Papers by Min Zhang
Team-Based Self-Play With Dual Adaptive Weighting for Fine-Tuning LLMs (2026.acl-long)
Copied to clipboard
| 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. |
MTSA: Multi-turn Safety Alignment for LLMs through Multi-round Red-teaming (2025.acl-long)
Copied to clipboard
| 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. |
When to Trust Tools? Adaptive Tool Trust Calibration For Tool-Integrated Math Reasoning (2026.findings-acl)
Copied to clipboard
| Challenge: | Large reasoning models have performance enhancements but still suffer from shortcomings due to limitations of the underlying language models. |
| Approach: | They propose a framework that allows the model to choose when to trust or ignore the tool results based on the confidence score of generated code blocks. |
| Outcome: | The proposed framework reduces the "Tool Ignored" issue by 4.1% to 7.5%. |
Chinese Sequence Labeling with Semi-Supervised Boundary-Aware Language Model Pre-training (2024.lrec-main)
Copied to clipboard
| Challenge: | Pretrained language models (PLMs) have been successful in addressing word boundaries in Chinese sequence labeling tasks, but they rarely consider boundary information explicitly. |
| Approach: | They propose a method to integrate unsupervised boundary information into Chinese BERT's pre-training objectives and a supervised boundary-aware PLM. |
| Outcome: | The proposed model outperforms the vanilla version on Chinese sequence labeling tasks and in broader Chinese natural language understanding tasks. |
OmniCharacter: Towards Immersive Role-Playing Agents with Seamless Speech-Language Personality Interaction (2025.acl-long)
Copied to clipboard
Haonan Zhang, Run Luo, Xiong Liu, Yuchuan Wu, Ting-En Lin, Pengpeng Zeng, Qiang Qu, Feiteng Fang, Min Yang, Lianli Gao, Jingkuan Song, Fei Huang, Yongbin Li
| Challenge: | Existing methods focus on replicating dialogues in textual form, neglecting the role’s voice traits as a crucial effect in interaction, which tends to be more immersive experiences in realistic scenarios. |
| Approach: | They propose a first seamless speech-language personality interaction model to achieve immersive RPAs with low latency. |
| Outcome: | The proposed model exhibits role-specific personality traits and vocal traits throughout the interaction, enabling a mixture of speech and language responses. |
Improving In-Context Learning with Prediction Feedback for Sentiment Analysis (2024.findings-acl)
Copied to clipboard
| 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. |
SumSurvey: An Abstractive Dataset of Scientific Survey Papers for Long Document Summarization (2024.findings-acl)
Copied to clipboard
| Challenge: | a growing need for long document summarization datasets with 16k input is causing problems. |
| Approach: | They propose to use a dataset to analyze salient information in long document summarizations. |
| Outcome: | The proposed dataset outperforms existing models and LLMs in the distribution form of salient information and the distribution of salinal information is an indicator of quality. |
An In-depth Study on Internal Structure of Chinese Words (2021.acl-long)
Copied to clipboard
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. |
LLM-based Translation Inference with Iterative Bilingual Understanding (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing studies show that the ability of large language models to generate contextual understanding of the sentence can degrade translation quality. |
| Approach: | They propose a method that generates contextual understanding for both source and target languages separately. |
| Outcome: | The proposed method outperforms strong comparison methods in multiple domains. |
Achieving Stronger Generation via Simple Contrastive Tuning (2024.findings-emnlp)
Copied to clipboard
| 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. |
Towards Better Hierarchical Text Classification with Data Generation (2023.findings-acl)
Copied to clipboard
| 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)
Copied to clipboard
| 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. |
Your Language Model Can Secretly Write Like Humans: Contrastive Paraphrase Attacks on LLM-Generated Text Detectors (2025.emnlp-main)
Copied to clipboard
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. |
Measuring and Narrowing the Compositionality Gap in Language Models (2023.findings-emnlp)
Copied to clipboard
| Challenge: | a language model can correctly answer all sub-problems but not generate the overall solution. |
| Approach: | They propose a method that asks itself and then answers follow-up questions to narrow the compositionality gap by reasoning explicitly instead of implicitly. |
| Outcome: | The proposed method improves on chain of thought by asking itself and answering follow-up questions. |
Cognitive Visual-Language Mapper: Advancing Multimodal Comprehension with Enhanced Visual Knowledge Alignment (2024.acl-long)
Copied to clipboard
| Challenge: | Recent Large Multimodal Models (LMMs) focus on visual knowledge-dimension alignment, but ignore visual knowledge. |
| Approach: | They propose a cognitive visual-language mapper that integrates visual-linguistic knowledge alignment with a fine-grained knowledge Adapter. |
| Outcome: | The proposed model significantly improves LMMs on knowledge-based visual question answering (VQA) it also improves the performance of other models, including GPT-4V and Gemini-Pro. |
Bridging Subword Gaps in Pretrain-Finetune Paradigm for Natural Language Generation (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods to pretrain language models are limited by one-size-fits-all vocabulary . embeddings of mismatch tokens can be efficiently initialized in downstream tasks . |
| Approach: | They propose to extend pretrain-finetune pipeline with an embedding transfer step . plug-and-play embeddable generator is introduced to generate any input token . |
| Outcome: | The proposed approach allows for more efficient and better performed NLG models. |
Encouraging Lexical Translation Consistency for Document-Level Neural Machine Translation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Experimental results show document-level neural machine translation improves lexical consistency . inconsistent translations tend to confuse readers in some cases . |
| Approach: | They propose to use a word link to obtain a document word link and an auxiliary loss function to constrain that their translation should be consistent. |
| Outcome: | The proposed approach improves translation consistency on ChineseEnglish and EnglishFrench translation tasks. |
Hierarchical Acoustic-Semantic Modeling: Modality Separation and Semantic Coherence for Full-Duplex SLMs (2026.acl-long)
Copied to clipboard
Zhenyu Liu, Xuanyu Zhang, Yunxin li, Qixun Teng, Shenyuan Jiang, Haolan Chen, Mingjun Zhao, Fanbo Meng, Yu Xu, Yancheng He, Baotian Hu, Haizhou Li, Min Zhang
| Challenge: | despite significant progress, full-duplex SLMs are constrained by severe modality interference, authors say . modality interferes with acoustic and semantic modeling, making them unintelligent and unnatural . authors propose a hierarchical parameter separation strategy that decouples conflicting modalities in deep layers . |
| Approach: | They propose a hierarchical parameter separation strategy that decouples conflicting modalities in deep layers while preserving cross-modality coherence via a dedicated semantic alignment channel. |
| Outcome: | The proposed method significantly advances the state of the art on full-duplex benchmarks . it decouples conflicting modalities in deep layers while preserving cross-modality coherence . |
An Empirical Study of Iterative Refinements for Non-autoregressive Translation (2025.acl-long)
Copied to clipboard
| Challenge: | Iterative non-autoregressive (NAR) models have recently demonstrated impressive performance in varied generation tasks, surpassing the autoregressive Transformer. |
| Approach: | They propose a strategy to conduct efficient refinements without performance declines by using two simple metrics to identify potential problems existing in current refinement processes. |
| Outcome: | The proposed model outperforms the autoregressive Transformer by around one BLEU on average. |
FunnelRAG: A Coarse-to-Fine Progressive Retrieval Paradigm for RAG (2025.findings-naacl)
Copied to clipboard
| Challenge: | Retrieval-Augmented Generation (RAG) is widely adopted in Large Language Models, but is flat and has limitations such as a significant burden on one retriever and constant granularity limits the ceiling of retrieval performance. |
| Approach: | They propose a progressive retrieval paradigm with coarse-to-fine granularity for RAG, termed FunnelRAG, so as to balance effectiveness and efficiency. |
| Outcome: | The proposed paradigm achieves comparable retrieval performance while the time overhead is reduced by nearly 40%. |
A Unified Span-Based Approach for Opinion Mining with Syntactic Constituents (2021.naacl-main)
Copied to clipboard
| 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. |
A User-Centric Multi-Intent Benchmark for Evaluating Large Language Models (2024.emnlp-main)
Copied to clipboard
| 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. |
Synergetic Event Understanding: A Collaborative Approach to Cross-Document Event Coreference Resolution with Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to cross-document event coreference resolution are prone to learning simple co-occurrences due to the complexity of contexts. |
| Approach: | They propose a collaborative approach to cross-document event coreference resolution that leverages both a universally capable LLM and a task-specific SLM. |
| Outcome: | The proposed approach surpasses the performance of both large and small language models individually, underscoring its effectiveness in diverse scenarios. |
Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation (2022.acl-long)
Copied to clipboard
| 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)
Copied to clipboard
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. |
An Empirical Study of Frame Selection for Text-to-Video Retrieval (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for text-to-video retrieval select a subset of frames to represent video content . current methods only explore video contents while ignoring relevancy to texts . |
| Approach: | They propose to use a subset of frames to represent video content for TVR . they analyze six different frame selection methods to determine their effectiveness . |
| Outcome: | The proposed method improves retrieval efficiency without sacrificing visual details . the proposed method explores the video contents while ignoring relevancy to texts . |
When Words Smile: Generating Diverse Emotional Facial Expressions from Text (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing systems that generate only coarse facial expressions ignore the rich and dynamic nature of face-to-face communication. |
| Approach: | They propose an end-to-end text-to expression model that explicitly focuses on emotional dynamics. |
| Outcome: | The proposed model outperforms baselines on 15,000 text–3D expression pairs on a large-scale dataset. |
Chain of Thought with Explicit Evidence Reasoning for Few-shot Relation Extraction (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to few-shot relation extraction require training. |
| Approach: | They propose a method for few-shot relation extraction using large language models, called CoT-ER, chain-of-thought with explicit evidence reasoning. |
| Outcome: | The proposed approach achieves competitive performance compared to the fully-supervised state-of-the-art approach on the FewRel1.0 and FewRela2.0 datasets. |
Reflection on Knowledge Graph for Large Language Models Reasoning (2025.findings-acl)
Copied to clipboard
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. |
Joint Multi-modal Aspect-Sentiment Analysis with Auxiliary Cross-modal Relation Detection (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on aspect-level sentiment analysis focus on extracting aspect terms and sentiment polarities separately. |
| Approach: | They propose a multi-modal joint learning approach with auxiliary cross-modal relation detection for multi-dimensional aspect-level sentiment analysis. |
| Outcome: | The proposed approach can obtain all aspect-level sentiment polarities dependent on the jointly extracted specific aspects. |
CommonIT: Commonality-Aware Instruction Tuning for Large Language Models via Data Partitions (2024.emnlp-main)
Copied to clipboard
| Challenge: | Current studies have focused on fine-tuning, but the use of instruction tuning is not as effective as fine-cuning. |
| Approach: | They propose a commonality-aware instruction tuning strategy to cluster instruction datasets into distinct groups with three proposed metrics Task, Embedding and Length. |
| Outcome: | The proposed strategy boosts an average improvement of 2.1% on the general domain and 5.2% on the special domain. |
Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods for text classification based on large language models are difficult to apply directly to solve. |
| Approach: | They propose a data quality enhancement method to improve LLMs' performance in classification tasks by using a greedy algorithm to select data and then performing fine-tuning. |
| Outcome: | The proposed method improves the performance of large language models in text classification tasks and significantly improves training efficiency, saving nearly half of the training time. |
Prediction Difference Regularization against Perturbation for Neural Machine Translation (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods for regularizing input perturbation are limited by under-fitting of training data. |
| Approach: | They propose a method that can reduce over-fitting and under-fitting at the same time. |
| Outcome: | The proposed method can reduce over-fitting and under-fitturing while making the model less sensitive to small input changes and more robust to under-perturbed training data. |
Rethinking Document-Level Relation Extraction: A Reality Check (2023.findings-acl)
Copied to clipboard
| 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 . |
Retrieval and Reasoning on KGs: Integrate Knowledge Graphs into Large Language Models for Complex Question Answering (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have performed impressively in various NLP tasks, but their inherent hallucination phenomena severely challenge their credibility in complex reasoning. |
| Approach: | They propose to integrate explainable Knowledge Graphs (KGs) with LLMs to alleviate hallucinations . they construct subgraphs to enhance the retrieval capabilities of KGs via CoT reasoning. |
| Outcome: | Extensive experiments on two KGQA datasets show that the proposed model achieves convincing performance compared to strong baselines. |
Improving Temporal Generalization of Pre-trained Language Models with Lexical Semantic Change (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to improve neural language models perform poorly on emerging data. |
| Approach: | They propose a lexical-level masking strategy to post-train a neural language model using static data from past years. |
| Outcome: | The proposed method outperforms existing methods on two pre-trained language models, two classification tasks, and four benchmark datasets. |
ORPP: Self-Optimizing Role-playing Prompts to Enhance Language Model Capabilities (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing research has explored model-driven strategies for prompt optimization, but these methods suffer from high computational overhead or require strong optimization capabilities from the model itself, which limits their broad applicability. |
| Approach: | They propose a framework that optimizes and generates role-playing prompts by limiting the prompt search space to role-player scenarios. |
| Outcome: | The proposed framework matches and surpasses existing prompt optimization methods in terms of performance. |
Efficient Continue Training of Temporal Language Model with Structural Information (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing temporal language models are limited by the superficial temporal information brought by timestamps, which fails to learn the inherent changes of linguistic components. |
| Approach: | They propose a method that captures syntactically changed tokens and captures the relationship between the time prefix and tokens. |
| Outcome: | The proposed method outperforms existing temporal language models on two datasets and three tasks. |
Self-Powered LLM Modality Expansion for Large Speech-Text Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models exhibit remarkable performance across diverse tasks . however, these methods require significant resource demands and tend to overfit specific tasks. |
| Approach: | They propose a self-powered LSM that leverages augmented automatic speech recognition data generated by the model itself for more effective instruction tuning. |
| Outcome: | The proposed model mitigates speech anchor bias and improves the fusion of speech and text modalities in large language models. |
Towards Robust Neural Machine Translation with Iterative Scheduled Data-Switch Training (2022.coling-1)
Copied to clipboard
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. |
Make Imagination Clearer! Stable Diffusion-based Visual Imagination for Multimodal Machine Translation (2025.acl-long)
Copied to clipboard
Andong Chen, Yuchen Song, Kehai Chen, Xuefeng Bai, Muyun Yang, Liqiang Nie, Jie Liu, Tiejun Zhao, Min Zhang
| Challenge: | Experimental results show that our model significantly outperforms existing multimodal MT and text-only MT. |
| Approach: | They propose a stable diffusion-based imagination network into a multimodal large language model to generate an image for each source sentence. |
| Outcome: | The proposed model outperforms existing multimodal and text-only MT and achieves an average improvement of 14 BLEU points on Multi30K and MSCOCO multimodal MT benchmarks. |
mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval (2024.emnlp-industry)
Copied to clipboard
Xin Zhang, Yanzhao Zhang, Dingkun Long, Wen Xie, Ziqi Dai, Jialong Tang, Huan Lin, Baosong Yang, Pengjun Xie, Fei Huang, Meishan Zhang, Wenjie Li, Min Zhang
| Challenge: | Existing models for text retrieval are based on a multi-stage process that involves retrieving documents from a large corpus. |
| Approach: | They propose to build a multilingual text representation model and a cross-encoder reranker from scratch for text retrieval. |
| Outcome: | The proposed models outperform the state-of-the-art models on long-context retrieval benchmarks. |
Revealing and Mitigating the Local Pattern Shortcuts of Mamba (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent studies show that Mamba excels in tasks that involve localized key information but faces challenges with tasks that require handling distributed key information. |
| Approach: | They propose to introduce a global gate module into Mamba to address this problem by adding 4M extra parameters to the model. |
| Outcome: | The proposed model outperforms attention-based models on synthetic and synthetic tasks with only 4M extra parameters. |
IS-CoT: Breaking the Long-form Generation Collapse via Interleaved Structural Thinking (2026.acl-long)
Copied to clipboard
Zechen Sun, Yuyang Sun, Zecheng Tang, Juntao Li, Wenpeng Hu, Wenliang Chen, Zhunchen Luo, Guotong Geng, Min Zhang
| Challenge: | Existing models with reasoning capabilities suffer from a severe length collapse in open-ended writing . |
| Approach: | They propose a framework that embeds a dynamic plan-write-reflect cycle into the generation process and train a model with interleaved reasoning traces. |
| Outcome: | The proposed framework achieves state-of-the-art performance on long-form benchmarks compared to other models on the same dataset. |
Forging Multiple Training Objectives for Pre-trained Language Models via Meta-Learning (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Empirical studies show that learning multiple training objectives in a single model makes the learned language representation barely converge to the desired optimum. |
| Approach: | They propose a meta-learning-based adaptive sampler which learns latent sampling pattern on arbitrary pre-training objectives. |
| Outcome: | Empirical studies show that learning multiple objectives in a single model makes it difficult to achieve the desired optimum. |
ExplainCPE: A Free-text Explanation Benchmark of Chinese Pharmacist Examination (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing explanation datasets for large language models are limited to the English language and general domain, leading to a scarcity of linguistic diversity and a lack of resources in specialized domains, such as medical. |
| Approach: | They propose to use a medical dataset to assess the interpretability of Large Language Models (LLMs) . they propose to analyze medical text and generate rationales for their decisions . |
| Outcome: | The proposed model passes the pharmacist examination with a 75.7% accuracy, while other models like ChatGPT fail. |
A Multimodal In-Context Tuning Approach for E-Commerce Product Description Generation (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for generating product descriptions from images are inaccurate and generic . e-commerce product descriptions are important for content marketing and increasing engagement . |
| Approach: | They propose a new setting for generating product descriptions from images, augmented by marketing keywords. |
| Outcome: | The proposed approach improves the accuracy and diversity of product descriptions by up to 3.3% on Rouge-L and 9.4% on D-5. |
Modeling Graph Structure in Transformer for Better AMR-to-Text Generation (D19-1)
Copied to clipboard
| Challenge: | Recent studies on AMR-to-text generation formalize the task as a sequence-tosequence learning problem . previous approaches only consider the relations between directly connected concepts while ignoring the rich structure in AMR graphs. |
| Approach: | They propose a structure-aware self-attention approach to model the relations between indirectly connected concepts in the seq2seq model. |
| Outcome: | The proposed approach outperforms the state-of-the-art on English AMR benchmarks . it significantly outperformed the state of the art on the benchmarks, with 29.66 and 31.82 BLEU scores . |
Dynamic and Efficient Inference for Text Generation via BERT Family (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to improve performance of pre-trained language models are limited due to large-scale parameters and the universal autoregressive decoding paradigm. |
| Approach: | They propose a novel fine-tuning method which can make a single pre-trained model support Dynamic and Efficient infERence and achieve an adaptive trade-off between model performance and latency. |
| Outcome: | The proposed method achieves higher BLEU scores than the strong autoregressive Transformer model on translation tasks with 3 12 times speedup and faster inference speed compared with the BART model on four GLGE benchmark tasks. |
Dynamic Sampling that Adapts: Self-Aware Iterative Data Persistent Optimization for Mathematical Reasoning (2026.findings-acl)
Copied to clipboard
| Challenge: | Current data selection paradigms rely on static, externally defined metrics, which fail to adapt to the evolving capabilities of models during training. |
| Approach: | They propose a dynamic sampling framework that aligns training data with the model's intrinsic competence by iterating on real-time feedback. |
| Outcome: | Extensive experiments on eight benchmarks show that SAI-DPO outperforms static baselines at most nearly 6 points, achieving state-of-the-art efficiency with significantly less data. |
Data Interpreter: An LLM Agent for Data Science (2025.findings-acl)
Copied to clipboard
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. |
Distantly Supervised NER with Partial Annotation Learning and Reinforcement Learning (C18-1)
Copied to clipboard
| Challenge: | Existing approaches to named entity recognition (NER) in Chinese are limited by the lack of annotated data. |
| Approach: | They propose a method which can automatically populate annotated training data without humancost by using distant supervision. |
| Outcome: | The proposed method performs better than comparison systems on two datasets. |
MMA: Cross-Domain Knowledge Integration via Mixture of Multi-Domain Agents (2025.findings-emnlp)
Copied to clipboard
| Challenge: | achieving synergistic improvements between generalization and domain specialization remains a challenge in pre-training and post-training. |
| Approach: | They propose a test-time cross-domain knowledge integration method that integrates general-purpose and domain-specific models to enhance their performance on complex, domainspecific tasks. |
| Outcome: | The proposed method combines the outputs of general-purpose and domain-specific models to improve their performance on complex, domainspecific tasks. |
An Evaluation Resource for Grounding Translation Errors (2025.findings-emnlp)
Copied to clipboard
| 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. |
Extending Phrase Grounding with Pronouns in Visual Dialogues (2022.emnlp-main)
Copied to clipboard
| Challenge: | Conventional phrase grounding aims to localize noun phrases mentioned in a caption to their corresponding image regions. |
| Approach: | They extend the task by considering pronouns to include noun phrases and pronounos . they construct a dataset of phrase grounding with noun and pronom phrases to image regions . |
| Outcome: | Experiments show that pronouns are easier to ground than noun phrases . a baseline model with coreference information can significantly boost the grounding performance . |
AgentInit: Initializing LLM-based Multi-Agent Systems via Diversity and Expertise Orchestration for Effective and Efficient Collaboration (2025.findings-emnlp)
Copied to clipboard
| 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. |
Benchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and Reasoning (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods for enhancing understanding and reasoning abilities in graphbased tasks focus on specific graph types or tasks, posing challenges in designing versatile systems suitable for various tasks and graphs across diverse domains. |
| Approach: | They propose a structure-aware fine-tuning framework to enhance LVLMs with structure learning abilities through three self-supervised learning tasks. |
| Outcome: | Extensive evaluations on 14 LVLMs reveal that LVLs are weak in basic graph understanding and reasoning tasks, particularly those concerning relational or structurally complex information. |
DB-LLM: Accurate Dual-Binarization for Efficient LLMs (2024.findings-acl)
Copied to clipboard
Hong Chen, Chengtao Lv, Liang Ding, Haotong Qin, Xiabin Zhou, Yifu Ding, Xuebo Liu, Min Zhang, Jinyang Guo, Xianglong Liu, Dacheng Tao
| Challenge: | Existing methods for ultra-low bit quantization cause severe accuracy drops . a novel Dual-Binarization method is proposed for efficient Large Language Models . |
| Approach: | They propose a Dual-Binarization method that takes 2-bit-width and binarization into account . they propose DB-LLM, which uses a 2-bit binarized weighted model to represent weights efficiently . |
| Outcome: | The proposed method surpasses the current State-of-the-Art in ultra-low bit quantization and achieves 20% reduction in computational consumption compared to the SOTA method under the same bit-width. |
Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences (2020.acl-main)
Copied to clipboard
| Challenge: | a monolingual speaker can learn to translate by looking up a bilingual dictionary . a novel task of machine translation (MT) is based on no parallel sentences but can refer to a ground-truth bilingual dictionary and large-scale monolingual corpora. |
| Approach: | They propose a task of machine translation that uses a bilingual dictionary and large-scale monolingual corpora to translate a monolingual speaker. |
| Outcome: | The proposed task is based on a bilingual dictionary and large scale monolingual corpora, while being independent on parallel sentences. |
CB-Whisper: Contextual Biasing Whisper Using Open-Vocabulary Keyword-Spotting (2024.lrec-main)
Copied to clipboard
Yuang Li, Yinglu Li, Min Zhang, Chang Su, Jiawei Yu, Mengyao Piao, Xiaosong Qiao, Miaomiao Ma, Yanqing Zhao, Hao Yang
| Challenge: | End-to-end automatic speech recognition systems struggle to recognize rare name entities such as personal names, organizations and terminologies that are not frequently encountered in the training data. |
| Approach: | They propose a convolutional neural network-based ASR system that performs open-vocabulary keyword-spotting before the decoder to match the features between the entities and the utterances. |
| Outcome: | The proposed system significantly improves mixed-error-rate (MER) and entity recall compared to the original Whisper model on three internal datasets and two publicly available datasets. |
Tailoring Diagnostic Modeling to Individual Learners: Personalized Distractor Generation via MCTS-Guided Reasoning Reconstruction (2026.acl-long)
Copied to clipboard
| 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. |
Visual Spatial Description: Controlled Spatial-Oriented Image-to-Text Generation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Image-to-text tasks such as captioning and controllable image descriptions have received extensive attention for decades. |
| Approach: | They propose a new perspective for image-to-text to generate spatial descriptions by combining two objects in an image. |
| Outcome: | The proposed model is awe-inspiring and human-like, and the proposed end-to-end architecture is the better choice for their integration. |
GTA: Supervised-Guided Reinforcement Learning for Text Classification with Large Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Reinforcement learning fine-tuning methods suffer from inefficient exploration and slow convergence . supervised fine- tuning methods have limited performance ceiling and less solid theoretical foundation . |
| Approach: | They propose a Guess-Think-Answer framework that combines supervised and supervised learning in a unified training paradigm. |
| Outcome: | The proposed framework outperforms both standalone SFT and RL training models on three text classification benchmarks. |
Sentence-Level Agreement for Neural Machine Translation (P19-1)
Copied to clipboard
| 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. |
Enhancing Speech Large Language Models with Prompt-Aware Mixture of Audio Encoders (2025.emnlp-main)
Copied to clipboard
Weiqiao Shan, Yuang Li, Yuhao Zhang, Yingfeng Luo, Chen Xu, Xiaofeng Zhao, Long Meng, Yunfei Lu, Min Zhang, Hao Yang, Tong Xiao, JingBo Zhu
| Challenge: | Existing work on integrating audio encoders with large language models (LLMs) has focused on semantic understanding tasks, but different tasks may require distinct features that emphasize either semantic or acoustic aspects. |
| Approach: | They propose to use a prompt-aware mixture to enhance the Speech LLM that uses multiple audio encoders to extract different features based on the prompt. |
| Outcome: | The proposed approach outperforms all single-encoder Speech LLMs on ASR, speaker number verification, and AC tasks. |
Using active learning to expand training data for implicit discourse relation recognition (D18-1)
Copied to clipboard
| Challenge: | Existing methods to determine semantic relations between text spans are limited in the field of discourse-level relation recognition. |
| Approach: | They propose to expand the training data set using the corpus of explicitly-related arguments by arbitrarily dropping the overtly presented discourse connectives. |
| Outcome: | The proposed model expands the training data set using the corpus of explicitly-related arguments, by arbitrarily dropping the overtly presented discourse connectives. |
ComfyUI-R1: Exploring Reasoning Models for Workflow Generation (2026.findings-acl)
Copied to clipboard
| 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)
Copied to clipboard
| 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. |
Towards Better Document-level Relation Extraction via Iterative Inference (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods only consider feature information of entity pairs, but our model exploits both feature information and previous predictions of entity pair. |
| Approach: | They propose a document-level relation extraction model with iterative inference to extract relations between entities from raw texts. |
| Outcome: | The proposed model outperforms existing methods on three commonly-used datasets. |
NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts (2023.findings-acl)
Copied to clipboard
| Challenge: | Recent studies on Chinese grammatical error correction focus on learning essays. |
| Approach: | They propose a Chinese grammatical error correction dataset that annotates multiple references for 12,500 sentences from three native domains. |
| Outcome: | The proposed dataset can be used to facilitate research on Chinese grammatical error correction (CGEC) for native speaker texts from multiple domains. |
MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct (2025.findings-acl)
Copied to clipboard
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. |
INFACT: A Diagnostic Benchmark for Induced Faithfulness and Factuality Hallucinations in Video-LLMs (2026.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks only evaluate models in clean settings due to hallucinations . |
| Approach: | They propose a diagnostic benchmark that evaluates models in four modes for faithfulness and factuality. |
| Outcome: | The proposed benchmark evaluates models in four modes: Base (clean), Visual Degradation, Evidence Corruption, and Temporal Intervention for order-sensitive items. |
Fast and Accurate End-to-End Span-based Semantic Role Labeling as Word-based Graph Parsing (2022.coling-1)
Copied to clipboard
| Challenge: | Using end-to-end span-based SRL, we propose a word-based graph parsing task for word-level representation of spans . compared with word-driven SRL, span-Based SRL is more complex due to difficulties in determining argument boundaries. |
| Approach: | They propose to cast end-to-end span-based SRL as a word-based graph parsing task . they propose a constrained Viterbi procedure to ensure the legality of the output graph . |
| Outcome: | The proposed model can parse 669/252 sentences per second without and with pre-trained models. |
Beyond Chunking: Discourse-Aware Hierarchical Retrieval for Long Document Question Answering (2026.acl-long)
Copied to clipboard
| Challenge: | Existing long document question answering systems process texts as flat sequences or use heuristic chunking, which overlooks the discourse structures that guide human comprehension. |
| Approach: | They propose a discourse-aware hierarchical framework that leverages rhetorical structure theory for long document question answering. |
| Outcome: | The proposed framework exhibits strong robustness across diverse document types and linguistic settings. |
Revisiting Token Dropping Strategy in Efficient BERT Pretraining (2023.acl-long)
Copied to clipboard
| Challenge: | Token dropping is a recently-proposed strategy to speed up the pretraining of masked language models, such as BERT. |
| Approach: | They propose a semantic-consistent learning method to improve token dropping by skipping the computation of a subset of input tokens at several middle layers. |
| Outcome: | The proposed method achieves consistent and significant performance gains across all tasks and model sizes. |
A Pilot Study for Chinese SQL Semantic Parsing (D19-1)
Copied to clipboard
| Challenge: | Existing datasets to map natural language text into SQL are limited in their use in question-to-sql mapping. |
| Approach: | They propose to use a Chinese-based semantic parser to map natural language text into SQL. |
| Outcome: | The proposed dataset compares a character-based parser with a word embedding scheme for Chinese . the results show that the parsers are subject to segmentation errors and cross-lingual embedders are useful for text-to-SQL mapping. |
Zero-Shot Cross-Lingual Abstractive Sentence Summarization through Teaching Generation and Attention (P19-1)
Copied to clipboard
| Challenge: | Abstractive Sentence Summarization (ASSUM) is a monolingual task that focuses on grasping the core idea of the source sentence and presenting it as the summary. |
| Approach: | They propose to use monolingual ASSUM to train a cross-lingual ASL system . they propose to train the system on summary word generation and attention . |
| Outcome: | Experiments show that the proposed method improves on the monolingual ASSUM task. |
TruthReader: Towards Trustworthy Document Assistant Chatbot with Reliable Attribution (2024.emnlp-demo)
Copied to clipboard
| Challenge: | Document assistant chatbots are empowered with extensive capabilities by Large Language Models (LLMs) however, they suffer from hallucinations that are difficult to verify in the context of given documents. |
| Approach: | They propose a document assistant chatbot with reliable attribution that enables users to seek relevant information from given documents. |
| Outcome: | The proposed system generates answers with detailed inline citations, which can be attributed to the original document paragraphs, facilitating verification of factual consistency of the generated text. |
On the Hallucination in Simultaneous Machine Translation (2024.acl-short)
Copied to clipboard
| Challenge: | Currently, there are no studies which systematically analyze hallucination in SiMT. |
| Approach: | They conduct a comprehensive analysis of hallucination in simultaneous machine translation (SiMT) they find that halluciation is extremely severe, especially as latency increases . |
| Outcome: | The results show that it is possible to alleviate hallucination by decreasing the over usage of target-side information for SiMT. |
Improving Relation Extraction with Relational Paraphrase Sentences (2020.coling-main)
Copied to clipboard
| Challenge: | Existing annotated data is expensive and non-scalable, limiting performance of relation extraction models. |
| Approach: | They propose to enrich relation expressions by relational paraphrase sentences by annotating human-annotated data. |
| Outcome: | The proposed model improves performance even on a strong baseline. |
PersonaX: A Recommendation Agent-Oriented User Modeling Framework for Long Behavior Sequence (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for user profile modeling extract only partial segments from full historical behavior sequence, resulting in incomplete modeling and suboptimal profiling. |
| Approach: | They propose an agent-agnostic LLM-UM framework to augment downstream recommendation agents . it segments complete historical behaviors into clustered groups and performs offline multi-persona profiling . |
| Outcome: | The proposed framework improves agent performance and inference efficiency by 31% and 10% using 30–50% of behavioral data. |
CTC-based Non-autoregressive Textless Speech-to-Speech Translation (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing direct speech-to-speech translation models require text supervision during training, which is not feasible for numerous unwritten languages. |
| Approach: | They propose a non-autoregressive (NAR) model that generates discrete units from the source speech and employs a unit-based vocoder to synthesize the target. |
| Outcome: | The proposed model achieves translation quality comparable to the autoregressive model while preserving up to 26.81 decoding speedup. |
Exploring Reversal Mathematical Reasoning Ability for Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have been a success in the wide range of natural language understanding and reasoning tasks. |
| Approach: | They propose a training method to improve general and reversal reasoning abilities by using a reversed dataset. |
| Outcome: | The proposed method improves general and reversal reasoning abilities and alleviates the reverse curse. |
Enhancing Zero-Shot Time Series Forecasting in Off-the-Shelf LLMs via Noise Injection Prompting (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing work relies on fine-tuning specialized modules to bridge this gap, but a novel approach is proposed to leverage off-the-shelf LLMs without any fine- tuning whatsoever. |
| Approach: | They propose a method to inject noise into the raw time series before tokenization to induce the model to extrapolate based on robust underlying temporal patterns rather than superficial numerical artifacts. |
| Outcome: | The proposed approach overcomes the brittleness of fully frozen models by injecting noise into the raw TS before tokenization. |
Can Diffusion Model Achieve Better Performance in Text Generation ? Bridging the Gap between Training and Inference ! (2023.findings-acl)
Copied to clipboard
| 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)
Copied to clipboard
| 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)
Copied to clipboard
| 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. |
DimA: A Parameter-efficient Fine-tuning Method with Knowledge Transfer Based on Transformer (2024.lrec-main)
Copied to clipboard
| Challenge: | Pre-trained language models (PLMs) have demonstrated impressive performance across various downstream tasks, but fine-tuning is computationally expensive and storage-intensive. |
| Approach: | They propose a parameter-efficient method called DimA which enhances the transformer architecture by increasing the dimensionality. |
| Outcome: | The proposed method achieves state-of-the-art results in GLUE and XSUM tasks while utilizing less than 1% of the original model’s parameters. |
Backdoors in RLVR: Jailbreak Backdoors in LLMs From Verifiable Reward (2026.acl-long)
Copied to clipboard
| Challenge: | Reinforcement Learning with Verifiable Rewards (RLVR) is an emerging paradigm that significantly boosts a Large Language Model’s reasoning abilities on complex logical tasks. |
| Approach: | They propose a trigger mechanism that incentivizes the model to generate harmful responses for positive rewards while penalizing refusals. |
| Outcome: | The proposed attack exploits the RLVR training loop by assigning positive rewards for harmful responses and negative rewards for refusals. |
Semi-supervised Domain Adaptation for Dependency Parsing via Improved Contextualized Word Representations (2020.coling-main)
Copied to clipboard
| Challenge: | Recent advances in deep neural network models have improved parsing performance on in-domain texts . however, the problem is to improve performance on out-of-domain text data when there is only a small-scale out-domain labeled data. |
| Approach: | They propose to use adversarial learning and fine-tuning BERT to improve contextualized word representations on out-of-domain texts. |
| Outcome: | The proposed models achieve consistent improvement and fine-tune BERT processes boost parsing accuracy by a large margin. |
Market-Bench: Benchmarking Large Language Models on Economic and Trade Competition (2026.acl-long)
Copied to clipboard
| Challenge: | Existing LLM benchmarks focus on semantic complexity or quantitative competition, but rarely both simultaneously under economic scarcity. |
| Approach: | They propose a benchmark that evaluates the capabilities of large language models (LLMs) in economically-relevant tasks through economic and trade competition. |
| Outcome: | The proposed model evaluates the capabilities of large language models in economically-relevant tasks through economic and trade competition. |
DISC: Plug-and-Play Decoding Intervention with Similarity of Characters for Chinese Spelling Check (2025.acl-long)
Copied to clipboard
Ziheng Qiao, Houquan Zhou, Yumeng Liu, Zhenghua Li, Min Zhang, Bo Zhang, Chen Li, Ji Zhang, Fei Huang
| Challenge: | Chinese spelling check (CSC) tasks require that incorrect characters are usually similar to the correct ones in either phonetics or glyph. |
| Approach: | They propose a plug-and-play decoding intervention with similarity of characters module for Chinese spelling check (CSC) they propose to incorporate phonetic and glyph similarities only during the inference phase. |
| Outcome: | The proposed method significantly improves Chinese spelling check models on benchmarks and on benchmark datasets. |
Improving Attributed Text Generation of Large Language Models via Preference Learning (2024.findings-acl)
Copied to clipboard
| Challenge: | Large language models have been widely adopted in natural language processing, yet they produce unreliable content. |
| Approach: | They propose to model the attribution task as preference learning and introduce an automatic preference optimization framework that synthesizes attribution preference data. |
| Outcome: | The proposed method achieves state-of-the-art citation F1 with higher answer quality than existing methods. |
MCS: An In-battle Commentary System for MOBA Games (2022.coling-1)
Copied to clipboard
Xiaofeng Qi, Chao Li, Zhongping Liang, Jigang Liu, Cheng Zhang, Yuanxin Wei, Lin Yuan, Guang Yang, Lanxiao Huang, Min Li
| Challenge: | In-battle commentary is an important component of live streaming of e-sports competitions and is applicable to a wide range of scenarios like combat information analysis and live streaming. |
| Approach: | They propose a generative system for in-battle real-time commentary in mobile MOBA games and propose 'transform' method to convert match statistics and utterances into consistent encoding space. |
| Outcome: | The proposed system is based on real-time match statistics and events and can be used for live streaming, e-sports commentary and combat information analysis. |
Token Drop mechanism for Neural Machine Translation (2020.coling-main)
Copied to clipboard
| Challenge: | Neural machine translation models are vulnerable to unfamiliar inputs. |
| Approach: | They propose to drop tokens of the input sentences to improve generalization and avoid overfitting for the NMT model. |
| Outcome: | The proposed approach improves on Chinese-English and English-Romanian benchmarks and achieves significant performance improvements over baselines. |
SmartSpanNER: Making SpanNER Robust in Low Resource Scenarios (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) is one of the most fundamental tasks in natural language processing. |
| Approach: | They propose a method which introduces a Named Entity Head (NEH) prediction task to SpanNER and performs multi-task learning together with task of span classification. |
| Outcome: | The proposed method improves the robustness of SpanNER in low resource scenarios on the CoNLL03, Few-NERD, GENIA and ACE05 benchmark datasets. |
Curriculum Consistency Learning for Conditional Sentence Generation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Consistency learning (CL) has proven to be a valuable technique for improving the robustness of conditional sentence generation models. |
| Approach: | They propose a strategy that guides models to learn consistency in alignment with their current capacity to differentiate between features. |
| Outcome: | The proposed strategy delivers +2.0 accuracy point improvement compared with vanilla IT and +0.7 COMET scores over traditional CL methods in MT tasks. |
Semantic Role Labeling from Chinese Speech via End-to-End Learning (2024.findings-acl)
Copied to clipboard
| Challenge: | Semantic role labeling (SRL) has traditionally focused on text input. |
| Approach: | They propose an end-to-end approach for SRL from speech integrating ASR and SRL in a joint-learning framework, focusing on the Chinese language. |
| Outcome: | The proposed model improves on the Chinese Proposition Bank 1.0 dataset and the existing model with improved performance. |
Generative Multimodal Entity Linking (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing Entity Linking methods focus on designing complex multimodal interaction mechanisms and require fine-tuning all model parameters. |
| Approach: | They propose a framework for multimodal entity linking based on Large Language Models (LLMs) that trains a feature mapper to enable cross-modal interactions. |
| Outcome: | The proposed framework achieves state-of-the-art on two well-established datasets with a performance gain of 7.7% on WikiDiverse and 8.8% on Wikileaks. |
MEVTR: A Multilingual Model Enhanced with Visual Text Representations (2024.lrec-main)
Copied to clipboard
| 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)
Copied to clipboard
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. |
Unleashing LLM Reasoning Capability via Scalable Question Synthesis from Scratch (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods to improve the mathematical reasoning capabilities of Large Language Models (LLMs) are limited due to the proprietary nature of the data. |
| Approach: | They propose a data synthesis method that generates large-scale mathematical reasoning datasets using lightweight 7B-scale models. |
| Outcome: | The proposed method outperforms existing open-source datasets in both in-domain and out-of-domain evaluations and shows improvements in code reasoning tasks. |
MSVBench: Towards Human-Level Evaluation of Multi-Shot Video Generation (2026.findings-acl)
Copied to clipboard
| 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. |
Crossing the Reward Bridge: Expanding Reinforcement Learning with Verifiable Rewards Across Diverse Domains (2026.acl-long)
Copied to clipboard
| Challenge: | Reinforcement learning with verifiable rewards (RLVR) has been effective on structured tasks, but its reliance on simple, rule-based verifiers creates a bottleneck. |
| Approach: | They propose a framework that uses a generative verifier to provide soft, probabilistic rewards. |
| Outcome: | The proposed framework outperforms existing models up to 10x their size and can be scalable and effective. |
MTLS: Making Texts into Linguistic Symbols (2024.emnlp-main)
Copied to clipboard
| 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. |
GUI-explorer: Autonomous Exploration and Mining of Transition-aware Knowledge for GUI Agent (2025.acl-long)
Copied to clipboard
| Challenge: | GUI automation is a key challenge in dynamic environments. |
| Approach: | They propose a training-free GUI agent that integrates two mechanisms to explore trajectories in GUIs. |
| Outcome: | The proposed GUI-explorer shows significant improvements over existing agents. |
Breaking the Corpus Bottleneck for Context-Aware Neural Machine Translation with Cross-Task Pre-training (2021.acl-long)
Copied to clipboard
| Challenge: | Context-aware neural machine translation (NMT) remains challenging due to the lack of large-scale document-level parallel corpora. |
| Approach: | They propose to use large-scale parallel datasets and source-side monolingual documents to improve context-aware neural machine translation. |
| Outcome: | The proposed model can be used to translate both sentences and documents on four translation tasks. |
Efficient Domain Adaptation for Non-Autoregressive Machine Translation (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing non-parametric approaches like nearest neighbor machine translation have made small Autoregressive translation models less efficient . despite their impressive generalization and task performance, LLMs suffer from prohibitive inference cost when confronted with specific domains. |
| Approach: | They propose a domain adaptation approach that tailors a k-nearest-neighbor algorithm for NAT models that incorporates the parallel nature of NAT. |
| Outcome: | The proposed approach achieves significant improvements over the Base-NAT model and exhibits enhanced efficiency. |
Take Off the Training Wheels! Progressive In-Context Learning for Effective Alignment (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies have explored the working mechanisms of In-Context Learning (ICL) however, they mainly focus on classification and simple generation tasks, limiting their broader application to more complex generation tasks in practice. |
| Approach: | They propose an efficient Progressive In-Context Alignment method that embeds the task function learned from demonstrations into the separator token representation. |
| Outcome: | The proposed method surpasses vanilla ICL and achieves comparable performance to other alignment tuning methods. |
ReasoningGuard: Safeguarding Large Reasoning Models with Inference-time Safety Aha Moments (2026.acl-long)
Copied to clipboard
| 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)
Copied to clipboard
| 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. |
Scene Graph as Pivoting: Inference-time Image-free Unsupervised Multimodal Machine Translation with Visual Scene Hallucination (2023.acl-long)
Copied to clipboard
| Challenge: | Current neural machine translation (NMT) relies on parallel sentences, which obstructs the development of NMT for minor languages. |
| Approach: | They propose an unsupervised multimodal machine translation setup where the model is trained with source-text image pairs and tested with only source- text inputs. |
| Outcome: | The proposed model outperforms the baseline model on the task and setup, helping yield translations with better completeness, relevance and fluency without relying on paired images. |
Act-Adaptive Margin: Dynamically Calibrating Reward Models for Subjective Ambiguity (2026.acl-long)
Copied to clipboard
Feiteng Fang, Dingwei Chen, Xiang Huang, Ting-En Lin, Yuchuan Wu, Xiong Liu, Jing Ye, Ziqiang Liu, Haonan Zhang, Liang Zhu, Hamid Alinejad-Rokny, Min Yang, Yongbin Li
| Challenge: | Existing approaches to reward modeling in reinforcement learning tasks are limited when dealing with ambiguous preferences. |
| Approach: | They propose to use AAM to dynamically calibrate preference margins using the Bradley-Terry model's internal parameter knowledge to improve reward modeling in subjective tasks. |
| Outcome: | The proposed approach improves reward modeling by dynamically calibrating preference margins using the model’s internal parameter knowledge. |
Accurate KV Cache Quantization with Outlier Tokens Tracing (2025.acl-long)
Copied to clipboard
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. |
SrDetection: A Self-Referential Framework for Data Leakage Detection in Code Large Language Models (2026.findings-acl)
Copied to clipboard
Shuaimin Li, Liyang Fan, Zeyang li, Zhuoyue Wan, Yufang Lin, Shiwen Ni, Feiteng Fang, Hamid Alinejad-Rokny, Yuanfeng Song, Kun Jing, Chen Jason Zhang, Min Yang
| Challenge: | Existing methods for evaluating code large language models assume access to proprietary training corpora or use external reference sets with manually tuned, non-generalizable thresholds. |
| Approach: | They propose a framework for self-referential leakage detection for gray-box and black-box settings. |
| Outcome: | The proposed framework improves average F1 by 21.52 points in the gray-box setting and 14.46 points in black-box settings over strong baselines. |
XNLP: An Interactive Demonstration System for Universal Structured NLP (2024.acl-demos)
Copied to clipboard
| Challenge: | Structured Natural Language Processing (XNLP) is an important subset of NLP that entails understanding the underlying semantic or syntactic structure of texts. |
| Approach: | They propose a XNLP demonstration system that leverages LLM to achieve universal XnLP with one model for all with high generalizability. |
| Outcome: | The proposed system advances in multiple aspects, including universal XNLP modeling, high performance, interpretability, scalability, and interactivity. |
The Rise of Darkness: Safety-Utility Trade-Offs in Role-Playing Dialogue Agents (2025.findings-acl)
Copied to clipboard
| 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. |
A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised Learning (2020.emnlp-main)
Copied to clipboard
| Challenge: | Structured belief states are crucial for goal tracking and database query in task-oriented dialog systems. |
| Approach: | They propose a probabilistic dialog model where belief states are represented as discrete latent variables and jointly modeled with system responses given user inputs. |
| Outcome: | The proposed model outperforms supervised-only and semi-supervised baselines on three benchmark datasets. |
Tiny Budgets, Big Gains: Parameter Placement Strategy in Parameter Super-Efficient Fine-Tuning (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods such as LoRA and VeRA use memory-efficient methods to fine-tune large language models. |
| Approach: | They propose a method that uses only 1–5% of the standard LoRA parameters and achieves state-of-the-art performance across a wide range of tasks. |
| Outcome: | The proposed method achieves state-of-the-art performance across a wide range of tasks using only 1–5% of the standard LoRA parameters. |
Argument Pair Extraction with Mutual Guidance and Inter-sentence Relation Graph (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on argumentation mining focus on monological argumentation and dialogical argumentation. |
| Approach: | They propose a mutual guidance framework that could guide arguments in one passage . they propose an inter-sentence relation graph to effectively model the inter-relations between two sentences . |
| Outcome: | The proposed method outperforms the current state-of-the-art model. |
How Far Can LLMs Improve from Experience? Measuring Test-Time Learning Ability in LLMs with Human Comparison (2025.emnlp-main)
Copied to clipboard
| 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)
Copied to clipboard
| 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)
Copied to clipboard
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. |
When Is Thinking Enough? Early Exit via Sufficiency Assessment for Efficient Reasoning (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to early exit reasoning often rely on handcrafted or empirical indicators that are unreliable and impractical. |
| Approach: | They propose a framework that allows LRMs to assess the sufficiency of its chain-of-thought and determine the optimal point for early exit. |
| Outcome: | The proposed framework reduces reasoning length by 28.9%–34.9% with minimal performance loss, effectively mitigating overthinking. |
ARM: An Alignment-and-Replacement Module for Chinese Spelling Check Based on LLMs (2024.emnlp-main)
Copied to clipboard
| Challenge: | Chinese Spelling Check (CSC) aims to identify and correct spelling errors in Chinese texts, where enhanced semantic understanding of a sentence can significantly improve correction accuracy. |
| Approach: | They propose a plug-and-play Alignment-and -Replacement module that enhances existing Chinese CSC models without retraining or fine-tuning. |
| Outcome: | The proposed module improves existing models while reducing retraining and fine-tuning. |
CDT: A Comprehensive Capability Framework for Large Language Models Across Cognition, Domain, and Task (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing benchmarks focus on isolated abilities, lacking a holistic framework for assessing LLM capabilities. |
| Approach: | They propose a Cognition-Domain-Task framework which measures a model’s capabilities across three dimensions. |
| Outcome: | The proposed framework improves performance on dataset evaluation and data selection, while achieving higher scores on general and specific benchmarks. |
Semantic Role Labeling with Heterogeneous Syntactic Knowledge (2020.coling-main)
Copied to clipboard
| 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. |
BrainECHO: Semantic Brain Signal Decoding through Vector-Quantized Spectrogram Reconstruction for Whisper-Enhanced Text Generation (2025.findings-acl)
Copied to clipboard
| 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. |
CPsyExam: A Chinese Benchmark for Evaluating Psychology using Examinations (2025.coling-main)
Copied to clipboard
Jiahao Zhao, Jingwei Zhu, Minghuan Tan, Min Yang, Renhao Li, Yang Di, Chenhao Zhang, Guancheng Ye, Chengming Li, Xiping Hu, Derek F. Wong
| Challenge: | CPsyExam prioritizes psychological knowledge and case analysis separately, recognizing the significance of applying psychological knowledge to real-world scenarios. |
| Approach: | They propose a psychological benchmark, CPsyExam, constructed from questions from Chinese examination systems. |
| Outcome: | The proposed benchmark prioritizes psychological knowledge and case analysis separately, recognizing the significance of applying psychological knowledge to real-world scenarios. |
A Two-Stage Adaptation of Large Language Models for Text Ranking (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in pre-trained language models (PLMs) have significantly improved ranking performance in text ranking tasks. |
| Approach: | They propose a two-stage progressive paradigm to better adapt LLMs to text ranking by conducting continual pre-training on a large weakly-supervised corpus and performing SFT on high-quality data. |
| Outcome: | The proposed approach outperforms previous methods on in- and out-domain scenarios. |
HiMATE: A Hierarchical Multi-Agent Framework for Machine Translation Evaluation (2025.findings-emnlp)
Copied to clipboard
| 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. |
Multimodal Cross-lingual Phrase Retrieval (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing approaches to cross-lingual phrase retrieval only deal with textual modality, leaving the question of the effectiveness of using multimodal information unanswered. |
| Approach: | They propose a multimodal cross-lingual phrase retrieval resource that integrates a Wikimedia Commons media store and a large multimodal pre-trained model to bridge the gap between different modalities. |
| Outcome: | The proposed approach performs significantly better than pure textual cross-lingual phrase retrieval on a benchmarked dataset covering eight language pairs. |
Constructing Code-mixed Universal Dependency Forest for Unbiased Cross-lingual Relation Extraction (2023.findings-acl)
Copied to clipboard
| Challenge: | Recent efforts on cross-lingual relation extraction (XRE) leverage language-consistent structural features from the universal dependency resource. |
| Approach: | They propose to construct a type of code-mixed UD forest that combines UD and source-/target-side UD structures to achieve unbiased transfer. |
| Outcome: | The proposed UD forest achieves significant performance gains on ACE XRE benchmark datasets. |
Open Domain Question Answering with Conflicting Contexts (2025.findings-naacl)
Copied to clipboard
Siyi Liu, Qiang Ning, Kishaloy Halder, Zheng Qi, Wei Xiao, Phu Mon Htut, Yi Zhang, Neha Anna John, Bonan Min, Yassine Benajiba, Dan Roth
| Challenge: | Open domain question answering systems often rely on information retrieved from large collections of text to answer questions. |
| Approach: | They evaluate and benchmark three powerful Large Language Models with a dataset . they find that 25% of unambiguous open domain questions can lead to conflicting contexts . |
| Outcome: | The proposed model can't be used to answer questions with conflicting contexts . it can be fine tuned to provide richer information into the model's training . |
Can LLMs Learn Uncertainty on Their Own? Expressing Uncertainty Effectively in A Self-Training Manner (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) exhibit excessive, random, and uninformative uncertainty rendering them unsuitable for decision-making in human-computer interactions. |
| Approach: | They propose an uncertainty-aware instruction tuning method that aligns LLMs’ perception with the probabilistic uncertainty of the generation. |
| Outcome: | The proposed method improves LLMs' performance by 45.2%, with reasonably good out-of-domain generalization capabilities. |
Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScore (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods for detecting LLM-generated text require no training data. |
| Approach: | They propose a black-box zero-shot detection approach that calculates the Grammar Error Correction Score for a given text to differentiate between human-written and LLM-generated texts. |
| Outcome: | The proposed method outperforms current state-of-the-art zero-shot and supervised methods, achieving an average AUROC of 98.62% across XSum and Writing Prompts datasets. |
SelfMix: Robust Learning against Textual Label Noise with Self-Mixup Training (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods to handle label noise in text classification tasks are limited to visual data. |
| Approach: | They propose a method to handle label noise in text classification tasks using a Gaussian Mixture Model. |
| Outcome: | The proposed method outperforms baselines on three types of text classification tasks on visual and textual data. |
Beyond Unimodal Shortcuts: MLLMs as Cross-Modal Reasoners for Grounded Named Entity Recognition (2026.findings-acl)
Copied to clipboard
| 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)
Copied to clipboard
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 . |
Cross-Domain Audio Deepfake Detection: Dataset and Analysis (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing audio deepfake detection datasets are outdated and lack generalization capabilities. |
| Approach: | They construct a new cross-domain audio deepfake detection dataset comprising over 300 hours of speech data that is generated by five advanced zero-shot TTS models. |
| Outcome: | The proposed models achieve 4.1% and 6.5% error rates in the cross-domain ADD dataset generated by five advanced zero-shot TTS models. |
Neural Parameter Search for Slimmer Fine-Tuned Models and Better Transfer (2025.acl-long)
Copied to clipboard
Guodong Du, Zitao Fang, Jing Li, Junlin Li, Runhua Jiang, Shuyang Yu, Yifei Guo, Yangneng Chen, Sim Kuan Goh, Ho-Kin Tang, Daojing He, Honghai Liu, Min Zhang
| Challenge: | Foundational models and their checkpoints have advanced deep learning, boosting performance across applications. |
| Approach: | They propose a method for pruning fine-tuned models by calculating differences between them and original model. |
| Outcome: | The proposed method can improve performance across vision, NLP, and multi-modal benchmarks. |
Demonstration Augmentation for Zero-shot In-context Learning (2024.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated an impressive capability known as In-context Learning (ICL), which enables them to acquire knowledge from textual demonstrations without the need for parameter updates. |
| Approach: | They propose to use model’s previously predicted historical samples as demonstrations for subsequent ones to improve model’ s performance. |
| Outcome: | The proposed method significantly outperforms the previous method and its predecessors in terms of inference cost and time. |
Capture Human Disagreement Distributions by Calibrated Networks for Natural Language Inference (2022.findings-acl)
Copied to clipboard
| 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)
Copied to clipboard
| 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. |
Third-Party Aligner for Neural Word Alignments (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing work shows that word alignment can be competitive . |
| Approach: | They propose to use word alignments generated by a third-party word aligner to supervise the neural word alignment training. |
| Outcome: | The proposed approach can find more accurate word alignments and delete wrong alignments, leading to better performance than the current best third-party word aligner. |
JOLT-SQL: Joint Loss Tuning of Text-to-SQL with Confusion-aware Noisy Schema Sampling (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in Large Language Models have improved Text-to-SQL methods . however, they still face challenges such as complex multi-stage pipelines and poor robustness to noisy schema information. |
| Approach: | They propose a single-stage SFT framework that optimizes schema linking and SQL generation via a unified loss. |
| Outcome: | Experiments on the Spider and BIRD benchmarks show that JOLT-SQL achieves state-of-the-art execution accuracy among comparable-size open-source models. |
Breaking the Representation Bottleneck of Chinese Characters: Neural Machine Translation with Stroke Sequence Modeling (2022.emnlp-main)
Copied to clipboard
| 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)
Copied to clipboard
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. |
Improving Low-resource Question Answering by Augmenting Question Information (2023.findings-emnlp)
Copied to clipboard
Andong Chen, Yuan Sun, Xiaobing Zhao, Rosella Galindo Esparza, Kehai Chen, Yang Xiang, Tiejun Zhao, Min Zhang
| Challenge: | Low-resource questions pose a significant challenge within the field of Question-Answering (QA) tasks. |
| Approach: | They propose a method that leverages large models' internal knowledge to enhance the quality of augmented data by Prompt Answer, Question Generation, and Question Filter. |
| Outcome: | The proposed method outperforms existing augmentation strategies on high-resource QA tasks like SQUAD1.1 and TriviaQA. |
LycheeCluster: Efficient Long-Context Inference with Structure-Aware Chunking and Hierarchical KV Indexing (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing retrieval-based methods compromise semantic integrity through fixed-size chunking and suffer from inefficient linear scanning. |
| Approach: | They propose a method that preserves local semantic coherence through boundary-aware chunking and constructs a recursive hierarchical index rooted in the triangle inequality. |
| Outcome: | The proposed method achieves 3.6 end-to-end inference speedup with negligible degradation in model performance. |
Adaptive Detoxification: Safeguarding General Capabilities of LLMs through Toxicity-Aware Knowledge Editing (2025.findings-acl)
Copied to clipboard
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. |
D-QRELO: Training- and Data-Free Delta Compression for Large Language Models via Quantization and Residual Low-Rank Approximation (2026.findings-acl)
Copied to clipboard
Junlin Li, Shuangyong Song, Guodong DU, Ngai Wong, Xuebo Liu, Yongxiang Li, Min Zhang, Jing Li, Xuelong Li
| Challenge: | Existing methods for fine-tuned large language models fail on fine-scale datasets . large data scale amplifies delta parameter magnitude, singular values, and entropy, causing compression errors. |
| Approach: | They propose a training- and data-free delta compression method that captures dominant delta structure and compensates residual low-rank approximation to recover fine-grained details from smaller residual error. |
| Outcome: | The proposed method outperforms existing methods on large-scale datasets on dense and MoE architectures. |
ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation (2022.acl-long)
Copied to clipboard
Bei Li, Quan Du, Tao Zhou, Yi Jing, Shuhan Zhou, Xin Zeng, Tong Xiao, JingBo Zhu, Xuebo Liu, Min Zhang
| Challenge: | Residual networks are an Euler discretization of solutions to Ordinary Differential Equations (ODE). |
| Approach: | They propose a residual block of layers in Transformer that can be described as a higher-order solution to ODE. |
| Outcome: | The proposed architecture can gain large improvements over strong baselines at a slight cost in inference efficiency. |
Domain-Aware k-Nearest-Neighbor Knowledge Distillation for Machine Translation (2024.findings-acl)
Copied to clipboard
| 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. |
Boundary-Driven Table-Filling for Aspect Sentiment Triplet Extraction (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing work focuses on extracting aspect terms and opinion terms without considering the relations between aspect terms . |
| Approach: | They propose a task to extract aspect terms, opinion terms, and expressed sentiments from a two-dimensional (2D) table. |
| Outcome: | The proposed method achieves state-of-the-art on several public benchmarks and is well-suited to the ASTE task. |
TransGEC: Improving Grammatical Error Correction with Translationese (2023.findings-acl)
Copied to clipboard
| Challenge: | Experimental results show that data augmentation improves accuracy over strong baselines. |
| Approach: | They propose to use translationese as input for GEC data augmentation to overcome stylistic discrepancies . they propose to obtain human-translated texts with a more similar style to non-native texts . |
| Outcome: | The proposed method improves correction accuracy over strong baselines on four GEC benchmarks. |
StreamSpeech: Simultaneous Speech-to-Speech Translation with Multi-task Learning (2024.acl-long)
Copied to clipboard
| Challenge: | Existing simultaneous translation methods focus on text-to-text and speech-totext translation. |
| Approach: | They propose a Simul-S2ST model that jointly learns translation and simultaneous policy in a unified framework of multi-task learning. |
| Outcome: | The proposed model can perform offline and simultaneous speech recognition, speech translation and speech synthesis via an "All-in-One" seamless model. |
CMD: a framework for Context-aware Model self-Detoxification (2024.emnlp-main)
Copied to clipboard
| 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. |
Beware of Model Collapse! Fast and Stable Test-time Adaptation for Robust Question Answering (2023.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained language models (PLMs) have achieved great success in question answering, but their robustness is insufficient to support their practical applications. |
| Approach: | They propose a method which regularizes the model's output and an efficient side block to reduce its inference time. |
| Outcome: | The proposed method achieves comparable or better results than previous TTA methods at a speed close to vanilla forward propagation, which is 1.8 to 4.4 speedup compared to previous methods. |
Paying More Attention to Source Context: Mitigating Unfaithful Translations from Large Language Model (2024.findings-acl)
Copied to clipboard
| Challenge: | Large language models lack explicit alignment between source and target contexts, leading to unfaithful translations. |
| Approach: | They propose three learning strategies to encourage LLMs to pay more attention to source context . they use a dataset to test the effectiveness of their model across multiple language pairs . |
| Outcome: | The proposed model reduces hallucinatory translation and improves fidelity across multiple languages. |
ComfyFlow: Benchmarking LLMs for AIGC Workflow Generation (2026.findings-acl)
Copied to clipboard
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)
Copied to clipboard
| 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)
Copied to clipboard
| 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. |
DRPruning: Efficient Large Language Model Pruning through Distributionally Robust Optimization (2025.acl-long)
Copied to clipboard
| Challenge: | Structured pruning reduces model size but often causes uneven degradation across domains, leading to biased performance. |
| Approach: | They propose a method that dynamically adjusts the data distribution during training to restore balanced performance across heterogeneous and multi-tasking data. |
| Outcome: | Experiments in monolingual and multilingual settings show that the proposed method surpasses similarly sized models in pruning and continued pretraining over perplexity, downstream tasks, and instruction tuning. |
Concise and Precise Context Compression for Tool-Using Language Models (2024.findings-acl)
Copied to clipboard
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. |
A Pilot Study on Dialogue-Level Dependency Parsing for Chinese (2023.findings-acl)
Copied to clipboard
| Challenge: | Dialogue-level dependency parsing has received insufficient attention, especially for Chinese. |
| Approach: | They propose a signal-based method to transform seen syntactic dependencies into unseen ones between elementary discourse units (EDUs) they apply single-view and multi-view data selection to access reliable pseudo-labeled instances. |
| Outcome: | The proposed method transforms seen syntactic dependencies into unseen ones between elementary discourse units (EDUs) the proposed method also provides reliable pseudo-labeled instances. |
PTQ1.61: Push the Real Limit of Extremely Low-Bit Post-Training Quantization Methods for Large Language Models (2025.acl-long)
Copied to clipboard
| 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. |
INFORM : Information eNtropy based multi-step reasoning FOR large language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) have demonstrated exceptional performance with dedicated Chain-of-Thought (CoT) prompts. |
| Approach: | They propose a new method by introducing information entropy as a criteria on for CoT prompt selection. |
| Outcome: | The proposed model outperforms existing models on seven reasoning benchmarks using two language models. |
Understanding New-Knowledge-Induced Factual Hallucinations in LLMs: Analysis and Interpretation (2026.findings-acl)
Copied to clipboard
| Challenge: | Prior studies have shown that fine-tuning on new knowledge can induce factual hallucinations in large language models (LLMs), leading to incorrect outputs when evaluated on previously known information. |
| Approach: | They propose to conduct a fine-grained analysis of large language models using a dataset Biography-Reasoning and QA and knowledge reasoning tasks to understand their findings. |
| Outcome: | The proposed model is able to perform a range of downstream tasks without requiring a large amount of knowledge and is compared with a control dataset. |
LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding (2021.acl-long)
Copied to clipboard
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. |
SlowBERT: Slow-down Attacks on Input-adaptive Multi-exit BERT (2023.findings-acl)
Copied to clipboard
| Challenge: | Pre-trained language models such as Google’s BERT have been gaining significant improvements to various down-stream applications, but the enormous training and inference cost severely hinders its practice on real-time applications and hardwareconstrained edge devices. |
| Approach: | They propose a slow-down attack on input-adaptive multi-exit BERT where the adversary imperceptibly modifies the input texts to drastically increase the inference cost. |
| Outcome: | The proposed attack on input-adaptive multi-exit BERT dramatically increases the average inference cost by 4.57, which would hurt the service quality of multi-extit BRT in practice, e.g., increasing the real-time cloud services’ response times for online users. |
DoCIA: An Online Document-Level Context Incorporation Agent for Speech Translation (2025.findings-acl)
Copied to clipboard
Xinglin Lyu, Wei Tang, Yuang Li, Xiaofeng Zhao, Ming Zhu, Junhui Li, Yunfei Lu, Min Zhang, Daimeng Wei, Hao Yang, Min Zhang
| Challenge: | Document-level context is crucial for speech translation due to noise from ASR . incorporating document-level contextual information into ST remains a challenge . |
| Approach: | They develop an online framework that integrates document-level context into machine translation . they use document-based modules to integrate document- level context into ST . |
| Outcome: | The proposed framework outperforms baselines in sentence and discourse metrics . it can correct ASR transcription errors and improve translation performance . |
Generative Reward Modeling via Synthetic Criteria Preference Learning (2025.acl-long)
Copied to clipboard
| Challenge: | Generative Reward Models (GenRMs) leverage synthesized Chains of Thought (CoT) but this approach introduces risks of overoptimization due to the inability to guarantee the correctness of the CoTs. |
| Approach: | They propose a criteria-based preference tree for GenRMs that uses chain of thought to generate reasoning . they show that synthesized data can be learned using a long CoT format . |
| Outcome: | The proposed model shows significant improvements over baselines on multiple human preference benchmarks. |
Semi-supervised Domain Adaptation for Dependency Parsing with Dynamic Matching Network (2022.acl-long)
Copied to clipboard
| Challenge: | supervised parsing models have achieved impressive results on in-domain texts, but their performances drop drastically on out-of-domain text due to data distribution shift. |
| Approach: | They propose a dynamic matching network on the shared-private model for semi-supervised cross-domain dependency parsing. |
| Outcome: | The proposed model outperforms baseline models on all domains and achieves state-of-the-art results on all datasets. |
CPsyCoun: A Report-based Multi-turn Dialogue Reconstruction and Evaluation Framework for Chinese Psychological Counseling (2024.findings-acl)
Copied to clipboard
Chenhao Zhang, Renhao Li, Minghuan Tan, Min Yang, Jingwei Zhu, Di Yang, Jiahao Zhao, Guancheng Ye, Chengming Li, Xiping Hu
| Challenge: | Existing datasets lack consulting knowledge, resulting in LLMs lacking professional consulting competence. |
| Approach: | They propose a report-based multi-turn dialogue reconstruction framework for Chinese psychological counseling that uses large language models to assist counseling. |
| Outcome: | The proposed framework is open-source and can be used in future research. |
FIPO: Free-form Instruction-oriented Prompt Optimization with Preference Dataset and Modular Fine-tuning Schema (2025.coling-main)
Copied to clipboard
| Challenge: | naive prompts can enhance the task performance of large language models, but they are resource-intensive. |
| Approach: | They propose an automatic prompt optimization method that refines naive prompts according to task outputs from in-box testing models. |
| Outcome: | The proposed method is based on a large-scale dataset and performed fairly across multiple models. |
SEER: Self-Aligned Evidence Extraction for Retrieval-Augmented Generation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for enhancing RAG performance rely on heuristic-based augmentation . Existing approaches rely heavily on a heuriistic-driven approach, resulting in poor generalization and skews in the evidence length. |
| Approach: | They propose a model-based evidence extraction learning framework that optimizes a vanilla model as an evidence extractor with desired properties through self-aligned learning. |
| Outcome: | The proposed method reduces the evidence length by 9.25 times and improves reliability and reliability. |
OPT-Tree: Speculative Decoding with Adaptive Draft Tree Structure (2025.tacl-1)
Copied to clipboard
| 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. |
ConsistTL: Modeling Consistency in Transfer Learning for Low-Resource Neural Machine Translation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing transfer learning methods for low-resource NMT are static, which simply transfer knowledge from a parent model to a child model once via parameter initialization. |
| Approach: | They propose a transfer learning method that can continuously transfer knowledge from the parent model during the training of the child model. |
| Outcome: | The proposed method can transfer knowledge from the parent model to the child model during the training of the child. |
Augmenting Multi-Agent Communication with State Delta Trajectory (2025.emnlp-main)
Copied to clipboard
| Challenge: | Multi-agent systems based on large language models (LLMs) have shown to be effective in downstream tasks. |
| Approach: | They propose a protocol that transfers both natural language tokens and token-wise state transition trajectory from one agent to another. |
| Outcome: | The proposed protocol can transfer both natural language tokens and token-wise state transition trajectory from one agent to another. |
APT: Improving Specialist LLM Performance with Weakness Case Acquisition and Iterative Preference Training (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models often require domain-specific fine-tuning to address targeted tasks, which risks degrading their general capabilities. |
| Approach: | They propose to use self-generated dis-preferred weakness data to enhance model performance with a targeted training approach that minimizes interference with existing knowledge base. |
| Outcome: | The proposed approach ensures no reduction in generic capacity and achieves superior performance on downstream tasks compared to existing methods. |
MIND Your Reasoning: A Meta-Cognitive Intuitive-Reflective Network for Dual-Reasoning in Multimodal Stance Detection (2026.acl-long)
Copied to clipboard
| 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)
Copied to clipboard
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 . |
MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction (2022.naacl-main)
Copied to clipboard
| Challenge: | Using a multi-reference multi-source evaluation dataset, Chinese grammatical error correction (CGEC) is relatively scarce. |
| Approach: | They propose a multi-reference multi-source evaluation dataset for Chinese grammar error correction . the dataset contains 7,063 sentences written by Chinese-as-a-Second-Language learners . |
| Outcome: | The proposed dataset can be used to evaluate Chinese grammar errors in Chinese. |
Identifying Chinese Opinion Expressions with Extremely-Noisy Crowdsourcing Annotations (2022.acl-long)
Copied to clipboard
| 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. |
Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysis (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to train ABSA model are limited by lack of annotated data . a dual-granularity pseudo labeling approach is proposed to solve this problem . |
| Approach: | They propose a framework for aspect-based sentiment analysis that uses annotated data to train ABSA models. |
| Outcome: | The proposed framework surpasses previous methods on benchmarks. |
Revisiting Demonstration Selection Strategies in In-Context Learning (2024.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) have shown an impressive ability to perform a wide range of tasks using in-context learning (ICL). |
| Approach: | They propose a data- and model-dependent method to select models using in-context learning, TopK + ConE, and propose unified explanations for the effectiveness of previous methods. |
| Outcome: | The proposed method improves language understanding and generation tasks with different model scales. |
Investigating Capsule Networks with Dynamic Routing for Text Classification (D18-1)
Copied to clipboard
| Challenge: | Earlier efforts in text modeling have achieved limited success on word meanings . convolutional neural networks (CNNs) are used to model higher level concepts and facts in texts . |
| Approach: | They propose three strategies to stabilize dynamic routing process to alleviate disturbance of noise capsules. |
| Outcome: | The proposed methods achieve state-of-the-art on 4 out of 6 datasets . they show that capsule networks exhibit significant improvement over baseline methods . |
Unveiling the Potential of BERT-family: A New Recipe for Building Scalable, General and Competitive Large Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Generative large language models (LLMs) have significantly influenced various aspects of society, reshaping how we access and interact with information and knowledge. |
| Approach: | They propose a pre-training task that helps BERT-family excel in wider applications . they also explore the integration of cutting-edge technologies into their models to further enhance their capabilities. |
| Outcome: | The proposed model exhibits performance levels comparable to current SOTA LLMs across a spectrum of tasks. |
Generator-Assistant Stepwise Rollback Framework for Large Language Model Agent (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to integrate thoughts with actions can cause irreversible error propagation . Xi et al., 2023; Zhang eet coll., 2023) have focused on enhancing large language model (LLM) agents capable of helping humans tackle real-world challenges. |
| Approach: | They propose a framework called Generator-Assistant Stepwise Rollback to induce better decision-making for LLM agents by integrating a generator and an assistant to examine each action produced by the generator. |
| Outcome: | The proposed framework improves on three widely used benchmarks and can integrate seamlessly with other methods. |
Emotion Detection with Neural Personal Discrimination (D19-1)
Copied to clipboard
| 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. |
One-Shot Learning as Instruction Data Prospector for Large Language Models (2024.acl-long)
Copied to clipboard
Yunshui Li, Binyuan Hui, Xiaobo Xia, Jiaxi Yang, Min Yang, Lei Zhang, Shuzheng Si, Ling-Hao Chen, Junhao Liu, Tongliang Liu, Fei Huang, Yongbin Li
| Challenge: | Contemporary practices in instruction tuning often hinge on enlarging data scaling without a clear strategy for ensuring data quality. |
| Approach: | They propose a method that leverages one-shot learning to discern and select high-quality instruction data from extensive datasets. |
| Outcome: | Nuggets outperforms existing methods on MT-Bench and Alpaca-Eval benchmarks. |
Efficient Safety Alignment of Large Language Models via Preference Re-ranking and Representation-based Reward Modeling (2025.acl-long)
Copied to clipboard
| 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. |
ALW: Adaptive Layer-Wise contrastive decoding enhancing reasoning ability in Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing research has demonstrated that contrast decoding of two different models can improve text quality in open-ended text generation but with limited gains on reasoning tasks. |
| Approach: | They propose a framework that dynamically disentangles noise in shallow layers from critical signals in deep layers to enhance reasoning ability. |
| Outcome: | The proposed framework improves answer accuracy while maintaining inference efficiency. |
MoDification: Mixture of Depths Made Easy (2025.naacl-long)
Copied to clipboard
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)
Copied to clipboard
| 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. |
Modeling Consistency Preference via Lexical Chains for Document-level Neural Machine Translation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Experimental results show that consistency preference for lexical chains reduces lexical translation inconsistency . Lexical translation consistency is a common discourse phenomenon . |
| Approach: | They propose a consistency-aware model which captures consistency context . they then define consistency-tailored latent variables which guide translation of corresponding sentences . |
| Outcome: | The proposed model significantly improves translation performance in ChineseEnglish and FrenchEnglish translation tasks. |
A Simple yet Effective Training-free Prompt-free Approach to Chinese Spelling Correction Based on Large Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Using an LLM for Chinese spelling correction tasks is completely different from previous approaches . given a Chinese character, there may exist many others with the same or similar pronunciations, or with similar shapes. |
| Approach: | They propose a training-free prompt-free approach to leverage large language models for Chinese spelling correction task. |
| Outcome: | The proposed model significantly improves performance on five public datasets, enabling them to compete with state-of-the-art domain-general CSC models. |
Part Represents Whole: Improving the Evaluation of Machine Translation System Using Entropy Enhanced Metrics (2022.findings-aacl)
Copied to clipboard
| Challenge: | Existing machine translation metrics have poor correlations with human assessments . entropy-based evaluations are often limited to a limited number of samples . |
| Approach: | They propose a fast and unsupervised approach to enhance machine translation metrics using entropy by introducing sentence-level difficulty. |
| Outcome: | The proposed method outperforms existing metrics on five sub-tracks in the WMT19 Metrics shared tasks. |
Revisiting Commonsense Reasoning in Machine Translation: Training, Evaluation and Challenge (2023.acl-long)
Copied to clipboard
| 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. |
Chinese Spelling Corrector Is Just a Language Learner (2024.findings-acl)
Copied to clipboard
| Challenge: | a recent study shows that self-supervised learning can improve Chinese spelling correction by removing errors from training data. |
| Approach: | They propose a method that decodes Chinese spelling correction models using noise . they say it outperforms current methods that rely on annotated errors . |
| Outcome: | The proposed method outperforms the confusion set in specific domains because there are no errors in the training data. |
Understanding the RoPE Extensions of Long-Context LLMs: An Attention Perspective (2025.coling-main)
Copied to clipboard
| Challenge: | Enabling LLMs to handle lengthy context is currently a research hotspot . a notable challenge limiting further customization is the inability of LLM to utilize context beyond pretrained length due to the inherent flaw of rotary position embedding (RoPE). |
| Approach: | They propose to extend the RoPE from an attention perspective and on two benchmarking tasks. |
| Outcome: | The proposed extension of the RoPE improves extrapolation and retrieval errors. |
Bridging Pre-trained Language Models and Hand-crafted Features for Unsupervised POS Tagging (2022.findings-acl)
Copied to clipboard
| Challenge: | Large-scale pre-trained language models (PLMs) have made extraordinary progress in most NLP tasks, but they fail to achieve state-of-the-art (SOTA) performance. |
| Approach: | They propose a Guassian HMM variant for unsupervised POS tagging that incorporates contexualized word representations into the decoder. |
| Outcome: | The proposed model outperforms state-of-the-art models on Penn Treebank and multilingual Universal Dependencies treebank v2.0. |
Can MLLMs Understand the Deep Implication Behind Chinese Images? (2025.acl-long)
Copied to clipboard
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)
Copied to clipboard
| 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. |
UniMoE-Audio: Unified Speech and Music Generation with Dynamic-Capacity Mixture-of-Experts (2026.acl-long)
Copied to clipboard
Zhenyu Liu, Yunxin li, Xuanyu Zhang, Qixun Teng, Shenyuan Jiang, Xinyu Chen, Haoyuan Shi, Haolan Chen, Fanbo Meng, Mingjun Zhao, Yu Xu, Yancheng He, Baotian Hu, Haizhou Li, Min Zhang
| Challenge: | Recent advances in unified multimodal models indicate a clear trend towards comprehensive content generation. |
| Approach: | They propose a unified speech and music generation model built upon a novel framework . they propose specialized MoE architectures and curated training strategies to tackle data imbalances . |
| Outcome: | The proposed model achieves state-of-the-art performance on major speech and music generation benchmarks. |
Revisiting Sparse Retrieval for Few-shot Entity Linking (2023.emnlp-main)
Copied to clipboard
| Challenge: | Entity linking (EL) aims to link ambiguous mentions to their corresponding entities in a knowledge base. |
| Approach: | They propose an ELECTRA-based keyword extractor to denoise the mention context and construct a better query expression. |
| Outcome: | The proposed method outperforms state-of-the-art models on the ZESHEL dataset by a significant margin. |
Robust Self-Augmentation for Named Entity Recognition with Meta Reweighting (2022.naacl-main)
Copied to clipboard
| Challenge: | Prior research has focused on reducing noise for specific methods to achieve an effective integration. |
| Approach: | They propose to use token substitution and mixup to improve named entity recognition (NER) using a meta-reweighting strategy, which is extensible and requires little effort. |
| Outcome: | The proposed method is extensible, imposing little effort on a specific self-augmentation method. |
Dynamic Planning for LLM-based Graphical User Interface Automation (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to planning for GUI tasks are limited due to long historical dialogues. |
| Approach: | They propose a novel approach to dynamic planning based on environmental feedback and execution history to guide action prediction in GUI tasks. |
| Outcome: | The proposed approach surpasses the strong GPT-4V baseline by +12.7% in accuracy. |
Exploring All-In-One Knowledge Distillation Framework for Neural Machine Translation (2023.emnlp-main)
Copied to clipboard
| 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. |
Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning? (2024.acl-long)
Copied to clipboard
| Challenge: | Current temporal reasoning datasets are limited to questions about single or isolated events, falling short in mirroring the realistic temporal characteristics involving concurrent nature and intricate temporal interconnections. |
| Approach: | They propose a co-temporal Question Answering benchmark that contains four co-time scenarios with 4,748 samples for evaluating the co-timing abilities of large language models. |
| Outcome: | The proposed benchmarks show that current LLMs struggle on CoTempQA tasks even when enhanced with Chain of Thought methodologies. |
Unsupervised Boundary-Aware Language Model Pretraining for Chinese Sequence Labeling (2022.emnlp-main)
Copied to clipboard
| Challenge: | Experimental results show that Boundary-Aware BERT can improve Chinese sequence labeling tasks. |
| Approach: | They propose to encode boundary information directly into pre-trained language models . they propose to use unsupervised boundary information instead of supervised boundary info . |
| Outcome: | The proposed architecture improves Chinese sequence labeling tasks on ten benchmarks. |
Mirror: A Universal Framework for Various Information Extraction Tasks (2023.emnlp-main)
Copied to clipboard
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. |
End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions (2023.emnlp-main)
Copied to clipboard
| Challenge: | End-to-end task-oriented dialogue (EToD) can generate responses in an end-to end fashion without modular training, which attracts escalating popularity. |
| Approach: | They present a systematic review of EToD and propose a unified perspective to summarize existing approaches and recent trends. |
| Outcome: | The proposed approaches can generate responses in an end-to-end fashion without modular training, which attracts escalating popularity. |
Code Summarization with Structure-induced Transformer (2021.findings-acl)
Copied to clipboard
| Challenge: | Code summarization (CS) is a promising area in recent language understanding . previous work using structurebased traversal or non-sequential models to learn structural program semantics has shown no performance gain . |
| Approach: | They propose to use a structure-based traversal model to learn structural program semantics to generate human language automatically for programming language in the format of source code. |
| Outcome: | Experiments show that the proposed method achieves state-of-the-art on benchmarks. |
From Role-Play to Drama-Interaction: An LLM Solution (2024.findings-acl)
Copied to clipboard
| Challenge: | aristotle defined drama as a form of storytelling that involves a predefined storyline, emotions and thoughts. |
| Approach: | They propose to use LLMs to create an immersive mode of storytelling . they propose to create a backbone drama LLM to drive the playing process . |
| Outcome: | The proposed model can be used to drive the playing process, the authors say . it can be compared with existing models and can be evaluated on multiple scenarios. |
DUAL-REFLECT: Enhancing Large Language Models for Reflective Translation through Dual Learning Feedback Mechanisms (2024.acl-short)
Copied to clipboard
| Challenge: | Existing self-reflection methods lack effective feedback information, limiting the translation performance of large language models (LLMs). |
| Approach: | They propose a framework that leverages the dual learning of translation tasks to provide effective feedback, thereby enhancing the models’ self-reflective abilities and improving translation performance. |
| Outcome: | The proposed framework improves the models’ self-reflective abilities and improves translation accuracy and eliminating ambiguities across translation tasks. |
Recognizing Everything from All Modalities at Once: Grounded Multimodal Universal Information Extraction (2024.findings-acl)
Copied to clipboard
| 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. |
Efficient Second-Order TreeCRF for Neural Dependency Parsing (2020.acl-main)
Copied to clipboard
| Challenge: | In the deep learning (DL) era, dependency parsing models are extremely simplified with little hurt on performance thanks to the remarkable capability of multi-layer BiLSTMs in context representation. |
| Approach: | They propose to extend the biaffine parser to a second-order TreeCRF extension to reduce the complexity of the inside-outside algorithm. |
| Outcome: | The proposed extension can be used to batchify the inside and Viterbi algorithms and avoid the complex outside algorithm via efficient back-propagation. |
DEFT: Distribution-guided Efficient Fine-Tuning for Human Alignment (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Experimental results show that the methods enhanced by DEFT outperform the original methods in both alignment capability and generalization ability, with significantly reduced training time. |
| Approach: | They propose a distribution-based alignment framework that integrates data filtering and distributional guidance to improve alignment efficiency and generalization ability. |
| Outcome: | The proposed framework outperforms existing methods in alignment capability and generalization ability with significantly reduced training time. |
Fair-CCD: Mitigating Bias in Large Language Models for Tabular Classification Through Context-Contrastive Decoding (2026.acl-long)
Copied to clipboard
| 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)
Copied to clipboard
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. |
Reasoning While Asking: Transforming Reasoning Large Language Models from Passive Solvers to Proactive Inquirers (2026.acl-long)
Copied to clipboard
| Challenge: | Existing reasoning-oriented LLMs lack a blind self-thinking paradigm . current models fail to recognize when their reasoning is underinformed or based on ambiguous user instructions . |
| Approach: | They propose a new reasoning paradigm that transforms LLMs into proactive inquirers that interleave reasoning with clarification. |
| Outcome: | The proposed model outperforms baseline models on mathematical reasoning, code generation, and document editing. |
OpenResearcher: Unleashing AI for Accelerated Scientific Research (2024.emnlp-demo)
Copied to clipboard
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. |
SAT: Balancing Reasoning Accuracy and Efficiency with Stepwise Adaptive Thinking (2026.acl-long)
Copied to clipboard
| Challenge: | Large Reasoning Models (LRMs) produce excessively long Chains of Thought (COT) Existing solutions that improve token efficiency but sacrifice fine-grained control can disrupt the logical integrity of the reasoning process. |
| Approach: | They propose a framework that performs step-level, difficulty-aware pruning while preserving the core reasoning structure. |
| Outcome: | Experiments show that SAT reduces reasoning tokens by 40% while maintaining or improving accuracy. |
𝒜3: Automatic Alignment Framework for Attributed Text Generation (2025.acl-long)
Copied to clipboard
| 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. |
G-Tuning: Improving Generalization of Pre-trained Language Models with Generative Adversarial Network (2023.findings-acl)
Copied to clipboard
| Challenge: | Empirical evaluations on the GLUE benchmark demonstrate that fine-tuning can enhance the generalization performance of pre-trained language models (PLMs) in downstream tasks. |
| Approach: | They propose a fine-tuning framework that transforms the latent representation of pre-trained language models from a universal space to a target space and integrates a generative adversarial network into the fine-untun process. |
| Outcome: | Empirical evaluations on the GLUE benchmark and two additional demanding scenarios show that the proposed framework can improve the generalization performance of pre-trained language models (PLMs) in downstream tasks. |
Multi-grained Chinese Word Segmentation with Weakly Labeled Data (2020.coling-main)
Copied to clipboard
| Challenge: | Existing work on single-grained word segmentation (SWS) focuses on segmenting a sentence into multiple word sequences to preserve all words of different granularities. |
| Approach: | They propose to use a graph-based parser to accommodate weakly labeled data for MWS by employing a simple yet competitive graph-basic parsers with local loss. |
| Outcome: | The proposed model outperforms the state-of-the-art model on weakly labeled data on a high-quality dataset from canonical newswire (NEWS) and non-canonical web (BAIKE) data. |
Question-guided Knowledge Graph Re-scoring and Injection for Knowledge Graph Question Answering (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Knowledge graph question answering (KGQA) aims to provide factual answers to natural language questions by leveraging structured information stored in a knowledge graph. |
| Approach: | They propose a Question-guided Knowledge Graph Re-scoring method to eliminate noisy pathways for the input question, thereby focusing specifically on pertinent factual knowledge. |
| Outcome: | The proposed method eliminates noisy pathways for the input question, thereby focusing specifically on pertinent factual knowledge. |
Dynamic Long Context Reasoning over Compressed Memory via End-to-End Reinforcement Learning (2026.acl-long)
Copied to clipboard
| Challenge: | Large Language Models face quadratic computational costs, information forgetting, and context fragmentation . current methodologies diverge into three paradigms, each facing inherent trade-offs between Equal contribution. |
| Approach: | They propose a framework that enables efficient long-context inference via chunk-wise compression and selective memory recall. |
| Outcome: | The proposed framework reduces peak GPU memory usage and speeds up inference on multi-hop reasoning benchmarks. |
DataSciBench: An LLM Agent Benchmark for Data Science (2026.findings-acl)
Copied to clipboard
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. |
SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to grammatical error correction are unreliable when processing ungrammatically . a new approach is proposed that incorporates dependency syntactic information into the encoder part of GEC models. |
| Approach: | They propose a syntax-enhanced grammatical error correction approach called SynGEC that incorporates dependency syntactic information into the encoder part of GEC models. |
| Outcome: | The proposed approach outperforms strong baselines and achieves competitive performance on mainstream English and Chinese GEC datasets. |
DeMPT: Decoding-enhanced Multi-phase Prompt Tuning for Making LLMs Be Better Context-aware Translators (2024.emnlp-main)
Copied to clipboard
| Challenge: | Concatenating large language models are adapted to context-aware neural machine translation in a concatenated way . a recent paradigm shift has been witnessed in discourse-related challenges such as zero pronoun translation . |
| Approach: | They propose an alternative adaptation approach to make large language models discriminately model and utilize inter- and intra-sentence contexts. |
| Outcome: | The proposed approach outperforms concatenation mode and improves performance in discourse modeling. |
WindowsWorld: A Process-Centric Benchmark of Autonomous GUI Agents in Professional Cross-Application Environments (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing GUI agents perform poorly on multi-application tasks, stalling at early sub-goals. |
| Approach: | They propose to assess GUI Agents on complex multi-step tasks that mirror real-world professions. |
| Outcome: | The proposed benchmark contains 181 tasks with an average of 5.0 sub-goals across 17 common desktop applications, of which 78% are inherently multi-application. |
FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented Generation (2025.acl-demo)
Copied to clipboard
| Challenge: | Existing frameworks for retrieval-augmented generation (RAG) lack new techniques, difficulties in algorithm reproduction and sharing, and high system overhead. |
| Approach: | They propose a retrieval-augmented generation framework specifically designed for research and prototyping that supports text-based, multimodal, and network-based RAG. |
| Outcome: | The proposed framework supports text-based, multimodal, and network-based RAG, providing comprehensive lifecycle support alongside efficient asynchronous processing and persistent caching capabilities. |
Multi-Turn Dialogue Generation in E-Commerce Platform with the Context of Historical Dialogue (2020.findings-emnlp)
Copied to clipboard
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. |
PromptST: Abstract Prompt Learning for End-to-End Speech Translation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Experimental results show that PromptST can improve speech-to-text translation by capturing richer linguistic knowledge. |
| Approach: | They propose a plug-in prompt-enhanced S2T model that captures richer linguistic knowledge . they use a 10GB linguistic probing benchmark to investigate the fusion of speech and text features . |
| Outcome: | The proposed model can improve on a strong baseline by capturing richer linguistic knowledge. |
Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon Induction (2021.findings-acl)
Copied to clipboard
| Challenge: | Bilingual Lexicon Induction (BLI) aims to map words in one language to their translations in another. |
| Approach: | They propose a mechanism to combine static word embeddings and contextual representations to utilize the advantages of both paradigms. |
| Outcome: | The proposed method improves performance on supervised and unsupervised BLI benchmarks on all language pairs by average improving 3.2 points over baselines. |
Synchronous Refinement for Neural Machine Translation (2022.findings-acl)
Copied to clipboard
| 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. |
SeaPO: Strategic Error Amplification for Robust Preference Optimization of Large Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for preference optimization of large language models use pairs of positive and negative samples, but the quality of positive samples may become similar during training, complicating preference learning. |
| Approach: | SeaPO introduces error types commonly occurring in large language models to improve preference learning. |
| Outcome: | SeaPO introduces error types into model Preference Optimization to improve model performance . negative samples are more erroneous than positive samples, and preference-based training mitigates errors . |
A Non-autoregressive Generation Framework for End-to-End Simultaneous Speech-to-Any Translation (2024.acl-long)
Copied to clipboard
| Challenge: | Existing translation pipelines require additional cascade components to achieve speech-to-speech translation. |
| Approach: | They propose a non-autoregressive generation framework for simultaneous speech translation . it integrates both text-to-text and speech-tospeech tasks into a unified framework . |
| Outcome: | The proposed framework outperforms state-of-the-art models in speech-to-text and speech- to-speech tasks. |
Bridging the Domain Gaps in Context Representations for k-Nearest Neighbor Neural Machine Translation (2023.acl-long)
Copied to clipboard
Zhiwei Cao, Baosong Yang, Huan Lin, Suhang Wu, Xiangpeng Wei, Dayiheng Liu, Jun Xie, Min Zhang, Jinsong Su
| Challenge: | Existing methods to improve k-Nearest neighbor machine translation (kNN-MT) are based on the ability to non-parametrically adapt to new domains. |
| Approach: | They propose a method to boost the datastore retrieval of k-Nearest neighbor machine translation by reconstructing the original datastore. |
| Outcome: | The proposed method boosts the retrieval and translation quality of k-Nearest neighbor machine translation by reconstructing the original datastore. |
Marathon: A Race Through the Realm of Long Context with Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Existing long-context benchmarks do not accurately evaluate large language models’ comprehension and reasoning abilities in extended texts. |
| Approach: | They propose a new evaluation benchmark that adopts a multiple-choice question format and uses a multi-choke question format to assess the comprehension and reasoning skills of large language models. |
| Outcome: | The proposed benchmark provides a rapid, precise, and unbiased appraisal of the long-context comprehension skills of large language models. |
A Read-and-Select Framework for Zero-shot Entity Linking (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods focus on the candidate retrieval stage and ignore the essential candidate ranking stage, which disambiguates among entities and makes the final linking prediction. |
| Approach: | They propose a read-and-select framework that models the main components of entity disambiguation . they use mention context to output mention-aware entity representations . |
| Outcome: | The proposed framework achieves state-of-the-art performance on established zero-shot entity linking dataset ZESHEL with 2.55% micro-average accuracy gain, with no need for laborious multi-phase pre-training used in most of the previous work. |
One vs. Many QA Matching with both Word-level and Sentence-level Attention Network (C18-1)
Copied to clipboard
| 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. |
Don’t Go To Extremes: Revealing the Excessive Sensitivity and Calibration Limitations of LLMs in Implicit Hate Speech Detection (2024.acl-long)
Copied to clipboard
| Challenge: | Several studies have examined whether large language models exhibit bias or discrimination against individuals or groups in terms of protected attributes like race, gender, or religion. |
| Approach: | They evaluate LLMs' ability to detect implicit hate speech and express confidence in their responses by considering prompt patterns and mainstream uncertainty estimation methods. |
| Outcome: | The proposed models exhibit two extremes: (1) excessive sensitivity towards groups or topics that may cause fairness issues, resulting in misclassifying benign statements as hate speech; (2) confidence scores for each method excessively concentrate on a fixed range, remaining unchanged regardless of the dataset’s complexity. |
Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA (2024.emnlp-main)
Copied to clipboard
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. |
Efficient k-Nearest-Neighbor Machine Translation with Dynamic Retrieval (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing models for non-parametric domain adaptation lack kNN retrieval at each timestep, leading to substantial time overhead. |
| Approach: | They propose a kNN-MT-based model that uses a domain-specific translation knowledge store to interpolate the prediction distribution of the model. |
| Outcome: | The proposed model significantly extends kNN-MT with dynamic retrieval on widely-used datasets. |
JANUS: Joint Autoregressive and Non-autoregressive Training with Auxiliary Loss for Sequence Generation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to train autoregressive and non-autoregressive models only consider relevance of model parameters, ignoring correlations between the two manners. |
| Approach: | They propose a joint autoregressive and non-autoregressive training method using aUxiliary losS to enhance the model performance in both AR and NAR manners simultaneously. |
| Outcome: | The proposed method improves the model performance in both AR and NAR manners and reduces the inference latency. |
A Rationale-centric Counterfactual Data Augmentation Method for Cross-Document Event Coreference Resolution (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing state-of-the-art event coreference resolution systems rely on spurious and spurious associations in the input mention pair text. |
| Approach: | They propose a rationale-centric counterfactual data augmentation method that leverages the debiasing capability of counterfact data haussed by LLM-in-the-loop to mitigate spurious association while emphasizing causation. |
| Outcome: | The proposed method achieves state-of-the-art on three popular cross-document benchmarks and demonstrates robustness in out-of domain scenarios. |
Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network (P19-1)
Copied to clipboard
| 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. |
Non-autoregressive Streaming Transformer for Simultaneous Translation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Simultaneous machine translation models are trained to strike a balance between latency and translation quality. |
| Approach: | They propose a non-autoregressive streaming Transformer which generates blank tokens and decodes repetitive tokens to adjust its READ/WRITE strategy flexibly. |
| Outcome: | The proposed model outperforms previous strong autoregressive models on various benchmarks on siMT. |
Towards Demonstration-Aware Large Language Models for Machine Translation (2024.findings-acl)
Copied to clipboard
| Challenge: | Large language models for machine translation often face difficulties in leveraging demonstrations to further improve their performance. |
| Approach: | They propose a novel approach that integrates demonstration-aware training and inference strategies within the framework of tuning-based LTMs. |
| Outcome: | The proposed model integrates demonstration-aware training and inference strategies within tuning-based LTMs. |
NaviMaster: Learning a Unified Policy for GUI and Embodied Navigation Tasks (2026.acl-long)
Copied to clipboard
| 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. |
Temporal Knowledge Question Answering via Abstract Reasoning Induction (2024.acl-long)
Copied to clipboard
| Challenge: | a new method to enhance temporal knowledge reasoning in large language models addresses this challenge . Abstract Reasoning Induction (ARI) framework provides factual knowledge support to LLMs . |
| Approach: | They propose an abstract reasoning induction framework which divides temporal reasoning into two phases: Knowledge agnostic and Knowledge-based. |
| Outcome: | The proposed method achieves significant gains on two temporal QA datasets. |
RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering (2024.emnlp-main)
Copied to clipboard
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. |
Addressing the Length Bias Challenge in Document-Level Neural Machine Translation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Document-level neural machine translation (DNMT) models incorporate context information through increased maximum lengths of source and target sentences. |
| Approach: | They propose a sliding decoding strategy that limits the length of target sentences . they propose 'length-normalized attention mechanism' to aid the model in focusing on target information . |
| Outcome: | The proposed method can achieve state-of-the-art results on open datasets. |
Isotropic Representation Can Improve Zero-Shot Cross-Lingual Transfer on Multilingual Language Models (2023.findings-emnlp)
Copied to clipboard
| 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. |
Bridging the Gap between Training and Inference: Multi-Candidate Optimization for Diverse Neural Machine Translation (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing diverse NMT models lack translation diversity due to a discrepancy between training and inference . despite the success of diverse NTM, there is still a lack of translation diversity . |
| Approach: | They propose a multi-candidate optimization framework for diverse NMT to deal with this defect. |
| Outcome: | The proposed framework is transparent to basic diverse NMT models, and universally makes better trade-off between diversity and quality. |
Supervised Treebank Conversion: Data and Approaches (P18-1)
Copied to clipboard
| Challenge: | Existing work on treebank conversion focuses on unsupervised treebanks . lack of manually labeled data means that sentences have two syntactic trees at the same time. |
| Approach: | They propose supervised treebank conversion using bi-tree aligned sentences . they propose two conversion approaches based on state-of-the-art deep biaffine parser . |
| Outcome: | The proposed method outperforms the state-of-the-art deep biaffine parser on the English WSJ dataset by 0.97 (93.76% -92.79%) |
A Challenge Dataset and Effective Models for Conversational Stance Detection (2024.lrec-main)
Copied to clipboard
| Challenge: | stance detection studies focus on evaluating stances within individual instances, hindering progress of conversational stance analysis. |
| Approach: | They propose a multi-turn conversation stance detection dataset that encompasses multiple targets for conversational stance detector. |
| Outcome: | The proposed dataset encompasses multiple targets for conversational stance detection. |
STORYTELLER: An Enhanced Plot-Planning Framework for Coherent and Cohesive Story Generation (2025.findings-acl)
Copied to clipboard
Jiaming Li, Yukun Chen, Ziqiang Liu, Minghuan Tan, Lei Zhang, Yunshui Li, Run Luo, Longze Chen, Jing Luo, Ahmadreza Argha, Hamid Alinejad-Rokny, Wei Zhou, Min Yang
| Challenge: | Existing methods for storytelling lack coherence and consistency, compromising the overall storytelling experience. |
| Approach: | They propose a novel approach that improves the coherence and consistency of automatically generated stories by managing plot nodes and enabling dynamic interactions between different parts of the story. |
| Outcome: | The proposed approach outperforms existing methods in 84.33% of the trials. |
Submodular-based In-context Example Selection for LLMs-based Machine Translation (2024.lrec-main)
Copied to clipboard
| Challenge: | Prior studies have focused on the role of well-chosen examples in in-context learning . |
| Approach: | They propose to use multiple translational factors for in-context example selection by using monotone submodular function maximization. |
| Outcome: | The proposed approach outperforms random selection and robust single-factor baselines across various NLP tasks. |
kNN-TL: k-Nearest-Neighbor Transfer Learning for Low-Resource Neural Machine Translation (2023.acl-long)
Copied to clipboard
| Challenge: | Transfer learning is an effective technique for enhancing low-resource neural machine translation (NMT) however, these methods do not make use of the parent knowledge during the child inference, which may limit the translation performance. |
| Approach: | They propose a k-Nearest-Neighbor Transfer Learning approach which leverages the parent knowledge throughout the entire developing process of the child model. |
| Outcome: | The proposed approach outperforms strong baselines on four low-resource translation tasks. |
Mitigating Hallucinations in Large Vision-Language Models via Entity-Centric Multimodal Preference Optimization (2025.emnlp-main)
Copied to clipboard
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. |
Can We Achieve High-quality Direct Speech-to-Speech Translation without Parallel Speech Data? (2024.acl-long)
Copied to clipboard
| Challenge: | Existing two-pass direct speech-to-speech translation models require parallel speech data to train, which is challenging to collect. |
| Approach: | They propose a two-pass direct speech-to-speech translation (S2ST) model that decomposes the task into speech- to-text translation (s2TT) and text-tospech (TTS) they propose 'composer' S2ST model that integrates pretrained S2TT and TTS models into a direct S2 ST model. |
| Outcome: | The proposed model integrates pretrained S2TT and TTS models into a direct S2ST model without parallel speech data. |
Improving AMR Parsing with Sequence-to-Sequence Pre-training (2020.emnlp-main)
Copied to clipboard
| Challenge: | Abstract meaning representation (AMR) parsing is limited by the size of curated datasets. |
| Approach: | They propose a seq2seq pre-training approach to build pre-trained models on three relevant tasks. |
| Outcome: | The proposed model improves performance on three relevant tasks while maintaining the response of pre-trained models. |
Revisiting Grammatical Error Correction Evaluation and Beyond (2022.emnlp-main)
Copied to clipboard
| Challenge: | Pretraining-based (PT) evaluation metrics are not effective for training grammatical error correction systems. |
| Approach: | They propose a pretraining-based GEC evaluation metric which only uses PT-based metrics to score the corrected parts of the system. |
| Outcome: | The proposed evaluation metric outperforms existing methods on a CoNLL14 evaluation task. |
3AM: An Ambiguity-Aware Multi-Modal Machine Translation Dataset (2024.lrec-main)
Copied to clipboard
Xinyu Ma, Xuebo Liu, Derek F. Wong, Jun Rao, Bei Li, Liang Ding, Lidia S. Chao, Dacheng Tao, Min Zhang
| Challenge: | Existing studies have shown that visual information in existing MMT datasets is insufficient, causing models to disregard it and overestimate their capabilities. |
| Approach: | They propose to use 3AM to create an ambiguity-aware multimodal machine translation dataset. |
| Outcome: | The proposed dataset includes more ambiguity and a greater variety of captions and images than other MMT datasets. |
A Syntax-aware Multi-task Learning Framework for Chinese Semantic Role Labeling (D19-1)
Copied to clipboard
| Challenge: | Semantic role labeling (SRL) aims to identify the predicate-argument structure of a sentence. |
| Approach: | They propose to use a unified span-based model for Chinese SRL as a strong baseline. |
| Outcome: | The proposed framework achieves state-of-the-art 87.54 and 88.5 F1 scores on the Chinese Proposition Bank and CoNLL-2009 datasets. |
Medico: Towards Hallucination Detection and Correction with Multi-source Evidence Fusion (2024.emnlp-demo)
Copied to clipboard
| 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. |
Two Intermediate Translations Are Better Than One: Fine-tuning LLMs for Document-level Translation Refinement (2025.acl-long)
Copied to clipboard
| Challenge: | Recent research has shown that large language models (LLMs) can enhance translation quality through self-refinement. |
| Approach: | They propose to extend translation refinement from sentence-level to document-level by using document-to-document (Doc2Doc) translations. |
| Outcome: | The proposed method improves translation quality across ten translation tasks with LLaMA-3-8B-Instruct and Mistral-Nemo-Instru. |
Syntax-Aware Opinion Role Labeling with Dependency Graph Convolutional Networks (2020.acl-main)
Copied to clipboard
| 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)
Copied to clipboard
| 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. |
Improving Disentangled Text Representation Learning with Information-Theoretic Guidance (2020.acl-main)
Copied to clipboard
Pengyu Cheng, Martin Renqiang Min, Dinghan Shen, Christopher Malon, Yizhe Zhang, Yitong Li, Lawrence Carin
| Challenge: | Disentangled representation learning (DRL) maps different aspects of data into distinct and independent low-dimensional latent vector spaces. |
| Approach: | They propose a method that manifests disentangled representations of text without supervision on semantics by minimizing the upper bound between style and content. |
| Outcome: | The proposed method improves on conditional text generation and text-style transfer tasks and improves style preservation. |
Speech Sense Disambiguation: Tackling Homophone Ambiguity in End-to-End Speech Translation (2024.acl-long)
Copied to clipboard
| Challenge: | End-to-end speech translation (ST) models require simultaneous crossmodal and crosslingual transformations to be effective. |
| Approach: | They propose a homophone-aware contrastive learning approach that integrates a speech-text masking strategy to reduce ambiguity. |
| Outcome: | The proposed approach achieves SOTA results on BLEU scores on different MuST-C and CoVoST ST tasks, underlining its effectiveness in reducing speech sense ambiguity. |
Improving Chinese Named Entity Recognition with Multi-grained Words and Part-of-Speech Tags via Joint Modeling (2024.lrec-main)
Copied to clipboard
| 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. |
ToolOmni: Enabling Open-World Tool Use via Agentic learning with Proactive Retrieval and Grounded Execution (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for large language models struggle to align user intent with tool semantics or generalize to unseen tools. |
| Approach: | They propose a framework that enables LLMs for open-world tool use by proactive retrieval and grounded execution within a reasoning loop. |
| Outcome: | The proposed framework surpasses baselines in retrieval and execution efficiency by +10.8%. |
TasTe: Teaching Large Language Models to Translate through Self-Reflection (2024.acl-long)
Copied to clipboard
| 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. |
Improving Seq2Seq Grammatical Error Correction via Decoding Interventions (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to grammatical error correction (GEC) are sequence-to-sequence and sequence-edit. |
| Approach: | They propose a unified decoding intervention framework that employs an external critic to assess the appropriateness of the token to be generated incrementally. |
| Outcome: | The proposed framework outperforms baselines and state-of-the-art methods on English and Chinese datasets. |
Probing Graph Decomposition for Argument Pair Extraction (2023.findings-acl)
Copied to clipboard
| Challenge: | Argument pair extraction (APE) aims to extract interactive argument pairs from two passages within a discussion. |
| Approach: | They propose a method to extract interactive argument pairs from two passages . they propose to decompose the probing graph into four sub-graphs based on inter- and intra-passage perspectives . |
| Outcome: | The proposed method improves on strong baselines on two benchmark datasets. |
Three Stream Based Multi-level Event Contrastive Learning for Text-Video Event Extraction (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for event extraction ignore motion representations in videos and are misguided by background noise. |
| Approach: | They propose a text-video based multimodal event extraction framework that integrates video appearance features and motion representations with video appearance. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods in the event extraction field. |
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms (P18-1)
Copied to clipboard
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. |
Translatotron-V(ison): An End-to-End Model for In-Image Machine Translation (2024.findings-acl)
Copied to clipboard
| Challenge: | In-image machine translation (IIMT) aims to translate an image containing texts in source language into an image with translations in target language. |
| Approach: | They propose an end-to-end IIMT model with four modules that translate images . they propose a two-stage training framework to assist the model in learning alignment across languages . |
| Outcome: | The proposed model outperforms cascaded models with only 70.9% of parameters and is highly accurate. |
Sentiment Classification towards Question-Answering with Hierarchical Matching Network (D18-1)
Copied to clipboard
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)
Copied to clipboard
| 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)
Copied to clipboard
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. |
A Multi-Modal Context Reasoning Approach for Conditional Inference on Joint Textual and Visual Clues (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for conditional inference on joint textual and visual clues lack multimodal context reasoning capability. |
| Approach: | They propose a multi-modal context reasoning approach that embeds textual semantics and objective image information into the pretrained language model to perform context reasoning. |
| Outcome: | The proposed approach improves on two data sets and shows 4.8% gain on the PMR. |
TemplateGEC: Improving Grammatical Error Correction with Detection Template (2023.acl-long)
Copied to clipboard
| 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. |
MuCPAD: A Multi-Domain Chinese Predicate-Argument Dataset (2022.naacl-main)
Copied to clipboard
| Challenge: | Recent studies show that shallow semantic role labeling (SRL) performance drops under out-of-domain setting. |
| Approach: | They propose to annotate a multi-domain Chinese predicate-argument dataset using a frame-free annotation methodology and strict double annotation for improving data quality. |
| Outcome: | The proposed dataset is compared with a dataset from six different domains. |
MathAgent: Adversarial Evolution of Constraint Graphs for Mathematical Reasoning Data Synthesis (2026.findings-acl)
Copied to clipboard
| Challenge: | Current approaches to synthesising high-quality mathematical reasoning data without human priors suffer from mode collapse and limited logical complexity. |
| Approach: | They propose a hierarchical synthesis framework that formulates data synthesis as an unsupervised optimization problem over a constraint graph followed by semantic instantiation rather than a direct text generation task. |
| Outcome: | The proposed framework outperforms widely-used datasets on eight mathematical benchmarks. |
Redundancy Principles for MLLMs Benchmarks (2025.acl-long)
Copied to clipboard
Zicheng Zhang, Xiangyu Zhao, Xinyu Fang, Chunyi Li, Xiaohong Liu, Xiongkuo Min, Haodong Duan, Kai Chen, Guangtao Zhai
| Challenge: | Rapid growth of Multi-modality Large Language Models has led to significant redundancy among benchmarks. |
| Approach: | They propose a framework to improve MLLM benchmark design by identifying redundancy at three levels: dimension, instance, and cross-benchmark redundancies. |
| Outcome: | The proposed framework streamlines evaluations and enhances reliability. |
Clustering Pseudo Language Family in Multilingual Translation Models with Fisher Information Matrix (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to cluster languages based on ancestral families can yield suboptimal results due to variations in the datasets employed during the model’s training phase. |
| Approach: | They propose a method that leverages the fisher information matrix to cluster language families anchored on the multilingual translation model's characteristics. |
| Outcome: | The proposed method improves performance over conventional language families in adapting a multilingual translation model to unfamiliar language pairs. |
M-CNER: A Corpus for Chinese Named Entity Recognition in Multi-Domains (L18-1)
Copied to clipboard
| Challenge: | NER is one of the most important natural language processing tasks. |
| Approach: | They propose to annotate sentences from human-computer interaction, social media, and e-commerce using two rounds of annotation. |
| Outcome: | The proposed system performs the best on all the data sets. |
Tool learning via Inference-time Scaling and Cycle Verifier (2025.findings-acl)
Copied to clipboard
| 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. |
Basic Reading Distillation (2025.acl-long)
Copied to clipboard
| Challenge: | Large language models require high computational resources which limits their deployment in real-world applications. |
| Approach: | They propose to distill large language models into smaller language models by either knowledge distillation or task distillation. |
| Outcome: | The proposed model outperforms or performs comparable to over 20x bigger LLMs on language inference benchmarks and BIG-bench tasks. |
Adaptive Weighting for Neural Machine Translation (C18-1)
Copied to clipboard
| Challenge: | Existing weighted sum models (WSMs) take inputs and generate one output, but they are independent of each other and are fixed for all inputs. |
| Approach: | They propose adaptive weighting for WSMs to control the contribution of each input and output state. |
| Outcome: | The proposed weighting improves translation accuracy by 1.49 and 0.92 BLEU points on Chinese-to-English translation and English-to German translation tasks. |
Neural Sparse Topical Coding (P18-1)
Copied to clipboard
| 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. |
Contrastive Attention Mechanism for Abstractive Sentence Summarization (D19-1)
Copied to clipboard
| Challenge: | Existing attention mechanisms for abstractive sentence summarization are based on rule-based methods and large-scale training corpora. |
| Approach: | They propose a contrastive attention mechanism that extends the sequence-to-sequence framework for abstractive sentence summarization task. |
| Outcome: | The proposed mechanism improves the state-of-the-art on the abstractive sentence summarization task. |
MelTrim: Coarse-to-Fine Data Pruning for Speech Classification (2026.findings-acl)
Copied to clipboard
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)
Copied to clipboard
| 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. |
Unlocking Recursive Thinking of LLMs: Alignment via Refinement (2025.findings-acl)
Copied to clipboard
| 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)
Copied to clipboard
| 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)
Copied to clipboard
| 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. |
L-CiteEval: A Suite for Evaluating Fidelity of Long-context Models (2025.acl-long)
Copied to clipboard
| Challenge: | Long-context models (LCMs) have seen remarkable advancements in recent years, facilitating tasks like long-document QA. |
| Approach: | They propose an out-of-the-box suite that can assess both generation quality and fidelity in long-context understanding tasks. |
| Outcome: | The proposed suite can assess both generation quality and fidelity in long-context understanding tasks. |
Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word Representations (N19-1)
Copied to clipboard
| Challenge: | Syntax integration has been demonstrated highly effective in neural machine translation (NMT). |
| Approach: | They propose a method to integrate source-side syntax implicitly for neural machine translation . they use hidden representations of a well-trained end-to-end dependency parser to concatenate them with ordinary word embeddings to enhance basic NMT models. |
| Outcome: | The proposed method outperforms existing methods on two translation tasks . it can be easily integrated into the widely-used sequence-to-sequence (Seq2Sequen) framework . |
AutoCAP: Towards Automatic Cross-lingual Alignment Planning for Zero-shot Chain-of-Thought (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to cross-lingual chain-of-thought integrate reasoning knowledge from different languages, but they still rely on manual language specification and weight allocation. |
| Approach: | They propose an automatic cross-lingual alignment planning framework that integrates reasoning knowledge from different languages. |
| Outcome: | The proposed framework surpasses existing methods that require manual effort to integrate languages. |
Improving the Transformer Translation Model with Document-Level Context (D18-1)
Copied to clipboard
| Challenge: | Existing models for document-level context translation ignore documentlevel context. |
| Approach: | They propose a document-level context encoder to represent document- level context and integrate it into the Transformer model. |
| Outcome: | Experiments on NIST Chinese-English and IWSLT French-English datasets show that the proposed translation model outperforms the Transformer model significantly. |
APGN: Adversarial and Parameter Generation Networks for Multi-Source Cross-Domain Dependency Parsing (2021.findings-emnlp)
Copied to clipboard
| 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. |
ZigZagKV: Dynamic KV Cache Compression for Long-context Modeling based on Layer Uncertainty (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods to accelerate inference of Large Language models (LLMs) are limited in their ability to retain key tokens as input length increases. |
| Approach: | They propose a method that leverages layer uncertainty to allocate budget size for each layer to reduce memory usage. |
| Outcome: | The proposed method reduces memory usage of the KV caches to only 20% when compared to full KV inference while achieving nearly lossless performance. |
New Terms, New Toxicity: Consensus-based Chinese Neologism Toxicity Detection via Search-Augmented LLMs (2026.acl-long)
Copied to clipboard
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. |
Modeling Intra- and Inter-Modal Relations: Hierarchical Graph Contrastive Learning for Multimodal Sentiment Analysis (2022.coling-1)
Copied to clipboard
| Challenge: | Existing studies in Multimodal Sentiment Analysis lack a mechanism to understand complex relations between different modalities. |
| Approach: | They propose a hierarchical graph contrastive learning framework for multimodal sentiment analysis that explores the relationships between modality representations. |
| Outcome: | The proposed framework outperforms the state-of-the-art in multimodal sentiment analysis on two benchmark datasets. |
Disambiguated Lexically Constrained Neural Machine Translation (2023.findings-acl)
Copied to clipboard
| 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)
Copied to clipboard
| 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. |
AQuilt: Weaving Logic and Self-Inspection into Low-Cost, High-Relevance Data Synthesis for Specialist LLMs (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to synthesis large language models often suffer from performance limitations and high computational costs. |
| Approach: | They propose a framework for constructing instruction-tuning data from unlabeled data for any specialized domains from corresponding unlabed data. |
| Outcome: | The proposed framework is comparable to DeepSeek-V3 while utilizing just 17% of the production cost. |
Ruler: A Model-Agnostic Method to Control Generated Length for Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models struggle to meet user’s needs when required to generate responses of a specific length due to their inherent difficulty in accurately perceiving numerical constraints. |
| Approach: | They propose a Target Length Generation Task and propose RULER, a model-agnostic approach that controls generated length for large language models. |
| Outcome: | The proposed model-agnostic approach improves instruction-following ability of large language models under length-constrained instructions and can generate appropriate MLT when length constraints are not explicitly provided. |
Decoder-Only LLMs can be Masked Auto-Encoders (2025.acl-short)
Copied to clipboard
Dan Qiao, Yuan Gao, Zheming Yang, Di Yang, Ziheng Wu, Pengcheng Lu, Minghui Qiu, Juntao Li, Min Zhang
| Challenge: | Modern NLP workflows require different models for generation and embedding tasks. |
| Approach: | They propose a method that transforms an LLM into a Uni-Directional Masked Auto-Encoder. |
| Outcome: | The proposed method achieves state-of-the-art under unsupervised conditions with merely 100 training steps. |
Language Anisotropic Cross-Lingual Model Editing (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing work studies monolingual model editing, which lacks cross-lingual transferability to perform editing simultaneously across languages. |
| Approach: | They propose a framework to naturally adapt monolingual model editing approaches to the cross-lingual scenario using parallel corpus. |
| Outcome: | The proposed framework adapts monolingual model editing approaches to the cross-lingual scenario using parallel corpus and amplifies different subsets of parameters for each language. |
Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures inside Arguments (2022.coling-1)
Copied to clipboard
| Challenge: | Recent works of SRL mainly fall into two lines: 1) BIO-based; 2) span-based. |
| Approach: | They propose to regard flat argument spans as latent subtrees, thus reducing SRL to a tree parsing task. |
| Outcome: | The proposed model performs better than previous syntax-agnostic models on CoNLL05 and CoNll12 benchmarks. |
Dancing in Chains: Reconciling Instruction Following and Faithfulness in Language Models (2024.emnlp-main)
Copied to clipboard
Zhengxuan Wu, Yuhao Zhang, Peng Qi, Yumo Xu, Rujun Han, Yian Zhang, Jifan Chen, Bonan Min, Zhiheng Huang
| Challenge: | Modern language models fail to follow human instructions while being faithful . a trade-off exists between instruction following and faithfulness when training LMs . |
| Approach: | They propose a method that relies on Reject-sampling by Self-instruct with Continued Fine-tuning to train LMs to follow human instructions while being faithful. |
| Outcome: | The proposed method outperforms vanilla MTL with high-quality data, but with significantly smaller data. |
Context Consistency between Training and Inference in Simultaneous Machine Translation (2024.acl-long)
Copied to clipboard
| Challenge: | Simultaneous machine translation (SiMT) aims to yield a partial translation with a monotonically growing source-side context. |
| Approach: | They propose a training approach that encourages consistent context usage between training and inference by optimizing translation quality and latency as bi-objectives and exposing the predictions to the model during the training. |
| Outcome: | The proposed system outperforms existing SiMT systems with context inconsistency for the first time. |
CaDRL: Document-level Relation Extraction via Context-aware Differentiable Rule Learning (2025.coling-main)
Copied to clipboard
Kunli Zhang, Pengcheng Wu, Bohan Yu, Kejun Wu, Aoze Zheng, Xiyang Huang, Chenkang Zhu, Min Peng, Hongying Zan, Yu Song
| Challenge: | Existing methods for document-level relation extraction (DocRE) lack logic and transparency. |
| Approach: | They propose a Context-aware differentiable rule learning framework that learns the doc-specific logical rule to avoid suboptimal constraints. |
| Outcome: | The proposed framework outperforms existing rule-based frameworks on three DocRE datasets. |
A Training-free LLM-based Approach to General Chinese Character Error Correction (2025.acl-long)
Copied to clipboard
| Challenge: | Chinese spelling correction (CSC) is a crucial task that aims to correct character errors in text. |
| Approach: | They propose a task that handles missing and redundant characters and an additional prompt-based large language model to improve performance. |
| Outcome: | The proposed task is based on a high-quality dataset and a prompt-based large language model. |
MultiSkill: Evaluating Large Multimodal Models for Fine-grained Alignment Skills (2024.findings-emnlp)
Copied to clipboard
| 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. |
Speaker-Aware Discourse Parsing on Multi-Party Dialogues (2022.coling-1)
Copied to clipboard
| Challenge: | Discourse parsing on multi-party dialogues is an important but difficult task in dialogue systems and conversational analysis. |
| Approach: | They propose a speaker-aware model for parsing on multi-party dialogues using interaction features between different speakers. |
| Outcome: | The proposed model achieves the best-reported performance on two standard benchmark datasets. |
CopyNE: Better Contextual ASR by Copying Named Entities (2024.acl-long)
Copied to clipboard
| 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. |
Learning to Extract Rational Evidence via Reinforcement Learning for Retrieval-Augmented Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | Retrieval-Augmented Generation (RAG) is effective in Large Language Models (LLMs). However, retrieval noises undermine the quality of LLMs’ generation, necessitating the development of denoising mechanisms. |
| Approach: | They propose a model which integrates reasoning and extracting into one unified trajectory, followed by knowledge token masking to avoid information leakage. |
| Outcome: | Extensive experiments on five benchmark datasets show the superiority of EviOmni, which provides compact and high-quality evidence, enhances the accuracy of downstream tasks, and supports both traditional and agentic RAG systems. |
Filter-then-Generate: Large Language Models with Structure-Text Adapter for Knowledge Graph Completion (2025.coling-main)
Copied to clipboard
| Challenge: | Empirical evidence suggests that LLMs perform worse than conventional KGC approaches. |
| Approach: | They propose a filter-then-generate paradigm and a multiple-choice question format to harness the capability of LLMs while mitigating the issue casused by hallucinations. |
| Outcome: | The proposed method achieves substantial performance gain compared to existing state-of-the-art methods. |
MoZIP: A Multilingual Benchmark to Evaluate Large Language Models in Intellectual Property (2024.lrec-main)
Copied to clipboard
Shiwen Ni, Minghuan Tan, Yuelin Bai, Fuqiang Niu, Min Yang, Bowen Zhang, Ruifeng Xu, Xiaojun Chen, Chengming Li, Xiping Hu
| Challenge: | Large language models (LLMs) have demonstrated impressive performance in various natural language processing tasks. |
| Approach: | They propose a benchmark for the evaluation of large language models in the IP domain . they also propose supervised multilingual large language model called MoZi . |
| Outcome: | The proposed model outperforms four well-known LLMs on the MoZIP benchmark . the most powerful ChatGPT does not reach the passing level . |
InImageTrans: Multimodal LLM-based Text Image Machine Translation (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing multimodal large language models suffer from repetition and omission hallucinations when transferred to text image machine translation task. |
| Approach: | They propose an efficient MLLM named InImageTrans for TiMT and a method for advancing it. |
| Outcome: | The proposed method outperforms existing open-source MLLMs on the MCiT benchmark. |
Stance Detection on Social Media with Background Knowledge (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies of stance detection focus on learning stance information about specific targets from context, but in real-world scenarios, we usually have a certain understanding of a target when we express our stance on it. |
| Approach: | They propose to take the background knowledge of the target into account for better stance detection by categorizing it into episodic and discourse knowledge categories and a heuristic retrieval algorithm based on the topic to retrieve the Wikipedia documents relevant to the sample. |
| Outcome: | The proposed framework achieves state-of-the-art on four benchmark datasets showing that the proposed framework is able to detect stances in-target and zero-shot scenarios. |
Multi-Document Event Extraction Using Large and Small Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to multi-document event extraction have limited attention . despite its practical significance, this task has inherent challenges . |
| Approach: | They propose a collaborative framework that integrates large language models for multi-step reasoning and fine-tuned small language models to handle key subtasks. |
| Outcome: | The proposed framework outperforms existing methods and provides new insights into collaborative reasoning to tackle the complexities of multi-document event extraction. |
DynamicKV: Task-Aware Adaptive KV Cache Compression for Long Context LLMs (2025.findings-emnlp)
Copied to clipboard
| 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)
Copied to clipboard
| 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 . |
Taming Actor-Observer Asymmetry in Agents via Dialectical Alignment (2026.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have enabled the development of powerful autonomous systems. |
| Approach: | They propose a model trained through dialectical alignment to enforce perspective-invariant reasoning. |
| Outcome: | The proposed model mitigates attribution inconsistency and significantly improves fault resolution rates in ambiguous scenarios. |
Contrastive Learning on LLM Back Generation Treebank for Cross-domain Constituency Parsing (2025.acl-long)
Copied to clipboard
| Challenge: | Existing constituency treebanks are limited in out-of-domain settings, therefore constituency parsing is still a challenge. |
| Approach: | They propose a novel method for constituency parsing using large language models . they use a cross-domain constituency treebank to fill missing words with the incomplete one . |
| Outcome: | The proposed method achieves state-of-the-art performance on average compared with baselines on five target domains of MCTB. |
Isotropy-Enhanced Conditional Masked Language Models (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing non-autoregressive models with auto-regressing decoding paradigms have been used for various text generation tasks to accelerate inference but at the cost of generation quality to some extent. |
| Approach: | They propose to use Look Neighbors strategy to enhance learning of target token representations during training to achieve a good balance between inference speedup and generation quality. |
| Outcome: | The proposed models outperform current models on 4 WMT datasets and outperformed the current SoTA results. |
COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning (2025.findings-naacl)
Copied to clipboard
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. |
Interactively-Propagative Attention Learning for Implicit Discourse Relation Recognition (2020.coling-main)
Copied to clipboard
| Challenge: | Existing models for discourse relation recognition use self-attention and interactive-attention mechanisms. |
| Approach: | They develop a propagative attention learning model using a cross-coupled two-channel network. |
| Outcome: | The proposed model improves on the baseline models on a Penn Discourse Treebank. |
VideoVista-CulturalLingo: 360° Horizons-Bridging Cultures, Languages, and Domains in Video Comprehension (2025.acl-long)
Copied to clipboard
| 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)
Copied to clipboard
| 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)
Copied to clipboard
| 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)
Copied to clipboard
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)
Copied to clipboard
| 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. |
CoTJudger: A Graph-Driven Framework for Automatic Evaluation of Chain-of-Thought Efficiency and Redundancy in LRMs (2026.findings-acl)
Copied to clipboard
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. |
LLMs Can Also Do Well! Breaking Barriers in Semantic Role Labeling via Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Semantic role labeling (SRL) is a crucial task of natural language processing (NLP). |
| Approach: | They propose to equip LLMs with retrieval-augmented generation and self-correction mechanisms to enable SRL to perform better in Chinese and English. |
| Outcome: | The proposed method achieves state-of-the-art in Chinese and English on three widely-used benchmarks. |
G-SPEED: General SParse Efficient Editing MoDel (2023.findings-emnlp)
Copied to clipboard
| 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. |
Efficient Sparse Attention needs Adaptive Token Release (2024.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide array of text-centric tasks, however, their ‘large’ scale introduces significant computational and storage challenges, particularly in managing the key-value states of the transformer, which limits their wider applicability. |
| Approach: | They propose to release resources from caches and rebuild key-value states by a lightweight controller module to approximate an ideal top-K sparse attention. |
| Outcome: | The proposed method achieves a significant throughput improvement of 221.8% over full attention and a model with 7 billion tokens. |
XLPT-AMR: Cross-Lingual Pre-Training via Multi-Task Learning for Zero-Shot AMR Parsing and Text Generation (2021.acl-long)
Copied to clipboard
| Challenge: | Abstract Meaning Representation (AMR) research is limited and challenging for languages other than English. |
| Approach: | They propose a cross-lingual pre-training approach for AMR parsing and text generation . they use an English-to-English parallel dataset and a multi-task learning approach . |
| Outcome: | The proposed approach outperforms baseline pre-training methods on English parsing and text generation tasks. |
Towards Making the Most of ChatGPT for Machine Translation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Prior studies have shown that ChatGPT achieves comparable results to commercial systems for high-resource languages, but lags behind in complex tasks, e.g., low-resourced and distant-language-pairs translation. |
| Approach: | They propose task-specific prompts and domain-specific prompts which are based on task information and domain information and a task-specific prompt. |
| Outcome: | The proposed prompts improve the performance of ChatGPT in complex tasks and generate hallucinations for non-English-centric tasks. |
LIME: Less Is More for MLLM Evaluation (2025.findings-acl)
Copied to clipboard
King Zhu, Qianbo Zang, Shian Jia, Siwei Wu, Feiteng Fang, Yizhi Li, Shuyue Guo, Tianyu Zheng, Jiawei Guo, Bo Li, Haoning Wu, Xingwei Qu, Jian Yang, Ruibo Liu, Xiang Yue, Jiaheng Liu, Chenghua Lin, Hamid Alinejad-Rokny, Min Yang, Shiwen Ni, Wenhao Huang, Ge Zhang
| Challenge: | Existing MLLM benchmarks and unified evaluation frameworks cannot accurately and efficiently reflect the ability of MLMLs. |
| Approach: | They propose a semi-automated benchmark curated using a pipeline that filters out uninformative samples and eliminates answer leakage by focusing on tasks that require image-based understanding. |
| Outcome: | The proposed benchmark reduces the number of samples by 76% and evaluation time by 77% while it can more effectively distinguish different models’ abilities. |
Step-GRPO: Internalizing Dynamic Early Exit for Efficient Reasoning (2026.acl-long)
Copied to clipboard
| 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 . |
QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for reducing LLM hallucinations rely on model-internal signals . Existing approaches rely only on model internal signals, resulting in unreliability . |
| Approach: | They propose a method that shifts from subjective confidence to objective statistics . they leverage Infini-gram for millisecond-latency queries over 4 trillion tokens . |
| Outcome: | The proposed method reduces hallucinations in large language models by reducing uncertainty in the model. |
The Digital Dunning-Kruger Effect: Decoupling Hallucinations via Geometric Hidden-state Observation for Semantic Truthfulness (2026.acl-long)
Copied to clipboard
Yueheng Mao, Min Yu, Gengwang Li, Jianguo Jiang, Gang Li, Meng Zhang, Zhen Xu, Weiqing Huang, Ming Liu
| Challenge: | Large Language Models (LLMs) often generate overconfident yet factually incorrect hallucinations. |
| Approach: | They propose a black-box-based framework that captures stubborn hallucinations by integrating internal geometric dynamics with output probability distributions. |
| Outcome: | The proposed framework outperforms white-box methods and reduces computational overhead by over 90%. |
Generating Visual Spatial Description via Holistic 3D Scene Understanding (2023.acl-long)
Copied to clipboard
| Challenge: | Existing VSD work focuses on skewed spatial understanding of target objects . Existing work merely models the 2D geometrical vision features . |
| Approach: | They propose to incorporate 3D scene features into visual spatial description tasks by sampling topologically-diverse subgraphs from Go3D-S2G. |
| Outcome: | The proposed framework outperforms baselines on two VSD datasets and produces more spatially-diversified generation. |
Escaping the Echo Trap: On Credit Assignment Failure in Multi-turn LLM Self-Reflection (2026.acl-long)
Copied to clipboard
Linxuan Du, Guangquan Xue, Xiaobo Liang, Qipeng Huang, Yuyang Ding, Xinyu Shi, Zhang Yijun, Ji Qi, Wenpeng Zhu, Juntao Li, Min Zhang
| Challenge: | Existing methods for multi-turn self-reflection are limited by the Echo Trap problem . the model is limited by its inherent capabilities and repeats earlier reflections to preserve reward signals . |
| Approach: | They propose a tree-structured extension of GRPO for multi-turn self-reflection which enables more accurate advantage estimation. |
| Outcome: | The proposed method mitigates behavior collapse and improves performance across benchmarks. |
Speed Up Your Code: Progressive Code Acceleration Through Bidirectional Tree Editing (2025.acl-long)
Copied to clipboard
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)
Copied to clipboard
| 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. |
Benchmarking LLMs for Translating Classical Chinese Poetry: Evaluating Adequacy, Fluency, and Elegance (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing large language models fall short of translating culturally significant content . existing models fall behind in achieving such translations, authors say . |
| Approach: | They propose a suitable benchmark for translating classical Chinese poetry into English . they propose RAT, a retrieval-augmented machine translation method that enhances the translation process . |
| Outcome: | The proposed method improves translation quality in terms of adequate, fluent, and elegant translations. |
A Unified Agentic Framework for Evaluating Conditional Image Generation (2025.acl-long)
Copied to clipboard
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. |
Test-time Adaptation for Machine Translation Evaluation by Uncertainty Minimization (2023.acl-long)
Copied to clipboard
| Challenge: | evaluators of machine translation systems often use text-based metrics to evaluate performance . however, these metrics lack semantic-level information and exhibit poor correlation with human ratings . authors propose a method to reduce inference bias of neural metrics in out-of-distribution data . |
| Approach: | They propose to reduce inference bias by using uncertainty estimation, test-time adaptation, and inference to reduce model uncertainty. |
| Outcome: | The proposed method reduces model uncertainty and improves correlation performance across models. |
BatonVoice: An Operationalist Framework for Enhancing Controllable Speech Synthesis with Linguistic Intelligence from LLMs (2026.acl-long)
Copied to clipboard
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. |
Safety Alignment via Constrained Knowledge Unlearning (2025.acl-long)
Copied to clipboard
Zesheng Shi, Yucheng Zhou, Jing Li, Yuxin Jin, Yu Li, Daojing He, Fangming Liu, Saleh Alharbi, Jun Yu, Min Zhang
| Challenge: | Existing defense mechanisms have not fully deleted harmful knowledge in large language models (LLMs) Existing methods to address safety alignment have not completely deleted harmful information in LLMs. |
| Approach: | They propose a safety alignment strategy that uses scoring neurons to identify useful knowledge in LLMs and pruning the gradients of neurons in U to preserve beneficial information. |
| Outcome: | The proposed method significantly improves model safety while maintaining utility compared to existing methods. |
E3-TIR: Enhanced Experience Exploitation for Tool-Integrated Reasoning (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing training paradigms for Large Language Models (LLMs) suffer from inefficient exploration and mode degradation due to a lack of prior guidance, while SFT-then-RL is limited by high data costs and capability plateaus caused by low-entropy collapse. |
| Approach: | They propose an Enhanced Experience Exploitation paradigm that integrates expert prefixes, expert guided, and self-exploration to improve agent training. |
| Outcome: | The proposed model achieves a 6% performance improvement over traditional paradigms on tool-use tasks while requiring less than 10% of the synthetic data. |
Enhancing Cross-target Stance Detection with Transferable Semantic-Emotion Knowledge (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for stance detection are struggling to cope with the data across targets. |
| Approach: | They propose a model that uses external knowledge as a bridge to enable knowledge transfer across different targets. |
| Outcome: | The proposed model outperforms existing methods on a large real-world dataset. |
Less Languages, Less Tokens: An Efficient Unified Logic Cross-lingual Chain-of-Thought Reasoning Framework (2026.acl-long)
Copied to clipboard
Chenyuan Zhang, Qiguang Chen, Xie Chen, Zhuotao Tian, Bowen Xing, Meishan Zhang, Libo Qin, Baotian Hu, Min Zhang
| Challenge: | Existing methods for cross-lingual chain-of-thought (XCoT) with self-consistency are costly due to extensive sampling of full trajectories across languages. |
| Approach: | They propose a cross-lingual chain-of-thought framework that minimizes redundancy in token usage and latency. |
| Outcome: | Experiments on polymath show that UL-XCoT reduces decoding token costs and latency by 50% . UL XCot also aggregates remaining high-quality reasoning paths via voting . |
Exploring and Mitigating Shortcut Learning for Generative Large Language Models (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent large language models (LLMs) have incredible instruction-following capabilities while maintaining strong task completion ability. |
| Approach: | They propose a framework to encourage LLMs to Forget Spurious correlations and Learn from In-context information. |
| Outcome: | The proposed framework can mitigate shortcut learning by forging spurious correlations and learning from in-context information. |
RST Discourse Parsing with Second-Stage EDU-Level Pre-training (2022.acl-long)
Copied to clipboard
| Challenge: | Existing pre-trained language models (PLMs) are based on sentence-level pre-training, which is different from the basic processing unit, i.e. element discourse unit (EDU). |
| Approach: | They propose a second-stage EDU-level pre-training approach to learn effective EDU representations continually based on well pre-trained language models. |
| Outcome: | The proposed method improves F1 score by 2.1 points on a benckmark dataset. |
A Neural Divide-and-Conquer Reasoning Framework for Image Retrieval from Linguistically Complex Text (2023.acl-long)
Copied to clipboard
| Challenge: | Pretrained Vision-Language Models (VLMs) have achieved remarkable performance in image retrieval from text, but their performance drops drastically when confronted with linguistically complex texts. |
| Approach: | They propose an end-to-end Neural Divide-and-Conquer Reasoning framework for linguistically complex texts that they struggle to comprehend. |
| Outcome: | The proposed framework significantly improves performance in complex image-text reasoning problem. |
Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction (2020.coling-main)
Copied to clipboard
| 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. |
Facilitating Fine-grained Detection of Chinese Toxic Language: Hierarchical Taxonomy, Resources, and Benchmarks (2023.acl-long)
Copied to clipboard
| Challenge: | Existing datasets suffer from a lack of fine-grained annotations, such as the toxic type and expressions with indirect toxicity. |
| Approach: | They propose a benchmark model to detect toxic language by incorporating lexical features into a Chinese dataset to facilitate fine-grained annotations. |
| Outcome: | The proposed model is based on insulting vocabulary containing implicit profanity and is able to detect toxic language with lexical features. |
Towards Text-Image Interleaved Retrieval (2025.acl-long)
Copied to clipboard
Xin Zhang, Ziqi Dai, Yongqi Li, Yanzhao Zhang, Dingkun Long, Pengjun Xie, Meishan Zhang, Jun Yu, Wenjie Li, Min Zhang
| Challenge: | Existing multimodal information retrieval models rely on single-image inputs . current models use a dense retrieval paradigm, but this approach is not effective . |
| Approach: | They propose a text-image interleaved retrieval task where query and document are interleaves . they adapt off-the-shelf retrievers and build a dense baseline by interleaded multimodal large language model . |
| Outcome: | The proposed model achieves significant improvements over the baseline by substantially fewer visual tokens. |
Enhancing EEG-to-Text Decoding through Transferable Representations from Pre-trained Contrastive EEG-Text Masked Autoencoder (2024.acl-long)
Copied to clipboard
| 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. |
Chinese Spoken Named Entity Recognition in Real-world Scenarios: Dataset and Approaches (2024.findings-acl)
Copied to clipboard
| Challenge: | Current Chinese Spoken NER datasets are laboratory-controlled and are limited in topics. |
| Approach: | They propose to use Chinese Spoken NER datasets to extract entities from speech to help voice assistants better grasp the intent behind user's questions and instructions. |
| Outcome: | The proposed methods improve on self-training-asr and mapping then distilling, and even compared with GPT4.0, they achieve better results. |
LLM-enhanced Self-training for Cross-domain Constituency Parsing (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to self-training rely on limited and potentially low-quality raw corpora. |
| Approach: | They propose to enhance self-training with the large language model to generate domain-specific raw corpora iteratively and introduce grammar rules that guide the LLM in generating raw corporeals and establish criteria for selecting pseudo instances. |
| Outcome: | The proposed method outperforms traditional methods regardless of the large language model's performance. |
Confusion is the Final Barrier: Rethinking Jailbreak Evaluation and Investigating the Real Misuse Threat of LLMs (2025.findings-emnlp)
Copied to clipboard
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 . |
Locate-and-Focus: Enhancing Terminology Translation in Speech Language Models (2025.acl-long)
Copied to clipboard
Suhang Wu, Jialong Tang, Chengyi Yang, Pei Zhang, Baosong Yang, Junhui Li, Junfeng Yao, Min Zhang, Jinsong Su
| Challenge: | Existing methods for terminology translation struggle with interference from irrelevant noise. |
| Approach: | They propose a Locate-and-Focus method that locates terminologies within utterances to construct translation knowledge by minimizing irrelevant information for ST models. |
| Outcome: | The proposed method locates terminologies within utterances and enhances the success rate of terminology translation while maintaining robust general translation performance. |
Empower Nested Boolean Logic via Self-Supervised Curriculum Learning (2023.emnlp-main)
Copied to clipboard
| Challenge: | a new self-supervised learning method allows language models to generalize to much harder and longer-hop logic. |
| Approach: | They propose a self-supervised learning method to empower language models with boolean logic . they augment training data with nested booles and program training from simpler to harder ones . |
| Outcome: | The proposed method allows language models to generalize to much harder and longer-hop logic, which can hardly be learned through naive training. |
Co-training and Co-distillation for Quality Improvement and Compression of Language Models (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Knowledge Distillation (KD) compresses expensive pre-trained language models . however, most smaller models fail to surpass performance of larger model . |
| Approach: | They propose a framework that co-trains two models while mutually distilling knowledge to improve performance and inference speed together. |
| Outcome: | The proposed framework outperforms the original larger model by 1.66 on the GLUE benchmark. |
Memory-augmented Query Reconstruction for LLM-based Knowledge Graph Reasoning (2025.findings-acl)
Copied to clipboard
| 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. |
Scaling Law for Document Neural Machine Translation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Neural machine translation (NMT) methods fail to capture discourse phenomena such as pronominal anaphora, lexical consistency, and document coherence as the input text exceeds a single sentence. |
| Approach: | They examine the effects of model scale, data scale, and sequence length on translation quality when model size is limited. |
| Outcome: | The proposed model scales and data scales are compared with the existing models and show that increasing sequence length improves translation quality when model size is limited. |
Open-ended Long Text Generation via Masked Language Modeling (2023.acl-long)
Copied to clipboard
| Challenge: | Pre-trained autoregressive language models have dominated OPen-ended Long Text Generation (Open-LTG) however, the low inference efficiency of AR impedes their usability. |
| Approach: | They propose a representative iterative non-autoregressive (NAR) decoding strategy to improve inference efficiency for Open-LTG. |
| Outcome: | The proposed model can generate short text and collapse for long text modeling. |
Efficient Table Retrieval and Understanding with Multimodal Large Language Models (2026.findings-eacl)
Copied to clipboard
| 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)
Copied to clipboard
| 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)
Copied to clipboard
| 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. |