (2023.findings-emnlp)
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Sebastian Ruder, Jonathan Clark, Alexander Gutkin, Mihir Kale, Min Ma, Massimo Nicosia, Shruti Rijhwani, Parker Riley, Jean-Michel Sarr, Xinyi Wang, John Wieting, Nitish Gupta, Anna Katanova, Christo Kirov, Dana Dickinson, Brian Roark, Bidisha Samanta, Connie Tao, David Adelani, Vera Axelrod, Isaac Caswell, Colin Cherry, Dan Garrette, Reeve Ingle, Melvin Johnson, Dmitry Panteleev, Partha Talukdar
| Challenge: | Existing datasets are often informed by established research directions in the NLP community. |
| Approach: | They propose a benchmark to evaluate the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks. |
| Outcome: | The proposed benchmark evaluates the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks. |
Multi-Turn Dialogue Generation in E-Commerce Platform with the Context of Historical Dialogue (2020.findings-emnlp)
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WeiSheng Zhang, Kaisong Song, Yangyang Kang, Zhongqing Wang, Changlong Sun, Xiaozhong Liu, Shoushan Li, Min Zhang, Luo Si
| Challenge: | Existing research on customer service dialogue generation generates generic responses from sellers . however, such cost prohibits small businesses, and multiturn dialogue generation is becoming more popular. |
| Approach: | They propose a novel and extensible dialogue generation method by leveraging sellers’ historical dialogue information to generate generic seller responses. |
| Outcome: | The proposed model can generate high-quality responses that cater to specific sellers’ characteristics and exhibit consistent superiority over baselines on a real-world multi-turn customer service dialogue dataset. |
Dirichlet Latent Variable Hierarchical Recurrent Encoder-Decoder in Dialogue Generation (D19-1)
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| Challenge: | Existing work assumes the Gaussian priors of the latent variable, which are incapable of representing complex latent variables effectively. |
| Approach: | They propose to use the Dirichlet distribution with flexible structures to characterize latent variables in place of the Gaussian priors. |
| Outcome: | The proposed model outperforms existing models on the dialogue generation task. |
Synchronous Refinement for Neural Machine Translation (2022.findings-acl)
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| Challenge: | Existing approaches to decode target sentences face a one-pass issue . generated wrong words are added to the historical context to affect the generation of subsequent target words, which hinders the performance of machine translation. |
| Approach: | They propose a synchronous refinement method to revise potential errors in the generated words by considering part of the target future context. |
| Outcome: | The proposed method can refine generated target words and generate the next target word synchronously. |
ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition (2022.naacl-main)
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| Challenge: | Recent work on Multi-modal Named Entity Recognition (MNER) relies on image information to model interactions between image and text representations. |
| Approach: | They propose to align image features into the textual space to better utilize attention mechanisms . they use regional object tags, captions and optical characters as visual contexts . |
| Outcome: | The proposed model can achieve state-of-the-art accuracy on multi-modal Named Entity Recognition datasets even without image information. |
One vs. Many QA Matching with both Word-level and Sentence-level Attention Network (C18-1)
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| Challenge: | Existing studies on question answer matching focus on formal text . however, there exists many scenarios where the QA text is informal . |
| Approach: | They propose a novel QA matching approach using informal text from a product review site. |
| Outcome: | The proposed approach improves word-level and sentence-level attentions for solving the noisy problem in the informal text. |
Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA (2024.emnlp-main)
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Minzheng Wang, Longze Chen, Fu Cheng, Shengyi Liao, Xinghua Zhang, Bingli Wu, Haiyang Yu, Nan Xu, Lei Zhang, Run Luo, Yunshui Li, Min Yang, Fei Huang, Yongbin Li
| Challenge: | Existing benchmarks for evaluating long-context language models employ irrelevant noise texts to artificially extend the length of test cases, diverging from the real-world scenarios of long-constituency applications. |
| Approach: | They propose a long-context benchmark, Loong, aligning with realistic scenarios through extended multi-document question answering (QA) . |
| Outcome: | The proposed model can scale up the context window of large language models to perform in-depth analysis of multiple long documents. |
Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network (P19-1)
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| Challenge: | Existing studies on aspect sentiment classification focus on non-interactive reviews . a new task aims to predict sentiment polarities for specific aspects from interactive reviews based on annotated corpus . |
| Approach: | They propose a task to predict aspects from interactive QA style reviews using an annotated corpus. |
| Outcome: | The proposed approach is compared with state-of-the-art methods against a high-quality corpus of data. |
Proxy-Driven Robust Multimodal Sentiment Analysis with Incomplete Data (2025.acl-long)
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| Challenge: | Existing studies focus on optimizing model structures to handle uncertain missingness, but models still face challenges when dealing with uncertain missing data. |
| Approach: | They propose a data-centric robust multimodal sentiment analysis method, Proxy-Driven Robust Multimodal Fusion, which maps unimodal data to the latent space of Gaussian distributions to capture core features and structure. |
| Outcome: | The proposed method outperforms existing models in noise resistance and achieves state-of-the-art performance on multiple benchmark datasets. |
NaviMaster: Learning a Unified Policy for GUI and Embodied Navigation Tasks (2026.acl-long)
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| Challenge: | Recent advances in Graphical User Interface (GUI) and embodied navigation have driven progress, yet these domains have largely evolved in isolation, with disparate datasets and training paradigms. |
| Approach: | They propose a visual-target trajectory collection pipeline that generates trajectories for GUI and embodied tasks using a single formulation. |
| Outcome: | The proposed agent outperforms state-of-the-art agents in GUI navigation, spatial affordance prediction, and embodied navigation. |
CTR-Guided Generative Query Suggestion in Conversational Search (2025.emnlp-industry)
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Erxue Min, Hsiu-Yuan Huang, Xihong Yang, Min Yang, Xin Jia, Yunfang Wu, Hengyi Cai, Junfeng Wang, Shuaiqiang Wang, Dawei Yin
| Challenge: | Generating effective query suggestions requires aligning model outputs with user click preferences. |
| Approach: | They propose a generative framework that leverages click modeling to denoise implicit feedback and enables reliable preference optimization for improving real-world user engagement. |
| Outcome: | The proposed framework outperforms strong baselines in CTR, relevance, diversity and diversity. |
RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering (2024.emnlp-main)
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Rujun Han, Yuhao Zhang, Peng Qi, Yumo Xu, Jenyuan Wang, Lan Liu, William Yang Wang, Bonan Min, Vittorio Castelli
| Challenge: | Existing datasets for question answering based on retrieval augmented generation (RAG-QA) are either constructed using a single source corpus or consist of short extractive answers, which fall short of evaluating large language model (LLM) based RAG-QA systems on cross-domain generalization. |
| Approach: | They propose a dataset that integrates short extractive answers from multiple documents into a single coherent narrative. |
| Outcome: | The proposed dataset integrates short extractive answers from multiple documents into a single coherent narrative, covering 26K queries and large corpora across seven different domains. |
Multi-choice Relational Reasoning for Machine Reading Comprehension (2020.coling-main)
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| Challenge: | cloze-style reading comprehension is a task that requires much semantic understanding and reasoning using various clues from texts. |
| Approach: | They propose a multi-choice relational reasoning model that emulates human reading comprehension by combining fusion representations of document, query and candidates. |
| Outcome: | The proposed model outperforms baseline models significantly on four datasets. |
Isotropic Representation Can Improve Zero-Shot Cross-Lingual Transfer on Multilingual Language Models (2023.findings-emnlp)
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| Challenge: | Existing mPLMs can align representations well for myriads of cross-lingual transfer tasks. |
| Approach: | They propose enhanced isotropy and constrained code-switching for zero-shot cross-lingual transfer to alleviate the problem of misalignment caused by anisotropic representations. |
| Outcome: | The proposed method improves on three zero-shot cross-lingual transfer tasks and over existing methods. |
Mitigating Hallucinations in Large Vision-Language Models via Entity-Centric Multimodal Preference Optimization (2025.emnlp-main)
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Jiulong Wu, Zhengliang Shi, Shuaiqiang Wang, Jizhou Huang, Dawei Yin, Lingyong Yan, Min Cao, Min Zhang
| Challenge: | Existing preference alignment methods focus on aligning model responses with human preferences while neglecting image-text modality alignment. |
| Approach: | They propose Entity-centric Multimodal Preference Optimization to improve modality alignment . they use open-source instruction datasets to automatically construct high-quality preference data . |
| Outcome: | The proposed approach reduces hallucination rates by 80.4% on Object HalBench and 52.6% on MM HalBech. |
Medico: Towards Hallucination Detection and Correction with Multi-source Evidence Fusion (2024.emnlp-demo)
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| Challenge: | Existing studies show that LLMs can confidently state non-existent facts rather than answering "I don't know". |
| Approach: | They propose a multi-source evidence fusion enhanced hallucination detection and correction framework that fuses evidence from multiple sources and iteratively revises the hallucinous content. |
| Outcome: | The proposed framework detects whether the generated content contains factual errors, provides the rationale behind the judgment, and iteratively revises the hallucinated content. |
Syntax-Aware Opinion Role Labeling with Dependency Graph Convolutional Networks (2020.acl-main)
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| Challenge: | Opinion role labeling (ORL) is a fine-grained opinion analysis task . due to the scarcity of labeled data, ORL remains challenging for data-driven methods due to its complexity and complexity. |
| Approach: | They propose to integrate syntactic knowledge into ORL models by comparing and integrating different representations and using dependency graph convolutional networks to encode parser information at different processing levels. |
| Outcome: | The proposed model achieves 4.34 higher F1 score than the current state-of-the-art. |
Rethinking Negative Instances for Generative Named Entity Recognition (2024.findings-acl)
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| Challenge: | Named Entity Recognition (NER) models are constrained by a pre-defined label set and require extensive human annotations, which limits their flexibility and adaptability to unseen tasks. |
| Approach: | They propose a Generative NER system that shows improved zero-shot performance across unseen entity domains by introducing contextual information and delineating label boundaries. |
| Outcome: | The proposed model outperforms state-of-the-art methods in zero-shot evaluation. |
DaNet: Dual-Aware Enhanced Alignment Network for Multimodal Aspect-Based Sentiment Analysis (2025.findings-acl)
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| Challenge: | Existing methods assume a direct alignment between images and aspects, matching the entire image with a corresponding aspect. Existing algorithms assume 'direct alignment' between images, introducing noise. |
| Approach: | They propose a Dual-Aware Enhanced Alignment Network (DaNet) that can enhance fine-grained multimodal aspect-image alignment and denoising. |
| Outcome: | The proposed system outperforms existing methods in three subtasks and is available on https://github.com/***/DaNet. |
Improving Chinese Named Entity Recognition with Multi-grained Words and Part-of-Speech Tags via Joint Modeling (2024.lrec-main)
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| Challenge: | Named entity recognition (CNER) is a fundamental task in natural language processing (NLP). |
| Approach: | They propose a tree parsing approach for jointly modeling Chinese named entity recognition (CNER) with multi-grained word segmentation (MWS) and POS tagging tasks. |
| Outcome: | The proposed approach achieves better or comparable performance with current methods. |
TasTe: Teaching Large Language Models to Translate through Self-Reflection (2024.acl-long)
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| Challenge: | Existing approaches to enhance LLMs' performance in machine translation are unable to fully exploit their instruction-following capabilities. |
| Approach: | They propose a framework for translating through self-reflection that involves two stages of inference . they propose to use the framework to refine LLMs' preliminary translations . |
| Outcome: | The proposed framework can produce translation outputs that match the quality of NMT systems. |
Progressive Self-Training with Discriminator for Aspect Term Extraction (2021.emnlp-main)
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| Challenge: | Existing approaches to extract aspect terms from review sentences are limited due to lack of annotated data. |
| Approach: | They propose to refine conventional self-training to progressive self-teaching to reduce noise . they use a discriminator to filter the noisy pseudo-labels. |
| Outcome: | The proposed model outperforms baseline models and achieves state-of-the-art performance on four SemEval datasets. |
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms (P18-1)
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Dinghan Shen, Guoyin Wang, Wenlin Wang, Martin Renqiang Min, Qinliang Su, Yizhe Zhang, Chunyuan Li, Ricardo Henao, Lawrence Carin
| Challenge: | Existing deep learning architectures to model compositionality in text sequences require a large number of parameters and expensive computations. |
| Approach: | They propose two additional pooling strategies over word embeddings for improved interpretability and hierarchical pooling for spatial (n-gram) information within text sequences. |
| Outcome: | The proposed pooling strategies improve interpretability and preserve spatial (n-gram) information within text sequences. |
Sentiment Classification towards Question-Answering with Hierarchical Matching Network (D18-1)
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Chenlin Shen, Changlong Sun, Jingjing Wang, Yangyang Kang, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, Guodong Zhou
| Challenge: | Existing methods to classify QA text contain rich sentiment information. |
| Approach: | They propose a task/method to address QA sentiment analysis by annotating QA text pair with annotation guidelines. |
| Outcome: | The proposed method can learn the matching vectors of each Q-sentence, A-sentent unit. |
Improving Gradient Trade-offs between Tasks in Multi-task Text Classification (2023.acl-long)
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| Challenge: | Existing methods to mitigate task conflict problem are heuristics or gradient-based algorithms to achieve an arbitrary Pareto optimal trade-off among different tasks . |
| Approach: | They propose a gradient trade-off approach to mitigate the task conflict problem by using heuristics or gradient-based algorithms to achieve an arbitrary Pareto optimal trade- off among different tasks. |
| Outcome: | The proposed model can achieve an arbitrary Pareto optimal trade-off among different tasks near the main objective of multi-task text classification (MTC) it is found that training all tasks simultaneously yields degraded performance than learning them independently, leading to poor training. |
Unsupervised Sign Language Translation and Generation (2024.findings-acl)
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Zhengsheng Guo, Zhiwei He, Wenxiang Jiao, Xing Wang, Rui Wang, Kehai Chen, Zhaopeng Tu, Yong Xu, Min Zhang
| Challenge: | Experimental results on the BBC-Oxford Sign Language dataset reveal that USLNet achieves competitive results compared to supervised baseline models. |
| Approach: | They propose an unsupervised sign language translation and generation network that learns from abundant single-modality data without parallel sign language data. |
| Outcome: | The proposed model achieves competitive results compared to baseline models on the BBC-Oxford Sign Language dataset and Open-Domain American Sign Language data. |
TemplateGEC: Improving Grammatical Error Correction with Detection Template (2023.acl-long)
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| Challenge: | Existing methods for grammatical error correction (GEC) have been developed. |
| Approach: | They propose a method which integrates the detection labels from a Seq2Edit model to construct a template as the input. |
| Outcome: | The proposed method can perform human-in-the-loop error correction tasks. |
Tool learning via Inference-time Scaling and Cycle Verifier (2025.findings-acl)
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| Challenge: | In inference-time scaling, Chain-of-Thought (CoT) data is scarce or even unavailable. |
| Approach: | They propose a method which establishes an inference cycle to synthesize user queries and CoT data. |
| Outcome: | The proposed method achieves a 75.4% pass rate and a 79.6% win rate using small models in StableToolBench. |
Neural Sparse Topical Coding (P18-1)
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| Challenge: | Topic models with sparsity enhancement are effective at learning discriminative and coherent latent topics of short texts. |
| Approach: | They propose a novel sparsity-enhanced topic model with back propagation that replaces the inference process with the back propagations, making it easy to explore extensions. |
| Outcome: | The proposed model outperforms existing methods on Web Snippet and 20Newsgroups datasets. |
MelTrim: Coarse-to-Fine Data Pruning for Speech Classification (2026.findings-acl)
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Shaobo Wang, Tianle Niu, Xuan Ouyang, Xintong Li, Zhengkun Ge, Yue Min, Xiaoqian Liu, Hankun Wang, Linfeng Zhang
| Challenge: | Unlike image or text classification, speech classification tasks are particularly challenging due to the difficulty in capturing the acoustic, semantic, and contextual representations. |
| Approach: | They propose a dataset pruning method that coarsely filters redundant samples using DBSCAN clustering on Mel-Frequency Cepstral Coefficients (MFCC) features. |
| Outcome: | The proposed method achieves 49.5% improvement in WA on the MEAD dataset and 41.9% reduction in EER on speaker identification tasks. |
DuSQL: A Large-Scale and Pragmatic Chinese Text-to-SQL Dataset (2020.emnlp-main)
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| Challenge: | Existing text-to-SQL parsing methods mainly focus on English, but there is no labeled data available for the language . a larges-scale and pragmatic Chinese dataset is used for cross-domain text- to-Sql task . |
| Approach: | They propose a larges-scale Chinese dataset for a cross-domain text-to-SQL task . they analyze questions from several representative applications and use an effective data construction framework . |
| Outcome: | The proposed dataset contains 200 databases, 813 tables, and 23,797 question/SQL pairs. |
In-context Learning for Few-shot Multimodal Named Entity Recognition (2023.findings-emnlp)
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| Challenge: | Existing methods for named entity recognition are time-consuming and laborintensive. |
| Approach: | They propose a few-shot multimodal named entity recognition task that uses few examples to locate and identify named entities for a text-image pair. |
| Outcome: | The proposed framework outperforms baselines under several few-shot settings. |
Unlocking Recursive Thinking of LLMs: Alignment via Refinement (2025.findings-acl)
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| Challenge: | Existing methods for recursive reasoning are limited due to lack of expert-curated data. |
| Approach: | They propose a method that unlocks the potential of Large Language Models for recursive reasoning through long-form Chain of Thought. |
| Outcome: | The proposed method outperforms preference optimization methods on the openAI o1-series models by 20% on 3k synthetic samples. |
Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing (2021.emnlp-main)
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| Challenge: | Existing approaches to parse text-to-SQL data are lacking labeled data for unseen evaluation databases. |
| Approach: | They propose a framework for enhancing SQL queries by automatically producing large numbers of SQL queries based on an abstract syntax tree grammar. |
| Outcome: | The proposed framework can produce high-quality natural language questions over strong baselines. |
SR-RAG: Verifiable Multi-Hop Reasoning via On-the-fly Symbolic Graph Construction (2026.findings-acl)
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| Challenge: | Existing paradigms for multi-hop reasoning suffer from high construction costs and limited adaptability to dynamic knowledge. |
| Approach: | They propose a symbolic reasoning framework for multi-hop question answering that integrates the advantages of both paradigms by dynamically generating sub-questions, performing information retrieval and symbolic encoding based on an on-the-fly graph and using a symbol verifier to validate intermediate reasoning steps. |
| Outcome: | The proposed framework significantly improves accuracy and robustness on multiple multi-hop benchmarks and a medical dataset. |
TInR: Exploring Tool-Internalized Reasoning in Large Language Models (2026.acl-long)
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| Challenge: | Existing methods rely on external tool documentation during reasoning, leading to tool mastery difficulty, tool size constraints, and inference inefficiency. |
| Approach: | They propose a tool-internalized reasoning framework for unified reasoning and tool usage that integrates external tools into Large Language Models (LLMs) to address these issues, they propose 'tool-internet-based' reasoning. |
| Outcome: | The proposed method achieves superior performance across in-domain and out-of-domain settings, highlighting its effectiveness and efficiency. |
APGN: Adversarial and Parameter Generation Networks for Multi-Source Cross-Domain Dependency Parsing (2021.findings-emnlp)
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| Challenge: | Existing models for dependency parsing use labeled training data for several fixed domains, but performance drops when labeles only exist for several out-domains. |
| Approach: | They propose a model for multi-source cross-domain dependency parsing that uses a parameter generation network and adversarial network for learning domain-invariant representations. |
| Outcome: | The proposed model improves cross-domain parsing performance by about 2 points over strong BERT-enhanced baselines over a recently released dataset for multi-domain dependency parse. |
New Terms, New Toxicity: Consensus-based Chinese Neologism Toxicity Detection via Search-Augmented LLMs (2026.acl-long)
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Shiyao Cui, QingLin Zhang, Di Wang, Yida Lu, Zhexin Zhang, Jinhua Gao, Jinglin Yang, Min He, Han Qiu, Minlie Huang
| Challenge: | Neologisms can foster new linguistic consensus by stabilizing shared meanings and usage in common communicative norms. |
| Approach: | They propose a taxonomy that captures the origins and consensus-verification criteria of toxic neologisms . they propose 'SeTox' framework that integrates real-time web context for naeologim detection . |
| Outcome: | The proposed framework outperforms large-scale models in detecting neologism toxicity. |
Disambiguated Lexically Constrained Neural Machine Translation (2023.findings-acl)
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| Challenge: | Current approaches to LCNMT assume that pre-specified lexicon constraints are contextually appropriate. |
| Approach: | They propose a framework that disambiguates constraints based on contexts at first and integrates them into LCNMT. |
| Outcome: | The proposed approach outperforms baseline approaches on benchmark datasets and comprehensive experiments in multiple target constraints. |
Unlocking General Long Chain-of-Thought Reasoning Capabilities of Large Language Models via Representation Engineering (2025.acl-long)
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| Challenge: | Existing work finds that long CoT reasoning can be efficiently elicited by tuning on only a few examples and can easily transfer to other tasks. |
| Approach: | They propose a representation engineering method to unleash the general long CoT reasoning capabilities of LLMs. |
| Outcome: | The proposed method is effective in in-domain and cross-domain scenarios. |
LLMBox: A Comprehensive Library for Large Language Models (2024.acl-demos)
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Tianyi Tang, Hu Yiwen, Bingqian Li, Wenyang Luo, ZiJing Qin, Haoxiang Sun, Jiapeng Wang, Shiyi Xu, Xiaoxue Cheng, Geyang Guo, Han Peng, Bowen Zheng, Yiru Tang, Yingqian Min, Yushuo Chen, Jie Chen, Ranchi Zhao, Luran Ding, Yuhao Wang, Zican Dong, Xia Chunxuan, Junyi Li, Kun Zhou, Xin Zhao, Ji-Rong Wen
| Challenge: | a library to facilitate the development, use, and evaluation of large language models (LLMs) is presented. |
| Approach: | They propose a unified library to facilitate the development, use and evaluation of large language models (LLMs). |
| Outcome: | The proposed library is based on extensive experiments in a variety of evaluation settings. |
LLMs + Persona-Plug = Personalized LLMs (2025.acl-long)
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Jiongnan Liu, Yutao Zhu, Shuting Wang, Xiaochi Wei, Erxue Min, Yu Lu, Shuaiqiang Wang, Dawei Yin, Zhicheng Dou
| Challenge: | Large language models (LLMs) have demonstrated extraordinary capabilities in natural language understanding, generation, and reasoning. |
| Approach: | They propose a plug-and-play LLM model that embeds a user-specific embedding for each individual by modeling her historical contexts through a lightweight plug-in user embedder module. |
| Outcome: | Experiments on various tasks in the language model personalization (LaMP) benchmark show that the proposed model significantly outperforms existing personalized LLM approaches. |
VLN-NF: Feasibility-Aware Vision-and-Language Navigation with False-Premise Instructions (2026.acl-long)
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| Challenge: | Existing Vision-and-Language Navigation benchmarks assume instructions are feasible and the referenced target exists. |
| Approach: | They propose a benchmark with false-premise instructions where the target is absent . they propose supervised room-level navigation with LLM/VLM-driven in-room exploration . |
| Outcome: | The proposed benchmark produces false-premise goals that are plausible but factually incorrect . ROAM achieves the best REV-SPL among compared methods, while baselines often under-explore and terminate prematurely under unreliable instructions. |
MultiSkill: Evaluating Large Multimodal Models for Fine-grained Alignment Skills (2024.findings-emnlp)
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| Challenge: | Existing evaluation settings for large multimodal models focus on coarse-grained evaluation without considering skill composition required by specific instructions. |
| Approach: | They propose an evaluation protocol that assesses large multimodal models across multiple fine-grained skills for alignment with human values. |
| Outcome: | The proposed evaluation protocol decomposes coarse-level scoring to fine-grained skill set-level score tailored to each instruction. |
MidPO: Dual Preference Optimization for Safety and Helpfulness in Large Language Models via a Mixture of Experts Framework (2025.findings-emnlp)
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| Challenge: | Recent studies address safety-constrained online and offline preferences optimizations, but offline methods perform poorly in adaptively balancing safety and helpfulness. |
| Approach: | They propose a mixture of experts framework for safety-helpfulness dual Preference Optimization . they combine a single-preference enhanced direct preference optimization approach with a dynamic routing mechanism . |
| Outcome: | The proposed framework outperforms state-of-the-art methods in safety and helpfulness. |
CopyNE: Better Contextual ASR by Copying Named Entities (2024.acl-long)
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| Challenge: | Existing approaches to transcribe contextual named entities (NEs) treat entities as tokens and generate them token-by-token, which may result in incomplete transcriptions of entities. |
| Approach: | They propose a mechanism that can copy entities from the NE dictionary and reduce errors during entity transcription. |
| Outcome: | The proposed mechanism can copy entities from the NE dictionary, reducing errors during entity transcription, ensuring the completeness of the entity. |
LastingBench: Defend Benchmarks Against Knowledge Leakage (2025.findings-emnlp)
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| Challenge: | Existing methods to detect and safeguard LLMs against knowledge leakage fail to address the long-term challenge of mitigating it. |
| Approach: | They propose a method to reinforce and safeguard existing benchmarks against knowledge leakage by perturbation-based detection and counterfactual rewriting to disrupt memorization while preserving original intent. |
| Outcome: | The proposed method reduces memorization effects in long-context QA benchmarks, providing a more accurate assessment of model reasoning and generalization abilities. |
DynamicKV: Task-Aware Adaptive KV Cache Compression for Long Context LLMs (2025.findings-emnlp)
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| Challenge: | Existing KV cache compression methods enforce a fixed pattern, neglecting task-specific characteristics, which hampers the effective retention of essential information while discarding less important tokens. |
| Approach: | They propose a Task-Aware KV cache mechanism that dynamically adjusts the KV caching size across different layers based on the characteristics of the tasks. |
| Outcome: | The proposed method surpasses state-of-the-art methods by 11% on the LongBench dataset even under extreme compression (0.9%) |
Scaling Laws Across Model Architectures: A Comparative Analysis of Dense and MoE Models in Large Language Models (2024.emnlp-main)
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| Challenge: | a study of large language models (LLMs) reveals the transferability and discrepancies of scaling laws between Dense and MoE models. |
| Approach: | They investigate the transferability and discrepancies of scaling laws between Dense Models and Mixture of Experts models. |
| Outcome: | The results show that the power-law scaling framework also applies to MoE Models . |
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. |
VideoVista-CulturalLingo: 360° Horizons-Bridging Cultures, Languages, and Domains in Video Comprehension (2025.acl-long)
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| Challenge: | Existing video evaluation benchmarks focus on a single language, typically English, and feature videos rooted in Western cultural contexts. |
| Approach: | They propose a video evaluation benchmark designed to bridge cultural, linguistic, and domain divide in video comprehension. |
| Outcome: | The proposed video evaluation benchmark bridges cultural, linguistic, and domain divides . existing benchmarks only feature videos from YouTube, Shutterstock, or established video datasets based on cultural diversity . |
Code-Switching for Enhancing NMT with Pre-Specified Translation (N19-1)
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| Challenge: | Existing methods to constrain NMT use placeholder tags for lexicon words and hard constraints during decoding. |
| Approach: | They propose to use placeholder tags to replace lexicon words with target translations . they use a data augmentation method to make code-switched training data . |
| Outcome: | The proposed method improves translation quality without hurting unconstrained words. |
From Verbatim to Gist: Distilling Pyramidal Multimodal Memory via Semantic Information Bottleneck for Long-Horizon Video Agents (2026.acl-long)
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| Challenge: | Existing multimodal large language models struggle with long-horizon video understanding due to limited context windows and static memory mechanisms that fail to mirror human cognitive efficiency. |
| Approach: | They propose a pyramidal multimodal memory architecture grounded in Fuzzy-Trace Theory that structures memory hierarchically into a *Sensory Buffer*, *Episodic Stream*, and *Symbolic Schema*. |
| Outcome: | The proposed architecture achieves state-of-the-art on both offline and streaming tasks, demonstrating robust generalization and validating the effectiveness of cognition-inspired memory organization. |
Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning (D19-1)
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Jingjing Wang, Changlong Sun, Shoushan Li, Jiancheng Wang, Luo Si, Min Zhang, Xiaozhong Liu, Guodong Zhou
| Challenge: | Recent neural networks have shown promising results on Document-level Aspect Sentiment Classification (DASC) however, these approaches often offer little transparency w.r.t. their inner working mechanisms and lack interpretability. |
| Approach: | They propose a Hierarchical Reinforcement Learning approach to DASC that incorporates clause selection and word selection strategies to tackle the data noise problem. |
| Outcome: | The proposed approach over the state-of-the-art approaches shows impressive performance over the current baselines. |
A Survey of Generative Information Extraction (2025.coling-main)
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| Challenge: | Information Extraction (IE) is a popular and fundamental task in natural language processing. |
| Approach: | They first review generative information extraction methods based on pre-trained language models and large language models focusing on their adaptation and generalization capabilities. |
| Outcome: | The proposed methods are based on pre-trained language models and large language models, and emphasize the importance of model collaboration. |
Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models (2024.lrec-main)
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Oana Ignat, Zhijing Jin, Artem Abzaliev, Laura Biester, Santiago Castro, Naihao Deng, Xinyi Gao, Aylin Ece Gunal, Jacky He, Ashkan Kazemi, Muhammad Khalifa, Namho Koh, Andrew Lee, Siyang Liu, Do June Min, Shinka Mori, Joan C. Nwatu, Veronica Perez-Rosas, Siqi Shen, Zekun Wang, Winston Wu, Rada Mihalcea
| Challenge: | Recent advances in large language models have led to misleading public discourse that “it’s all been solved.” |
| Approach: | They identify 14 research areas encompassing 45 research directions that require new research and are not directly solvable by LLMs. |
| Outcome: | The research areas identified are 45 research directions that require new research and are not directly solvable by LLMs. |
CoTJudger: A Graph-Driven Framework for Automatic Evaluation of Chain-of-Thought Efficiency and Redundancy in LRMs (2026.findings-acl)
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Siyi Li, Jiajun Shi, Shiwen Ni, Ge Zhang, Shuaimin Li, Shijian Wang, Zhoufutu Wen, Yizhi LI, Hamid Alinejad-Rokny, Jiaheng Liu, Min Yang, Wenhao Huang
| Challenge: | Existing evaluations emphasize final accuracy or coarse token counts, and lack automated tools to separate essential logic from structural redundancy. |
| Approach: | They propose a graph-driven framework that quantifies reasoning efficiency by converting free-form CoTs into directed dependency graphs and extracting the Shortest Effective Path needed to reach a correct solution. |
| Outcome: | Evaluating 21 LRMs, the proposed framework quantifies reasoning efficiency by converting free-form CoTs into directed dependency graphs and extracting the Shortest Effective Path (SEP) needed to reach a correct solution. |
G-SPEED: General SParse Efficient Editing MoDel (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have demonstrated incredible capabilities in understanding, generating, and manipulating languages. |
| Approach: | They propose a general SParse Efficient Editing MoDel which can fulfill diverse editing requirements through a single model while maintaining low computational costs. |
| Outcome: | The proposed model can fulfill diverse editing requirements through a single model while maintaining low computational costs. |
Step-GRPO: Internalizing Dynamic Early Exit for Efficient Reasoning (2026.acl-long)
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| Challenge: | Large reasoning models that use long chain-of-thought excel at problem-solving but waste computational resources. |
| Approach: | They propose a framework that internalizes dynamic early-exit capabilities directly into the model. |
| Outcome: | The proposed framework reduces token consumption by 32.0% on a Qwen3-8B model compared to the vanilla model . |
CiteEval: Principle-Driven Citation Evaluation for Source Attribution (2025.acl-long)
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Yumo Xu, Peng Qi, Jifan Chen, Kunlun Liu, Rujun Han, Lan Liu, Bonan Min, Vittorio Castelli, Arshit Gupta, Zhiguo Wang
| Challenge: | Current evaluation frameworks rely on NLI to assess binary or ternary support from cited sources, which is suboptimal for citation evaluation. |
| Approach: | They propose a citation evaluation framework based on fine-grained citation ratings within a broad context and construct a multi-domain benchmark with high-quality human annotations. |
| Outcome: | The proposed framework provides a high-quality human annotation benchmark and a suite of model-based metrics that exhibit strong correlation with human judgments. |
Speed Up Your Code: Progressive Code Acceleration Through Bidirectional Tree Editing (2025.acl-long)
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Longhui Zhang, Jiahao Wang, Meishan Zhang, GaoXiong Cao, Ensheng Shi, Mayuchi Mayuchi, Jun Yu, Honghai Liu, Jing Li, Min Zhang
| Challenge: | Existing training methods, such as direct instruction fine-tuning, overlook hierarchical relationships among acceleration patterns. |
| Approach: | They propose a new training paradigm that uses bidirectional tree editing and progressive code acceleration learning to improve LLMs’ CA capabilities. |
| Outcome: | The proposed training paradigm outperforms prompt-enhanced GPT-4 and current training-based methods on average across five programming languages. |
From Awareness to Adaptability: Enhancing Tool Utilization for Scientific Reasoning (2025.findings-acl)
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| Challenge: | Existing approaches enhance reasoning through Chain-of-Thought, Program-ofThough, and Tool-Integration. |
| Approach: | They propose a tool-awareness training method that leverages both forward and backward data generation strategies to strengthen the model’s conscious and selective tool utilization in multi-step reasoning tasks. |
| Outcome: | The proposed method improves the model's tool utilization capabilities, including proactivity and execution success rates. |
A Unified Agentic Framework for Evaluating Conditional Image Generation (2025.acl-long)
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Jifang Wang, Yangxue Yangxue, Longyue Wang, Zhenran Xu, Yiyu Wang, Yaowei Wang, Weihua Luo, Kaifu Zhang, Baotian Hu, Min Zhang
| Challenge: | Conditional image generation is a popular and personalization-oriented task, but there are challenges in developing task-agnostic, reliable, and explainable evaluation metrics. |
| Approach: | They propose a unified agentic framework for comprehensive evaluation of conditional image generation tasks. |
| Outcome: | The proposed framework achieves a high correlation with human assessments on seven prominent image generation tasks. |
BatonVoice: An Operationalist Framework for Enhancing Controllable Speech Synthesis with Linguistic Intelligence from LLMs (2026.acl-long)
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Yue Wang, Ruotian Ma, Xingyu Chen, Zhengliang Shi, Morunliu Yang, Wanshun Chen, Huang Liu, Jiadi Yao, Xin He, Qu Yang, Qingxuan Jiang, Fanghua Ye, Juntao Li, Zhaopeng Tu, Xiaolong Li, Liefeng Bo, Min Zhang
| Challenge: | Existing approaches often fail to leverage the linguistic intelligence of Large Language Models (LLMs) Existing models lack the ability to follow text instructions for controllable Text-to-Speech (TTS). |
| Approach: | They propose a framework where an LLM acts as a conductor, understanding user instructions and generating a textual plan - explicit vocal features. |
| Outcome: | The proposed model outperforms open- and closed-source models in speech synthesis and achieves zero-shot cross-lingual generalization. |
Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction (2020.coling-main)
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| Challenge: | Distant Supervision (DS) generates large-scale annotated data but has wrong labels that result in incorrect evaluation scores during testing. |
| Approach: | They build a dataset using DS-generated data as training data and hire annotators to label test data. |
| Outcome: | The proposed dataset NYTH has a much larger test set and performs more accurate and consistent evaluation. |
Enhancing EEG-to-Text Decoding through Transferable Representations from Pre-trained Contrastive EEG-Text Masked Autoencoder (2024.acl-long)
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| Challenge: | EEG-based language decoding is still in its nascent stages, despite promising applications in brain-computer interfaces. |
| Approach: | They propose a novel EEG-text Masked Autoencoder that orchestrates compound self-supervised learning across and within EEG and text through a dedicated multi-stream encoder. |
| Outcome: | The proposed model outperforms baseline framework in ROUGE-1 F1 and BLEU-4 scores and an LLM (specifically BART) to improve downstream tasks involving EEG and text. |
Confusion is the Final Barrier: Rethinking Jailbreak Evaluation and Investigating the Real Misuse Threat of LLMs (2025.findings-emnlp)
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Yu Yan, Sheng Sun, Zhe Wang, Yijun Lin, Zenghao Duan, Zhifei Zheng, Min Liu, Zhiyi Yin, Jianping Zhang
| Challenge: | Large Language Models have been developed to deal with real-world crimes, but it remains unclear whether they internalize authentic knowledge or are forced to simulate toxic language patterns. |
| Approach: | They construct knowledge-intensive Q&A to investigate misuse threats of Large Language Models in terms of dangerous knowledge possession, harmful task planning utility, and harmfulness judgment robustness. |
| Outcome: | The findings raise concerns that jailbreak success is often attributable to a hallucination loop between jailbroken LLM and judger LLM . |
Memory-augmented Query Reconstruction for LLM-based Knowledge Graph Reasoning (2025.findings-acl)
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| Challenge: | Existing methods that confuse tool utilization with knowledge reasoning harm readability and give rise to tool invocation hallucinations. |
| Approach: | They propose to decouple LLM from tool invocation tasks by establishing a memory module with explicit descriptions of query statements and a query memory module to facilitate the KGQA process. |
| Outcome: | The proposed method achieves state-of-the-art on WebQSP and CWQ benchmarks. |
Efficient Table Retrieval and Understanding with Multimodal Large Language Models (2026.findings-eacl)
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| Challenge: | Tabular data is often captured in image form across a wide range of real-world scenarios. |
| Approach: | They propose a framework that enables MLLMs to answer queries over large tables. |
| Outcome: | The proposed framework outperforms existing methods by 7.0% in retrieval recall and 6.1% in answer accuracy on a newly constructed dataset with 48,504 unique tables. |
Orchestrating Tokens and Sequences: Dynamic Hybrid Policy Optimization for RLVR (2026.findings-acl)
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| Challenge: | Existing RLVR algorithms focus on different granularities and have complementary strengths and limitations. |
| Approach: | They propose a framework for reinforcement learning with verifiable rewards that bridges RLVR and GSPO . group-level importance ratios are used to update a policy, which preserves fine-grained credit assignment . |
| Outcome: | The proposed framework outperforms existing methods on seven reasoning benchmarks. |
Semi-supervised Domain Adaptation for Dependency Parsing (P19-1)
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| Challenge: | Currently, most studies on cross-domain parsing focus on unsupervised domain adaptation . however, unsupervised approaches make limited progress due to the intrinsic difficulty of both domain adaptation and parse. |
| Approach: | They propose a semi-supervised domain adaptation problem for Chinese dependency parsing by using newly-annotated large-scale domain-aware datasets. |
| Outcome: | The proposed method is more effective than direct corpus concatenation and multi-task learning. |