Papers by Min Li
Team-Based Self-Play With Dual Adaptive Weighting for Fine-Tuning LLMs (2026.acl-long)
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| Challenge: | Recent self-training approaches have reduced reliance on human-labeled data, which limits their scalability. |
| Approach: | They propose a team-based self-play algorithm that iteratively refines alignment without additional human supervision. |
| Outcome: | The proposed algorithm outperforms baselines and LLM benchmarks in the self-supervised setting. |
Increasing Coverage and Precision of Textual Information in Multilingual Knowledge Graphs (2023.emnlp-main)
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| Challenge: | Existing methods to generate knowledge graphs are unable to handle non-English textual information. |
| Approach: | They propose a task of automatic Knowledge Graph Completion to bridge the gap between English and non-English textual information. |
| Outcome: | The proposed method bridges the gap between the quantity and quality of textual information between English and non-English languages. |
DiffuSpec: Unlocking Diffusion Language Models for Speculative Decoding (2026.findings-acl)
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| Challenge: | Autoregressive (AR) decoding in large language models is latency-bounded by strictly sequential token generation. |
| Approach: | They propose a diffusion-based drafter that proposes multi-token candidates and then verifies them in parallel by the target model. |
| Outcome: | The proposed drafter generates multi-token proposals in a single forward pass while remaining compatible with standard AR verifiers. |
MTSA: Multi-turn Safety Alignment for LLMs through Multi-round Red-teaming (2025.acl-long)
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| Challenge: | Existing jailbreak techniques rely on single-round interactions, pro-Corresponding author. |
| Approach: | They propose a multi-turn safety alignment framework to address the challenge of securing large language models in multi-round interactions. |
| Outcome: | The proposed framework exhibits state-of-the-art attack capabilities while improving safety performance on safety benchmarks. |
When to Trust Tools? Adaptive Tool Trust Calibration For Tool-Integrated Math Reasoning (2026.findings-acl)
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| 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%. |
OmniCharacter: Towards Immersive Role-Playing Agents with Seamless Speech-Language Personality Interaction (2025.acl-long)
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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. |
SumSurvey: An Abstractive Dataset of Scientific Survey Papers for Long Document Summarization (2024.findings-acl)
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| 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. |
Learning Context-Sensitive Convolutional Filters for Text Processing (D18-1)
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| Challenge: | Convolutional neural networks (CNNs) are a popular building block for natural language processing . despite their success, most existing CNN models share the same learned set of filters for all input sentences. |
| Approach: | They propose to use a meta network to learn context-sensitive convolutional filters for text processing by using a bidirectional filter generation mechanism. |
| Outcome: | The proposed framework outperforms standard and attention-based CNN models on four different tasks. |
An In-depth Study on Internal Structure of Chinese Words (2021.acl-long)
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Chen Gong, Saihao Huang, Houquan Zhou, Zhenghua Li, Min Zhang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan
| Challenge: | Unlike English letters, Chinese characters have rich and specific meanings. |
| Approach: | They propose to model Chinese words' internal structures as dependency trees with 11 labels for distinguishing syntactic relationships. |
| Outcome: | The proposed model of Chinese word-internal structures shows it can be used to parse sentences . it shows that the model can be applied to a sentence-level task with a competitive dependency parser. |
JointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection (2022.acl-long)
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| Challenge: | Existing methods achieve promising performance in in-target stance detection when trained and tested on the same datasets. |
| Approach: | They propose a joint contrastive learning framework to generalize stance features for unseen targets. |
| Outcome: | The proposed framework achieves state-of-the-art on three benchmark datasets. |
Achieving Stronger Generation via Simple Contrastive Tuning (2024.findings-emnlp)
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| Challenge: | Recent years have witnessed remarkable progress in large language models (LLMs). |
| Approach: | They propose a framework for contrastive decoding to enhance instruction-tuned models. |
| Outcome: | The proposed framework improves model performance without additional data or computational resources. |
WikiDiverse: A Multimodal Entity Linking Dataset with Diversified Contextual Topics and Entity Types (2022.acl-long)
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| Challenge: | Multimodal Entity Linking (MEL) is an essential task for many multimodal applications. |
| Approach: | They propose to use a human-annotated Wikipedia-based multimodal entity linking dataset to improve the quality of existing MEL models. |
| Outcome: | The proposed model uses the visual information of images more effectively than existing models. |
Towards Cross-Cultural Machine Translation with Retrieval-Augmented Generation from Multilingual Knowledge Graphs (2024.emnlp-main)
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| Challenge: | XC-Translate is a large-scale, manually-created benchmark for machine translation . current systems struggle to translate texts containing entity names, but KG-MT outperforms state-of-the-art approaches . |
| Approach: | They propose a method to integrate multilingual knowledge into a neural machine translation model . XC-Translate is the first large-scale, manually-created benchmark for machine translation . they propose KG-MT to integrate cultural-related references into MT models . |
| Outcome: | The proposed method outperforms state-of-the-art approaches by a large margin compared to NLLB-200 and GPT-4 . the proposed method is based on a multilingual knowledge graph and dense retrieval mechanism . |
Towards Better Hierarchical Text Classification with Data Generation (2023.findings-acl)
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| Challenge: | Existing methods to improve hierarchical text classification are expensive and lack high-quality labeled data. |
| Approach: | They propose a hierarchical text classification framework that can achieve both label controllability and text diversity by extracting high-quality hierarchic label information. |
| Outcome: | The proposed method can achieve label controllability and text diversity by extracting high-quality hierarchical label information. |
Multimodal Reasoning with Multimodal Knowledge Graph (2024.acl-long)
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| Challenge: | Multimodal reasoning with large language models (LLMs) often suffers from hallucinations and the presence of deficient or outdated knowledge within LLMs. |
| Approach: | They propose a multimodal reasoning method that leverages multimodal knowledge graphs to learn rich and semantic knowledge across modalities. |
| Outcome: | The proposed method outperforms state-of-the-art models on multimodal question answering and multimodal analogy reasoning tasks while training on only a small fraction of parameters. |
Exploring the Impact of Personality Traits on LLM Toxicity and Bias (2025.emnlp-main)
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| Challenge: | anthropomorphic LLMs are being developed to serve diversified roles, but content safety concerns remain regarding their toxicity and toxicity. |
| Approach: | They propose to assign personality traits to large language models (LLMs) to reduce toxic language and social biases in their outputs by using the widely accepted HEXACO personality framework developed in social psychology. |
| Outcome: | The proposed model is able to perform on three toxic and bias benchmarks and shows that assigning personality traits reduces bias and toxicity similar to humans’ correlations between personality traits and toxic behaviors. |
Cognitive Visual-Language Mapper: Advancing Multimodal Comprehension with Enhanced Visual Knowledge Alignment (2024.acl-long)
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| 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. |
Retrieval, Analogy, and Composition: A framework for Compositional Generalization in Image Captioning (2021.findings-emnlp)
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| Challenge: | Existing approaches fail to generalize well to concepts that are not observed during training. |
| Approach: | They propose a framework that revolves around probing several similar image caption training instances and performing analogical reasoning over relevant entities in retrieved prototypes. |
| Outcome: | The proposed framework improves on the widely used image captioning benchmarks and on composition-related evaluation metrics. |
Encouraging Lexical Translation Consistency for Document-Level Neural Machine Translation (2021.emnlp-main)
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| 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)
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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)
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| 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)
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| 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)
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| Challenge: | Existing methods for fine-grained opinion mining (OM) are based on span-based annotations, but they are not effective. |
| Approach: | They propose a unified span-based approach for the end-to-end OM setting using syntactic constituents and multi-task learning to integrate them into the proposed model. |
| Outcome: | The proposed approach achieves significant improvements over previous work on the MPQA 2.0 dataset and reduces the number of wrongly-predicted opinion expressions and roles. |
Multi-modal Stance Detection: New Datasets and Model (2024.findings-acl)
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| Challenge: | Existing methods for stance detection for pure texts have limited results to multi-modal content. |
| Approach: | They propose a multi-modal stance detection framework that leverages target information to learn multi-modal stance features from textual and visual modalities. |
| Outcome: | The proposed framework achieves state-of-the-art in multi-modal stance detection on five datasets based on Twitter . |
CLHA: A Simple Yet Effective Contrastive Learning Framework for Human Alignment (2024.lrec-main)
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Feiteng Fang, Liang Zhu, Xi Feng, Jinchang Hou, Qixuan Zhao, Chengming Li, Xiping Hu, Ruifeng Xu, Min Yang
| Challenge: | Large language models (LLMs) have attracted considerable attention from academic and industrial communities due to their outstanding performance in various natural language processing tasks. |
| Approach: | They propose a Contrastive Learning Framework for Human Alignment to evaluate the noise within the data and dynamically adjust the training process. |
| Outcome: | The proposed framework surpasses other algorithms in terms of reward model scores, automatic evaluations, and human assessments on the widely used dataset "Helpful and Harmless" |
From Charts to Code: A Hierarchical Benchmark for Multimodal Models (2026.acl-long)
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Jiahao Tang, Henry Hengyuan Zhao, Lijian Wu, Zijian Zhang, Yifei Tao, Dongxing Mao, Yang Wan, Jingru Tan, Min Zeng, Min Li, Alex Jinpeng Wang
| Challenge: | Chart2Code is a new benchmark for evaluating the natural language to chart code generation capabilities of large multimodal models. |
| Approach: | They introduce Chart2Code, a new benchmark for evaluating the natural language to chart code generation capabilities of large multimodal models. |
| Outcome: | The proposed benchmark is the first to scale task complexity while capturing diverse scenarios. |
Chain of Thought with Explicit Evidence Reasoning for Few-shot Relation Extraction (2023.findings-emnlp)
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| 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)
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Yigeng Zhou, Wu Li, Yifan Lu, Jing Li, Fangming Liu, Meishan Zhang, Yequan Wang, Daojing He, Honghai Liu, Min Zhang
| Challenge: | Existing methods for supplementing Large Language Models (LLMs) with knowledge graphs often introduce noise in the retrieval and reasoning pipeline, hindering their ability to integrate external knowledge for complex multi-hop question answering. |
| Approach: | They propose a framework to enhance LLMs' reasoning capabilities through reflective engagement with knowledge graphs by Query Decoupling, LLM-Driven Knowledge Graph Exploration, and Inference with Knowledge Reconstruction. |
| Outcome: | The proposed framework integrates external knowledge into LLMs and trains them to leverage this knowledge for answering questions. |
Joint Multi-modal Aspect-Sentiment Analysis with Auxiliary Cross-modal Relation Detection (2021.emnlp-main)
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| 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. |
Efficient One-Pass End-to-End Entity Linking for Questions (2020.emnlp-main)
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| Challenge: | Existing models for entity linking are limited to entity disambiguation and require mention boundaries to be given in the input. |
| Approach: | They propose a fast end-to-end entity linking model that uses a biencoder to jointly detect mentions and link in one pass. |
| Outcome: | The proposed model outperforms the current state of the art on WebQSP and GraphQuestions with extended annotations that cover multiple entities per question. |
Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy (2025.coling-main)
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| 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. |
Rethinking Document-Level Relation Extraction: A Reality Check (2023.findings-acl)
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| Challenge: | Recent efforts push up performance boundaries of document-level relation extraction (DocRE) but these efforts are not promising. |
| Approach: | They construct four types of entity mention attacks to examine model robustness . they also have a close check on model usability in a more realistic setting . |
| Outcome: | The proposed model is based on a strong or untenable assumption in common . the model is robust under four types of mention attacks and usable in a realistic setting . |
Retrieval and Reasoning on KGs: Integrate Knowledge Graphs into Large Language Models for Complex Question Answering (2024.findings-emnlp)
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| 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)
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| 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. |
Efficient Continue Training of Temporal Language Model with Structural Information (2023.findings-emnlp)
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| 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. |
Towards Robust Neural Machine Translation with Iterative Scheduled Data-Switch Training (2022.coling-1)
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Zhongjian Miao, Xiang Li, Liyan Kang, Wen Zhang, Chulun Zhou, Yidong Chen, Bin Wang, Min Zhang, Jinsong Su
| Challenge: | Existing methods on robust neural machine translation (NMT) construct adversarial examples by injecting noise into authentic examples and indiscriminately exploit two types of examples. |
| Approach: | They propose an iterative scheduled data-switch training framework to mitigate this problem by injecting noise into authentic examples and indiscriminately exploiting two types of examples. |
| Outcome: | The proposed model outperforms several competitive benchmarks on four translation benchmarks. |
BERT-EMD: Many-to-Many Layer Mapping for BERT Compression with Earth Mover’s Distance (2020.emnlp-main)
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| Challenge: | Pre-trained language models have been proposed and applied to many NLP tasks, yielding state-of-the-art performance, but high storage and computational costs obstruct them to be effectively deployed on resource-constrained devices and real-time applications. |
| Approach: | They propose a BERT distillation method which allows each intermediate student layer to learn from any intermediate teacher layers. |
| Outcome: | The proposed method can learn from different teacher layers adaptively for different NLP tasks. |
mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval (2024.emnlp-industry)
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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)
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| 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. |
Multiple Knowledge-Enhanced Interactive Graph Network for Multimodal Conversational Emotion Recognition (2024.findings-emnlp)
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| Challenge: | Multimodal Emotion Recognition in Conversations models struggle due to lack of Common Sense Knowledge (CSK). |
| Approach: | They propose a multimodal approach to integrate multiple knowledge into the edge representations by integrating textual and visual CSK. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two popular datasets. |
IS-CoT: Breaking the Long-form Generation Collapse via Interleaved Structural Thinking (2026.acl-long)
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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. |
Forget for Get: A Lightweight Two-phase Gradient Method for Knowledge Editing in Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing knowledge editing methodologies often encounter parameter conflict during knowledge overwriting and excessive computational overhead. |
| Approach: | They propose a method that erases outdated knowledge and inserts new knowledge at the location that corresponds to the target knowledge. |
| Outcome: | The proposed method achieves more effective knowledge editing at a lower cost compared to previous methods across various base models. |
AgentCourt: Simulating Court with Adversarial Evolvable Lawyer Agents (2025.findings-acl)
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Guhong Chen, Liyang Fan, Zihan Gong, Nan Xie, Zixuan Li, Ziqiang Liu, Chengming Li, Qiang Qu, Hamid Alinejad-Rokny, Shiwen Ni, Min Yang
| Challenge: | Existing legal language models struggle with dynamic courtroom interactions, resulting in overfitting to standardized legal tasks. |
| Approach: | They propose a new adversarial evolutionary approach for agents that performs dynamic knowledge learning and evolution through structured adversarials in a simulated courtroom program. |
| Outcome: | The proposed approach outperforms existing LLM-based models in three critical dimensions: cognitive agility, professional knowledge, and logical rigor. |
ExplainCPE: A Free-text Explanation Benchmark of Chinese Pharmacist Examination (2023.findings-emnlp)
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| 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. |
ToolRM: Towards Agentic Tool-Use Reward Modeling (2026.findings-acl)
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Renhao Li, Jianhong Tu, Yang Su, Yantao Liu, Fei Huang, Hamid Alinejad-Rokny, Derek F. Wong, Junyang Lin, Min Yang
| Challenge: | lack of reliable reward models for tool-use tasks has limited progress toward agentic AI . recent advances in agentic artificial intelligence are driven by tool-using capabilities of large language models. |
| Approach: | They propose a pipeline that constructs pairwise preference data using rule-based scoring and multidimensional sampling to build lightweight reward models. |
| Outcome: | The proposed model outperforms existing models on tool calling tasks with higher accuracy. |
A Multimodal In-Context Tuning Approach for E-Commerce Product Description Generation (2024.lrec-main)
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| Challenge: | Existing methods for generating product descriptions from images are inaccurate and generic . e-commerce product descriptions are important for content marketing and increasing engagement . |
| Approach: | They propose a new setting for generating product descriptions from images, augmented by marketing keywords. |
| Outcome: | The proposed approach improves the accuracy and diversity of product descriptions by up to 3.3% on Rouge-L and 9.4% on D-5. |
Modeling Graph Structure in Transformer for Better AMR-to-Text Generation (D19-1)
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| 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)
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| 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. |
Data Interpreter: An LLM Agent for Data Science (2025.findings-acl)
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Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Robert Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Yongxin Ni, Zhibin Gou, Zongze Xu, Yuyu Luo, Chenglin Wu
| Challenge: | Large Language Models (LLMs) excel in various domains but face challenges when applied to data science workflows due to their complex, multi-stage nature. |
| Approach: | They propose a hierarchical graph-based agent that represents complexity and a progressive strategy for step-by-step verification, refinement, and consistent context management. |
| Outcome: | The proposed agent surpasses state-of-the-art baselines on the MATH dataset and performs better on InfiAgent-DABench. |
Distantly Supervised NER with Partial Annotation Learning and Reinforcement Learning (C18-1)
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| 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)
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| 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. |
CB-Whisper: Contextual Biasing Whisper Using Open-Vocabulary Keyword-Spotting (2024.lrec-main)
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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)
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| Challenge: | Current distractor generation methods produce shared distractors for all students, ignoring individual variations in reasoning, which limits their diagnostic effectiveness. |
| Approach: | They propose a method which tailors distractors to each student’s specific cognitive flaws, inferred from their past question-answering (QA) history. |
| Outcome: | The proposed framework outperforms existing methods in generating plausible distractors and adapts to group-level settings. |
GTA: Supervised-Guided Reinforcement Learning for Text Classification with Large Language Models (2025.findings-emnlp)
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| 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. |
Enhancing Speech Large Language Models with Prompt-Aware Mixture of Audio Encoders (2025.emnlp-main)
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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. |
Just Like a Human Would, Direct Access to Sarcasm Augmented with Potential Result and Reaction (2023.acl-long)
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| Challenge: | sarcasm is a form of irony conveying mockery and contempt . social media has become increasingly popular for identifying sarcasm . |
| Approach: | They develop a method to detect sarcasm from social media using augmented potentials. |
| Outcome: | The proposed method outperforms baselines on benchmark datasets. |
Early Exit with Disentangled Representation and Equiangular Tight Frame (2023.findings-acl)
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| Challenge: | Existing early exit paradigm relies on training parametrical internal classifiers to complete specific tasks. |
| Approach: | They propose a method to decouple two distinct types of representation and introduce a non-parametric tight frame classifier for improvement. |
| Outcome: | Experiments on monolingual and multilingual tasks show that the proposed method improves over existing methods. |
NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts (2023.findings-acl)
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| 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)
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Run Luo, Haonan Zhang, Longze Chen, Ting-En Lin, Xiong Liu, Yuchuan Wu, Min Yang, Yongbin Li, Minzheng Wang, Pengpeng Zeng, Lianli Gao, Heng Tao Shen, Yunshui Li, Hamid Alinejad-Rokny, Xiaobo Xia, Jingkuan Song, Fei Huang
| Challenge: | a new framework for image-text instruction data evolution improves MLLM performance . lack of high-quality instruction data remains a major bottleneck in ML modeling . |
| Approach: | They propose a multimodal instruction data evolution framework that iteratively enhances data quality through fine-grained perception, cognitive reasoning, and interaction evolution. |
| Outcome: | The proposed approach improves MLLM performance in nine vision-language tasks while using significantly less data. |
Fast and Accurate End-to-End Span-based Semantic Role Labeling as Word-based Graph Parsing (2022.coling-1)
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| 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)
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| 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. |
Amalgamating Knowledge from Two Teachers for Task-oriented Dialogue System with Adversarial Training (2020.emnlp-main)
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| Challenge: | Existing approaches to task-oriented dialogue systems require a large number of handcrafted features and labels. |
| Approach: | They propose a "Two-Teacher One-Student" learning framework for task-oriented dialogue . the framework amalgamates knowledge from two teacher networks and provides guidance . |
| Outcome: | The proposed framework outperforms baseline methods on two benchmark datasets . it can retrieve accurate KB entities and generate human-like responses simultaneously . |
TruthReader: Towards Trustworthy Document Assistant Chatbot with Reliable Attribution (2024.emnlp-demo)
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| 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. |
Calibrated Speculative Decoding: Frequency-Guided Candidate Selection for Efficient Inference (2026.acl-long)
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| Challenge: | Speculative decoding (SD) is a powerful and efficient way to accelerate autoregressive generation. |
| Approach: | They propose a training-free framework that recovers valid tokens discarded by standard verification . they use online correction memory and Semantic Consistency Gating to analyze rejections . |
| Outcome: | The proposed framework outperforms existing methods and achieves peak throughput speedup of 2.33x. |
Exploring Reversal Mathematical Reasoning Ability for Large Language Models (2024.findings-acl)
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| 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. |
Expanding before Inferring: Enhancing Factuality in Large Language Models through Premature Layers Interpolation (2025.emnlp-main)
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Dingwei Chen, Ziqiang Liu, Feiteng Fang, Chak Tou Leong, Shiwen Ni, Ahmadreza Argha, Hamid Alinejad-Rokny, Min Yang, Chengming Li
| Challenge: | Existing approaches to generating factually inconsistent outputs are resource-intensive. |
| Approach: | They propose a plug-and-play intervention designed to enhance factuality by inserting premature layers formed through mathematical interpolation with adjacent layers. |
| Outcome: | The proposed intervention reduces hallucinations while outperforming baselines on four datasets. |
Can Diffusion Model Achieve Better Performance in Text Generation ? Bridging the Gap between Training and Inference ! (2023.findings-acl)
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| Challenge: | Existing models for text generation use a discrete data embedding module to map the data into the continuous space. |
| Approach: | They propose two methods to bridge the gap between training and inference by mapping the discrete text into the continuous space. |
| Outcome: | The proposed methods can achieve 100 200 speedup with better performance on 6 generation tasks. |
Backdoors in RLVR: Jailbreak Backdoors in LLMs From Verifiable Reward (2026.acl-long)
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| 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)
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| 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. |
DISC: Plug-and-Play Decoding Intervention with Similarity of Characters for Chinese Spelling Check (2025.acl-long)
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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)
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| 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)
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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. |
STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing (2022.findings-emnlp)
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Zefeng Cai, Xiangyu Li, Binyuan Hui, Min Yang, Bowen Li, Binhua Li, Zheng Cao, Weijie Li, Fei Huang, Luo Si, Yongbin Li
| Challenge: | Extensive experiments show that STAR outperforms previous pre-training methods and ranks first on the leaderboard . text-to-SQL parsing aims to translate natural language (NL) questions into executable SQL queries . |
| Approach: | They propose a SQL guided pre-training framework STAR for context-dependent text-to-SQL parsing . they propose two objectives that explore context-dependence of NL utterances and SQL queries . |
| Outcome: | The proposed framework outperforms existing methods on two downstream benchmarks and ranks first on the leaderboard. |
Semantic Role Labeling from Chinese Speech via End-to-End Learning (2024.findings-acl)
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| 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. |
DUAL RM: Beyond Rule-based Preference Reward Modeling via Meta-Reward (2026.acl-long)
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Xiaobo Liang, Wanfu Wang, Qipeng Huang, Yuyang Ding, Zecheng Tang, Yixin Ji, Qianben Chen, Zhe Zhao, Kehai Chen, Juntao Li, Min Zhang
| Challenge: | Existing preference-based reward modeling methods face a recursive dependency where each verifier requires a meta-verifier, leading to continuous and costly dependence on human annotation. |
| Approach: | They propose a dual RM that couples discriminative and generative reward models under a non-parametric meta-reward. |
| Outcome: | The proposed model achieves strong performance across major preference benchmarks and even when trained exclusively on language modality, it exhibits robust cross-modal transfer on Omni-RewardBench. |
Unleashing LLM Reasoning Capability via Scalable Question Synthesis from Scratch (2025.acl-long)
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| 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. |
Crossing the Reward Bridge: Expanding Reinforcement Learning with Verifiable Rewards Across Diverse Domains (2026.acl-long)
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| 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)
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| Challenge: | In linguistics, all languages can be considered as symbolic systems . most work overlooks the properties of languages as symbol systems - aaron et al., 1989). |
| Approach: | They propose a method to make texts into linguistic symbols to improve multilingual capability . they use a pre-training method to replace pre-trained language models with a vocabulary map . |
| Outcome: | The proposed method improves multilingual capabilities on multilingual tasks using BERT and RoBERTa as the backbone. |
Geo-Encoder: A Chunk-Argument Bi-Encoder Framework for Chinese Geographic Re-Ranking (2024.eacl-long)
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| Challenge: | Chinese geographic re-ranking task aims to find the most relevant addresses among retrieved candidates. |
| Approach: | They propose a framework to integrate Chinese geographic semantics into re-ranking pipelines. |
| Outcome: | The proposed framework improves on two Chinese benchmark datasets. |
Explore More Guidance: A Task-aware Instruction Network for Sign Language Translation Enhanced with Data Augmentation (2022.findings-naacl)
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| Challenge: | Existing studies focus on the recognition step, while paying less attention to sign language translation. |
| Approach: | They propose a task-aware instruction network, namely TIN-SLT, for sign language translation, by introducing the isntruction module and the learning-based feature fuse strategy into a Transformer network. |
| Outcome: | The proposed system outperforms existing solutions on two benchmark datasets, PHOENIX-2014-T and ASLG-PC12, and outperformed previous best solutions by 1.65 and 1.42 in terms of BLEU-4. |
GUI-explorer: Autonomous Exploration and Mining of Transition-aware Knowledge for GUI Agent (2025.acl-long)
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| 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)
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| 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)
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| 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)
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| 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. |
DATA-CUBE: Data Curriculum for Instruction-based Sentence Representation Learning (2024.findings-acl)
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| Challenge: | Existing methods to improve sentence representation learning (SRL) ignore the potential interference problems across tasks and instances. |
| Approach: | They propose a multi-task instruction tuning method that arranges the order of multi- task data for training to minimize interference risks. |
| Outcome: | The proposed method can boost the performance of state-of-the-art methods. |
KG-TRICK: Unifying Textual and Relational Information Completion of Knowledge for Multilingual Knowledge Graphs (2025.coling-main)
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Zelin Zhou, Simone Conia, Daniel Lee, Min Li, Shenglei Huang, Umar Farooq Minhas, Saloni Potdar, Henry Xiao, Yunyao Li
| Challenge: | Existing studies have shown that combining information from KGs in different languages aids knowledge Graph Completion and Knowledge Graph Enhancement. |
| Approach: | They propose a sequence-to-sequence framework that unifies tasks of textual and relational information completion for multilingual knowledge graphs. |
| Outcome: | The proposed framework unifies tasks of KGC and KGE into a single framework. |
Layer-wise Regularized Dropout for Neural Language Models (2024.lrec-main)
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| Challenge: | Existing methods to regularize dropout are consistency training and dropout is a problem in many pre-trained neural language models. |
| Approach: | They propose a layer-wise regularized dropout technique which regularizes dropout at the output layer using consistency training. |
| Outcome: | The proposed model can be regarded as a "self-distillation" framework, in which each sub-model generated by dropout is the other's "teacher" model and "student" model. |
Act-Adaptive Margin: Dynamically Calibrating Reward Models for Subjective Ambiguity (2026.acl-long)
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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)
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Yi Su, Yuechi Zhou, Quantong Qiu, Juntao Li, Qingrong Xia, Ping Li, Xinyu Duan, Zhefeng Wang, Min Zhang
| Challenge: | Large Language Models (LLMs) require substantial computational resources during deployment. |
| Approach: | They propose a method to identify outlier tokens and exclude them from quantization . they find that the method can deliver a 6.4 times reduction in memory usage and a 2.5 times increase in throughput . |
| Outcome: | The proposed method delivers a 6.4 times reduction in memory usage and a 2.5 times increase in throughput under 2-bit quantization. |
Beyond the Last Frame: Process-aware Evaluation for Generative Video Reasoning (2026.acl-long)
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| Challenge: | Existing evaluation frameworks often rely on single-frame assessments, which can lead to outcome-hacking. |
| Approach: | They propose a process-aware evaluation paradigm that uses a hierarchical rubric to evaluate the validity of the intermediate steps and the final result. |
| Outcome: | The proposed model achieves POC@1.0 only about 20% and exhibits significant outcome-hacking. |
SrDetection: A Self-Referential Framework for Data Leakage Detection in Code Large Language Models (2026.findings-acl)
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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. |
Topic-Guided Self-Introduction Generation for Social Media Users (2023.findings-acl)
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| Challenge: | Existing studies on social media use tags to profile users, but we have found that sentence-level self-introductions are more natural and engaging. |
| Approach: | They propose a novel topic-guided encoder-decoder framework that uses a user's tweeting history to generate a short sentence outlining their personal interests. |
| Outcome: | The proposed framework outperforms existing encoder-decoder models on a large-scale Twitter dataset and shows that it is more natural and engaging than previous approaches. |
Tiny Budgets, Big Gains: Parameter Placement Strategy in Parameter Super-Efficient Fine-Tuning (2025.emnlp-main)
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| 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. |
Hallucinations as Orthogonal Noise: Inference-Time Manifold Alignment via Dynamic Contextual Orthogonalization (2026.findings-acl)
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Mingkuan Zhao, Wentao Hu, Tianchen Huang, Yuheng Min, Suquan Chen, Yide Gao, Yanbo Zhai, Shuangyong Song, Xuelong Li
| Challenge: | Hallucinations in Large Language Models persist in critical domains where generated content diverges from contextual facts or logical constraints. |
| Approach: | They propose to generate hallucinations as orthogonal noise relative to the semantic manifold of the residual stream. |
| Outcome: | The proposed method achieves superior contextual faithfulness compared to state-of-the-art methods. |
Weak-to-Strong Honesty Alignment via Learning-to-Rank Supervision (2025.findings-acl)
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| Challenge: | Existing approaches to enhance honesty with prompt engineering and fine-tuning are limited by annotated data. |
| Approach: | They propose a framework that enhances honesty through weak-to-strong generalization by training weak LLMs under weak supervision to improve their honesty. |
| Outcome: | The proposed framework improves honesty in large models even with limited label data. |
Efficient Reasoning for LLMs through Speculative Chain-of-Thought (2026.findings-acl)
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| Challenge: | Existing methods for efficient reasoning focus on reducing the number of model parameters or shortening the chain-of-thought length. |
| Approach: | They propose a speculative chain-of-thought (SCoT) method to reduce reasoning latency by accelerating average reasoning speed through large and small model collaboration. |
| Outcome: | The proposed method reduces reasoning latency by 48%66% and 21%49% on GSM8K, MATH, GaoKao, CollegeMath and Olympiad datasets. |
Adaptive Feature-based Low-Rank Compression of Large Language Models via Bayesian Optimization (2024.findings-emnlp)
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Yixin Ji, Yang Xiang, Juntao Li, Qingrong Xia, Zi Ye, Xinyu Duan, Zhefeng Wang, Kehai Chen, Min Zhang
| Challenge: | Large language models require a balance between efficiency and performance. |
| Approach: | They propose a low-rank compression technique that reduces non-essential parameters by decomposing weight matrices into products of two low-ranked matrici. |
| Outcome: | The proposed method outperforms existing pruning and low-rank compression techniques in maintaining model performance at the same compression ratio. |
When Is Thinking Enough? Early Exit via Sufficiency Assessment for Efficient Reasoning (2026.acl-long)
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| 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. |
CDT: A Comprehensive Capability Framework for Large Language Models Across Cognition, Domain, and Task (2025.findings-emnlp)
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| 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)
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| Challenge: | Recent work on incorporating syntactic knowledge into neural semantic role labeling has gained much attention . incorporating heterogeneous syntaktic knowledge brings significant improvements over strong baselines . |
| Approach: | They propose to encode heterogeneous syntactic knowledge for SRL from explicit and implicit representations from heterogenous treebanks. |
| Outcome: | The proposed approaches improve on two widely-used benchmark datasets. |
HFMRE: Constructing Huffman Tree in Bags to Find Excellent Instances for Distantly Supervised Relation Extraction (2023.findings-emnlp)
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| Challenge: | Existing approaches to extract sentence-level features are labor-intensive and time-consuming. |
| Approach: | They propose a distantly supervised relation extraction algorithm that uses circular cosine similarity to show intrinsic associations between sentences within a bag. |
| Outcome: | The proposed method outperforms baselines on the popular DSRE datasets. |
BrainECHO: Semantic Brain Signal Decoding through Vector-Quantized Spectrogram Reconstruction for Whisper-Enhanced Text Generation (2025.findings-acl)
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| Challenge: | Current EEG/MEG-to-text decoding systems rely on teacher-forcing methods . pre-trained large language models are over-dominant in decoding text from brain activity . |
| Approach: | They propose a framework that employs decoupled representation learning to achieve state-of-the-art performance on EEG and MEG datasets. |
| Outcome: | The proposed framework achieves state-of-the-art performance on EEG and MEG datasets. |
CPsyExam: A Chinese Benchmark for Evaluating Psychology using Examinations (2025.coling-main)
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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. |
HiMATE: A Hierarchical Multi-Agent Framework for Machine Translation Evaluation (2025.findings-emnlp)
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| Challenge: | Existing LLM-based evaluation methods fail to accurately identify error spans and assess their severity. |
| Approach: | They propose a Hierarchical Multi-Agent Framework for Machine Translation Evaluation based on the MQM error typology and a hierarchical multi-agent system enabling granular evaluation of subtype errors. |
| Outcome: | The proposed framework outperforms baselines in error span detection and severity assessment. |
Beyond Quantity: Trajectory Diversity Scaling for Code Agents (2026.findings-acl)
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Guhong Chen, Chenghao Sun, Cheng Fu, Qiyao Wang, Zhihong Huang, ChaoPeng Wei, Guangxu Chen, Feiteng Fang, Ahmadreza Argha, Bing Zhao, Xander Xu, Qi Han, Hamid Alinejad-Rokny, Qiang Qu, Binhua Li, Shiwen Ni, Min Yang, HU Wei, Yongbin Li
| Challenge: | Code large language models (LLMs) are becoming tool-interactive agents . quantity-centric scaling exhibits an early bottleneck that underutilizes trajectory data . et al.: a new approach to scale trajectory diversity improves tool-use generalization . |
| Approach: | They propose a Trajectory Diversity Scaling-based data synthesis framework for code agents that scales performance through diversity rather than raw volume. |
| Outcome: | Experiments on general tool-use benchmarks and code agent tasks show that TDScaling improves tool-user generalization and inherent coding proficiency. |
Forgetting before Learning: Utilizing Parametric Arithmetic for Knowledge Updating in Large Language Models (2024.acl-long)
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| Challenge: | Existing methods of model editing and knowledge updating add additional network parameters, knowledge bases, knowledge base, and model parameters. |
| Approach: | They propose a new paradigm for fine-tuning called F-Learning that employs parametric arithmetic to facilitate the forgetting of old knowledge and learning of new knowledge. |
| Outcome: | The proposed model outperforms existing models on two datasets and is comparable to full fine-tuning and LoRA fine-uning. |
Can LLMs Learn Uncertainty on Their Own? Expressing Uncertainty Effectively in A Self-Training Manner (2024.emnlp-main)
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| 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. |
PEToolLLM: Towards Personalized Tool Learning in Large Language Models (2025.findings-acl)
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| Challenge: | Existing tool learning studies focus on general-purpose tool-use capability, but ignore the importance of personalized tool-user preferences. |
| Approach: | They propose a framework to adapt Large Language Models to personalized tool learning task, which is trained through supervised fine-tuning and direct preference optimization. |
| Outcome: | Extensive experiments on PEToolBench show that the proposed framework outperforms existing LLMs in the personalized tool learning task. |
SelfMix: Robust Learning against Textual Label Noise with Self-Mixup Training (2022.coling-1)
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| 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. |
Towards IP Intelligence: Benchmarking Large Language Models on Intellectual Property Knowledge and Practice (2026.findings-acl)
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Qiyao Wang, Guhong Chen, Hongbo Wang, Huaren Liu, Minghui Zhu, Zhifei Qin, Li Linwei, Yilin Yue, Shiqiang Wang, Jiayan Li, Wu Yihang, Ziqiang Liu, Longze Chen, Run Luo, Liyang Fan, Jiaming Li, Lei Zhang, Kan Xu, Hamid Alinejad-Rokny, Chengming Li, Shiwen Ni, Yuan Lin, Min Yang
| Challenge: | Existing datasets and benchmarks focus only on patents or cover limited aspects of the IP field, lacking alignment with real-world scenarios. |
| Approach: | They propose a bilingual IP task taxonomy and a large-scale bilingual benchmark to evaluate LLMs in real-world IP practice. |
| Outcome: | The proposed model achieves only 75.8% accuracy, indicating room for improvement . open-source IP and law-oriented models lag behind closed-source general-purpose models . |
MLDebugging: Towards Benchmarking Code Debugging Across Multi-Library Scenarios (2025.findings-acl)
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JinYang Huang, Xiachong Feng, Qiguang Chen, Hanjie Zhao, Zihui Cheng, Jiesong Bai, Jingxuan Zhou, Min Li, Libo Qin
| Challenge: | MLDebugging is a benchmark designed to assess debugging challenges within multi-library Python code. |
| Approach: | They propose to introduce a benchmark to assess debugging challenges within multi-library Python code using 126 Python libraries. |
| Outcome: | The proposed benchmark covers 126 Python libraries and a wide range of multi-library code issues. |
Pay More Attention to Relation Exploration for Knowledge Base Question Answering (2023.findings-acl)
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| Challenge: | Existing approaches focus on entity representation and final answer reasoning, which results in limited supervision for this task. |
| Approach: | They propose a framework that utilizes relations to enhance entity representation and introduce additional supervision. |
| Outcome: | The proposed framework improves the F1 score on two benchmark datasets by 5.8% . it improves by 6.7% on WebQSP, better than state-of-the-art methods . |
Cross-Domain Audio Deepfake Detection: Dataset and Analysis (2024.emnlp-main)
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| 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)
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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)
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| 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. |
Alignment-Augmented Speculative Decoding with Alignment Sampling and Conditional Verification (2025.emnlp-main)
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| Challenge: | Existing methods to accelerate autoregressive generation of large language models require training costs. |
| Approach: | They propose a training-free alignment-augmented speculative decoding algorithm . it leverages the output distribution obtained in the prefilling phase to provide more aligned draft candidates . |
| Outcome: | The proposed method increases the average generation score by 3.3 points for the LLaMA3 model. |
Speech-Text Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment (2023.acl-long)
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Tianshu Yu, Haoyu Gao, Ting-En Lin, Min Yang, Yuchuan Wu, Wentao Ma, Chao Wang, Fei Huang, Yongbin Li
| Challenge: | Existing speech-text pre-training methods are limited to one or two specific tasks, despite their success in speech-language processing tasks. |
| Approach: | They propose a temporal position prediction task to capture the speech-text alignment . they use a textual dialog pre-training task to generalize a response selection task . |
| Outcome: | The proposed model is superior in learning speech-text alignment and multi-turn dialog context. |
Interactive Key-Value Memory-augmented Attention for Image Paragraph Captioning (2020.coling-main)
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| Challenge: | Existing approaches to image paragraph captioning ignore the past alignment information, resulting in repetitive captioning and incomplete captioning. |
| Approach: | They propose an Interactive key-value Memory-augmented Attention model for image paragraph captioning to keep track of attention history along with update-chain of decoder state. |
| Outcome: | Extensive experiments on a benchmark dataset demonstrate the effectiveness of the proposed model. |
LycheeCluster: Efficient Long-Context Inference with Structure-Aware Chunking and Hierarchical KV Indexing (2026.findings-acl)
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| 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)
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Yifan Lu, Jing Li, Yigeng Zhou, Yihui Zhang, Wenya Wang, Xiucheng Li, Meishan Zhang, Fangming Liu, Jun Yu, Min Zhang
| Challenge: | Existing knowledge editing methods for large language models (LLMs) suffer from over-editing, where detoxified models reject legitimate queries, compromising overall performance. |
| Approach: | They propose a toxicity-aware knowledge editing approach that dynamically detects toxic activation patterns during forward propagation and then routes computations through adaptive inter-layer pathways to mitigate toxicity effectively. |
| Outcome: | The proposed method outperforms existing methods on large language models and enhances the SafeEdit benchmark. |
D-QRELO: Training- and Data-Free Delta Compression for Large Language Models via Quantization and Residual Low-Rank Approximation (2026.findings-acl)
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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)
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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. |
Do Large Language Models have an English Accent? Evaluating and Improving the Naturalness of Multilingual LLMs (2025.acl-long)
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| Challenge: | Current Large Language Models (LLMs) are predominantly designed with English as the primary language, but many are still English-dominated. |
| Approach: | They propose to use automatic corpus-level metrics to assess lexical and syntactic naturalness of LLMs in a multilingual context. |
| Outcome: | The proposed method improves naturalness of LLMs in target languages without compromising performance on general-purpose benchmarks. |
Boundary-Driven Table-Filling for Aspect Sentiment Triplet Extraction (2022.emnlp-main)
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| 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. |
CMD: a framework for Context-aware Model self-Detoxification (2024.emnlp-main)
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| Challenge: | Existing methods of text detoxification fail to achieve a decent balance between effectiveness and generation quality. |
| Approach: | They propose a text detoxification framework that pays attention to both context and detoxification process. |
| Outcome: | Experiments on various LLMs show that the proposed framework can yield the best performance compared to baselines. |
SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers (2022.coling-1)
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Bowen Qin, Lihan Wang, Binyuan Hui, Bowen Li, Xiangpeng Wei, Binhua Li, Fei Huang, Luo Si, Min Yang, Yongbin Li
| Challenge: | Existing methods that learn from multiple semantically-equivalent questions are limited to one-to-one mapping . |
| Approach: | They propose a constraint to explore the underlying complementary semantic information among multiple semantically-equivalent questions and learn robust feature representations with reduced spurious associations. |
| Outcome: | The proposed method outperforms strong competitors and achieves state-of-the-art results on five benchmark datasets. |
PaCE: Unified Multi-modal Dialogue Pre-training with Progressive and Compositional Experts (2023.acl-long)
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| Challenge: | Existing research on multi-modal dialogue pre-training is limited due to limited availability of multi-dimensional data . a recent emergence of chatGPT 1 has increased confidence in the potential for this goal . |
| Approach: | They propose a framework for multi-modal dialogue pre-training that integrates experts to accommodate multi-faceted tasks. |
| Outcome: | The proposed framework achieves state-of-the-art on eight multi-modal dialog benchmarks. |
Beware of Model Collapse! Fast and Stable Test-time Adaptation for Robust Question Answering (2023.emnlp-main)
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| 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. |
Self-Distillation with Meta Learning for Knowledge Graph Completion (2022.findings-emnlp)
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| Challenge: | Existing knowledge graph completion frameworks for knowledge graphs are far from complete and require missing triples to be added to them. |
| Approach: | They propose a dynamic pruning technique to obtain a pruned model from a large source model, where the pruning mask of the pruned models could be updated adaptively per epoch after the model weights are updated. |
| Outcome: | The proposed framework achieves competitive performance compared to strong baselines, while being 10x smaller than baselines. |
Think Faster Than Words: Efficient LLM Chain-of-Thought Reasoning via Dynamic Shortcut Decoding (2026.acl-long)
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| Challenge: | Existing methods that prune or employ early stopping to reduce latency often compromise reasoning reliability. |
| Approach: | They propose a shortcut decoding framework that integrates probes over internal hidden states with step-level entropy to detect convergence of reasoning during generation and adaptively selects between a fast-exit path and a stability-verified path to remove redundant steps while preserving answer correctness. |
| Outcome: | The proposed framework reduces token usage by approximately 35% and maintains accuracy comparable to full CoT decoding. |
DRPruning: Efficient Large Language Model Pruning through Distributionally Robust Optimization (2025.acl-long)
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| 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)
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Yang Xu, Yunlong Feng, Honglin Mu, Yutai Hou, Yitong Li, Xinghao Wang, Wanjun Zhong, Zhongyang Li, Dandan Tu, Qingfu Zhu, Min Zhang, Wanxiang Che
| Challenge: | Existing methods suffer from key information loss and difficulty in adjusting the length of compressed sequences based on documentation lengths. |
| Approach: | They propose two strategies for compressing tool documentation into concise and precise summary sequences for tool-using language models. |
| Outcome: | The proposed approach achieves comparable performance to the upper-bound baseline under 16x compression ratio. |
INFORM : Information eNtropy based multi-step reasoning FOR large language Models (2023.emnlp-main)
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| 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. |
PiKGL: Leveraging Pruned Knowledge Graphs for Explainable Stance Detection (2026.tacl-1)
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Bingbing Wang, Jingjie Lin, Zhixin Bai, Xintong Song, Qianlong Wang, Min Yang, Xi Zeng, Jing Li, Ruifeng Xu
| Challenge: | Experimental results demonstrate that a Pruned interpretable knowledge Graph Learning framework for explainable stance detection is state-of-the-art for social media stance prediction. |
| Approach: | They propose a Pruned interpretable knowledge Graph Learning framework for explainable stance detection that incorporates commonsense knowledge and prunes redundant information to ensure precision and minimize noise. |
| Outcome: | The proposed framework achieves state-of-the-art on three public datasets. |
CoEvol: Constructing Better Responses for Instruction Finetuning through Multi-Agent Cooperation (2024.emnlp-main)
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| Challenge: | Recent studies have focused on constructing substantial quantities of IFT data with minimal human effort. |
| Approach: | They propose a multi-agent cooperation framework for the improvement of IFT responses for large language models using a debate-advise-edit-judge paradigm. |
| Outcome: | The proposed framework outperforms baseline models on unseen tasks and shows that it can improve instruction-following capabilities on large language models. |
DoCIA: An Online Document-Level Context Incorporation Agent for Speech Translation (2025.findings-acl)
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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)
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| 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)
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| 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)
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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. |
SEER: Self-Aligned Evidence Extraction for Retrieval-Augmented Generation (2024.emnlp-main)
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| 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)
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| Challenge: | Autoregressive language models generate one token in one step, limiting inference efficiency . Existing methods do not adapt to different situations to maximize acceptance length . speculative decoding has shown great potential for lossless acceleration . |
| Approach: | They propose an algorithm to construct adaptive and scalable draft trees for autoregressive language models. |
| Outcome: | Experimental results show that OPT-Tree outperforms existing draft trees and achieves speed-up ratio of up to 3.2 compared with autoregressive decoding. |
ConsistTL: Modeling Consistency in Transfer Learning for Low-Resource Neural Machine Translation (2022.emnlp-main)
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| 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. |
MIND Your Reasoning: A Meta-Cognitive Intuitive-Reflective Network for Dual-Reasoning in Multimodal Stance Detection (2026.acl-long)
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| Challenge: | Existing methods operate by learning to fuse modalities, leading to frequent misjudgments. |
| Approach: | They propose a paradigm shift from *learning to fuse* to *learning the reason's process' inspired by the dual-process theory of human cognition, MIND operationalizes a self-improving loop. |
| Outcome: | The proposed model significantly outperforms baseline models and exhibits strong generalization. |
Towards More Realistic Chinese Spell Checking with New Benchmark and Specialized Expert Model (2024.lrec-main)
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Yue Wang, Zilong Zheng, Juntao Li, Zhihui Liu, Jinxiong Chang, Qishen Zhang, Zhongyi Liu, Guannan Zhang, Min Zhang
| Challenge: | Large Language Models (LLMs) have been gaining attention for their ability to perform a wide range of open-domain tasks . however, the performance of LLMs has yet to be comprehensively evaluated in realistic scenarios . |
| Approach: | They propose a task to evaluate the performance of Large Language Models (LLMs) they propose RCSC task to convert Chinese text into correct text . |
| Outcome: | The proposed task evaluates the performance of existing methods in Chinese text . the realistic Chinese spell checker can achieve state-of-the-art performance on the task . |
MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction (2022.naacl-main)
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| 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. |
Unveiling the Potential of BERT-family: A New Recipe for Building Scalable, General and Competitive Large Language Models (2025.acl-long)
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| 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)
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| 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)
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| Challenge: | Existing approaches to automatically predict the emotions of posts consider each post individually and predict their emotions independently. |
| Approach: | They propose a Neural Personal Discrimination approach to identify personal attributes from posts and connect relevant posts with similar attributes to jointly learn their emotions. |
| Outcome: | The proposed approach improves on existing models by capturing attributes-aware words and predicting emotions among relevant posts. |
One-Shot Learning as Instruction Data Prospector for Large Language Models (2024.acl-long)
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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. |
ALW: Adaptive Layer-Wise contrastive decoding enhancing reasoning ability in Large Language Models (2025.findings-acl)
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| 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. |
Aspect Sentiment Classification with Document-level Sentiment Preference Modeling (2020.acl-main)
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| Challenge: | Existing studies consider Aspect Sentiment Classification (ASC) as an independent sentence-level classification problem aspect by aspect. |
| Approach: | They propose a Cooperative Graph Attention Networks approach for cooperatively learning aspect-related sentence representation. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods in document-level sentiment classification. |
mRAKL: Multilingual Retrieval-Augmented Knowledge Graph Construction for Low-Resourced Languages (2025.findings-acl)
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| Challenge: | Knowledge Graphs are structured multirelational graphs that store factual knowledge. |
| Approach: | They introduce a Retrieval-Augmented Generation (mRAKL) based system to perform mKGC. |
| Outcome: | The proposed approach improves over a no-context setting with an idealized retrieval system. |
Modeling Consistency Preference via Lexical Chains for Document-level Neural Machine Translation (2022.emnlp-main)
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| 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)
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| 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. |
Bridging Pre-trained Language Models and Hand-crafted Features for Unsupervised POS Tagging (2022.findings-acl)
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| 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)
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Chenhao Zhang, Xi Feng, Yuelin Bai, Xeron Du, Jinchang Hou, Kaixin Deng, Guangzeng Han, Qinrui Li, Bingli Wang, Jiaheng Liu, Xingwei Qu, Yifei Zhang, Qixuan Zhao, Yiming Liang, Ziqiang Liu, Feiteng Fang, Min Yang, Wenhao Huang, Chenghua Lin, Ge Zhang, Shiwen Ni
| Challenge: | MLLMs perform poorly on traditional culture images, indicating limitations in understanding high-level semantics and lacking a deep knowledge base of Chinese traditional culture. |
| Approach: | They propose to use Chinese images to assess MLLMs' higher-order perception and understanding of Chinese visual content. |
| Outcome: | The proposed model incorporates images that represent Chinese traditional culture, such as famous Chinese traditional paintings, to ensure the authenticity of the Chinese context. |
Stacked AMR Parsing with Silver Data (2021.findings-emnlp)
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| Challenge: | Lack of large-scale annotated data is one main challenge for abstract meaning representation (AMR) parsing. |
| Approach: | They propose to use silver data to train a pre-trained abstract meaning representation model. |
| Outcome: | The proposed model outperforms previous models on the AMR2.0 dataset and is faster than the SOTA model. |
UniMoE-Audio: Unified Speech and Music Generation with Dynamic-Capacity Mixture-of-Experts (2026.acl-long)
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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. |
Exploring All-In-One Knowledge Distillation Framework for Neural Machine Translation (2023.emnlp-main)
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| Challenge: | Existing knowledge distillation methods only obtain one lightweight student each time . this could be resource-intensive and resulting in multiple students not being optimally utilized . |
| Approach: | They propose a knowledge distillation framework which generates multiple satisfactory students at once. |
| Outcome: | The proposed framework generates multiple satisfactory students at once. |
Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning? (2024.acl-long)
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| 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. |
Knowledge as A Bridge: Improving Cross-domain Answer Selection with External Knowledge (C18-1)
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| Challenge: | Existing approaches to answer selection are limited in domains with limited labeled data. |
| Approach: | They propose a Knowledge-aware Attentive Network framework for cross-domain answer selection that uses the knowledge base as a bridge to enable knowledge transfer from the source domain to the target domain. |
| Outcome: | The proposed model outperforms strong competitors by a noticeable margin in cross-domain answer selection. |
META-LORA: Memory-Efficient Sample Reweighting for Fine-Tuning Large Language Models (2025.coling-main)
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| Challenge: | Supervised fine-tuning (SFT) is widely adopted for tailoring large language models (LLMs) to specific downstream tasks. |
| Approach: | They propose a memory-efficient method for automatic sample reweighting that learns to re-weight fine-tuning samples by minimizing the loss on a small, high-quality validation set. |
| Outcome: | Meta-LoRA learns to reweight fine-tuning samples by minimizing the loss on a small, high-quality validation set through an end-to-end bi-level optimization framework based on meta-learning. |
TP-Link: Fine-grained Pre-Training for Text-to-SQL Parsing with Linking Information (2024.lrec-main)
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Ziqiang Liu, Shujie Li, Zefeng Cai, Xiangyu Li, Yunshui Li, Chengming Li, Xiping Hu, Ruifeng Xu, Min Yang
| Challenge: | Existing pre-training frameworks for text-to-SQL parsing have shown inherent differences in distributions between tables and plain text. |
| Approach: | They propose a framework to improve context-dependent Text-to-SQL parsing by leveraging Linking information. |
| Outcome: | The proposed framework achieves state-of-the-art performance on two leading downstream benchmarks. |
End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions (2023.emnlp-main)
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| 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. |
Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning (2023.acl-long)
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Alexander Hanbo Li, Mingyue Shang, Evangelia Spiliopoulou, Jie Ma, Patrick Ng, Zhiguo Wang, Bonan Min, William Yang Wang, Kathleen McKeown, Vittorio Castelli, Dan Roth, Bing Xiang
| Challenge: | Existing methods for data-to-text generation focus on specific types of structured data. |
| Approach: | They propose a method that provides a unified representation that can handle various forms of structured data such as tables, knowledge graph triples, and meaning representations. |
| Outcome: | The proposed method improves zero-shot and few-shot scenarios and can adapt to new structured data. |
Recognizing Everything from All Modalities at Once: Grounded Multimodal Universal Information Extraction (2024.findings-acl)
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| Challenge: | Existing studies on IE tasks have focused on recognizing and analyzing cross-modal information . a multimodal large language model (MLLM) is developed to analyze IE across modalities . |
| Approach: | They propose a multimodal large language model (MLLM) capable of grounding information from all modalities. |
| Outcome: | The proposed framework provides a framework to analyze IE tasks over various modalities and their fine-grained groundings. |
Efficient Second-Order TreeCRF for Neural Dependency Parsing (2020.acl-main)
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| 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. |
Fair-CCD: Mitigating Bias in Large Language Models for Tabular Classification Through Context-Contrastive Decoding (2026.acl-long)
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| Challenge: | Prior work to mitigate fairness issues often employs subjective demonstration selection, leading to low controllability and limited stability across different models and tasks. |
| Approach: | They propose to use in-context learning to insert social biases into large language models to create a structured and controllable representation of the relationship between sensitive attributes and predicted labels. |
| Outcome: | Extensive experiments show that Fair-CCD consistently improves fairness metrics without degrading task accuracy. |
Multi-Modality Expansion and Retention for LLMs through Parameter Merging and Decoupling (2025.acl-long)
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Junlin Li, Guodong Du, Jing Li, Sim Kuan Goh, Wenya Wang, Yequan Wang, Fangming Liu, Ho-Kin Tang, Saleh Alharbi, Daojing He, Min Zhang
| Challenge: | Large Language Models (LLMs) are a cornerstone in artificial intelligence due to their exceptional performance. |
| Approach: | They propose a training-free approach that integrates existing MLLMs for effective multimodal expansion while retaining their original performance. |
| Outcome: | The proposed approach can expand LLMs' multimodal capabilities while retaining original performance. |
OpenResearcher: Unleashing AI for Accelerated Scientific Research (2024.emnlp-demo)
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Yuxiang Zheng, Shichao Sun, Lin Qiu, Dongyu Ru, Cheng Jiayang, Xuefeng Li, Jifan Lin, Binjie Wang, Yun Luo, Renjie Pan, Yang Xu, Qingkai Min, Zizhao Zhang, Yiwen Wang, Wenjie Li, Pengfei Liu
| Challenge: | Global scientific publications are growing annually by about 4%-5% (Pinedo et al., 2024). |
| Approach: | They introduce an AI-assisted platform that answers diverse questions from researchers using Retrieval-Augmented Generation (RAG) they develop various tools to understand queries, search from the scientific literature, filter retrieved information, provide accurate and comprehensive answers, and self-refine answers. |
| Outcome: | OpenResearcher is built on Retrieval-Augmented Generation (RAG) to integrate Large Language Models (LLMs) with up-to-date, domain-specific knowledge. |
𝒜3: Automatic Alignment Framework for Attributed Text Generation (2025.acl-long)
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| Challenge: | Existing approaches of aligning large language models to follow user instructions can lead to undue emphasis on irrelevant documents, which in turn reduces the quality of responses. |
| Approach: | They propose to use a framework to automatically generate high-quality attributed query-response pairs for both supervised fine-tuning and preference optimization stages without human annotation. |
| Outcome: | The proposed framework can generate high-quality attributed query-response pairs without human annotation without human intervention. |
Multi-grained Chinese Word Segmentation with Weakly Labeled Data (2020.coling-main)
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| 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. |
Dynamic Long Context Reasoning over Compressed Memory via End-to-End Reinforcement Learning (2026.acl-long)
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| 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)
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Dan Zhang, Sining Zhoubian, Min Cai, Fengzu Li, Lekang Yang, Wei Wang, Tianjiao Dong, Ziniu Hu, Jie Tang, Yisong Yue
| Challenge: | Existing benchmarks focus on single task, simple evaluation metrics, and readily available ground truth (GT) DataSciBench is built on curated, natural, and challenging prompts with complex evaluation criteria and uncertain GT. |
| Approach: | They propose a benchmark for evaluating Large Language Models in data science that integrates LLM-based self-consistency and human verification to ensure accuracy. |
| Outcome: | The proposed framework outperforms open-source models in all metrics and offers rigorous insights into LLM strengths and weaknesses. |
SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser (2022.emnlp-main)
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| 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)
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| 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. |
E-EVAL: A Comprehensive Chinese K-12 Education Evaluation Benchmark for Large Language Models (2024.findings-acl)
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Jinchang Hou, Chang Ao, Haihong Wu, Xiangtao Kong, Zhigang Zheng, Daijia Tang, Chengming Li, Xiping Hu, Ruifeng Xu, Shiwen Ni, Min Yang
| Challenge: | despite the rapid development of Large Language Models, there is no dedicated benchmark for evaluating LLMs in Chinese K-12 education. |
| Approach: | They propose to develop a benchmark specifically tailored for Chinese K-12 education. |
| Outcome: | EVAL is the first evaluation benchmark specifically tailored for Chinese K-12 education. |
WindowsWorld: A Process-Centric Benchmark of Autonomous GUI Agents in Professional Cross-Application Environments (2026.findings-acl)
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| 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. |
Multi-Turn Dialogue Generation in E-Commerce Platform with the Context of Historical Dialogue (2020.findings-emnlp)
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WeiSheng Zhang, Kaisong Song, Yangyang Kang, Zhongqing Wang, Changlong Sun, Xiaozhong Liu, Shoushan Li, Min Zhang, Luo Si
| Challenge: | Existing research on customer service dialogue generation generates generic responses from sellers . however, such cost prohibits small businesses, and multiturn dialogue generation is becoming more popular. |
| Approach: | They propose a novel and extensible dialogue generation method by leveraging sellers’ historical dialogue information to generate generic seller responses. |
| Outcome: | The proposed model can generate high-quality responses that cater to specific sellers’ characteristics and exhibit consistent superiority over baselines on a real-world multi-turn customer service dialogue dataset. |
LLMs as World Models: Data-Driven and Human-Centered Pre-Event Simulation for Disaster Impact Assessment (2025.emnlp-main)
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| Challenge: | Recent advances in large language models (LLMs) show promise in simulating complex scenarios. |
| Approach: | They examine multiple LLMs to proactively estimate perceived earthquake impacts using multimodal datasets and multimodal imagery. |
| Outcome: | The framework generates Modified Mercalli Intensity (MMI) predictions at zip code and county scales using multimodal datasets. |
Marathon: A Race Through the Realm of Long Context with Large Language Models (2024.acl-long)
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| 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. |
One vs. Many QA Matching with both Word-level and Sentence-level Attention Network (C18-1)
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| Challenge: | Existing studies on question answer matching focus on formal text . however, there exists many scenarios where the QA text is informal . |
| Approach: | They propose a novel QA matching approach using informal text from a product review site. |
| Outcome: | The proposed approach improves word-level and sentence-level attentions for solving the noisy problem in the informal text. |
Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA (2024.emnlp-main)
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Minzheng Wang, Longze Chen, Fu Cheng, Shengyi Liao, Xinghua Zhang, Bingli Wu, Haiyang Yu, Nan Xu, Lei Zhang, Run Luo, Yunshui Li, Min Yang, Fei Huang, Yongbin Li
| Challenge: | Existing benchmarks for evaluating long-context language models employ irrelevant noise texts to artificially extend the length of test cases, diverging from the real-world scenarios of long-constituency applications. |
| Approach: | They propose a long-context benchmark, Loong, aligning with realistic scenarios through extended multi-document question answering (QA) . |
| Outcome: | The proposed model can scale up the context window of large language models to perform in-depth analysis of multiple long documents. |
JANUS: Joint Autoregressive and Non-autoregressive Training with Auxiliary Loss for Sequence Generation (2022.emnlp-main)
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| 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)
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| 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)
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| Challenge: | Existing studies on aspect sentiment classification focus on non-interactive reviews . a new task aims to predict sentiment polarities for specific aspects from interactive reviews based on annotated corpus . |
| Approach: | They propose a task to predict aspects from interactive QA style reviews using an annotated corpus. |
| Outcome: | The proposed approach is compared with state-of-the-art methods against a high-quality corpus of data. |
Cooperative Denoising for Distantly Supervised Relation Extraction (C18-1)
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| Challenge: | Existing methods for distantly supervised relation extraction suffer from noisy labeling problem, which can severely degrade its performance. |
| Approach: | They propose a framework for distantly supervised relation extraction that leverages text corpus and knowledge graph and a cooperative module involving their mutual learning. |
| Outcome: | The proposed method reduces the noisy labels and achieves substantial improvement over the state-of-the-art methods. |
Towards Demonstration-Aware Large Language Models for Machine Translation (2024.findings-acl)
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| 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. |
Temporal Knowledge Question Answering via Abstract Reasoning Induction (2024.acl-long)
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| 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. |
Isotropic Representation Can Improve Zero-Shot Cross-Lingual Transfer on Multilingual Language Models (2023.findings-emnlp)
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| Challenge: | Existing mPLMs can align representations well for myriads of cross-lingual transfer tasks. |
| Approach: | They propose enhanced isotropy and constrained code-switching for zero-shot cross-lingual transfer to alleviate the problem of misalignment caused by anisotropic representations. |
| Outcome: | The proposed method improves on three zero-shot cross-lingual transfer tasks and over existing methods. |
Supervised Treebank Conversion: Data and Approaches (P18-1)
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| 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)
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| 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)
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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)
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| 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)
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| 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. |
Improving AMR Parsing with Sequence-to-Sequence Pre-training (2020.emnlp-main)
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| 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. |
Making Flexible Use of Subtasks: A Multiplex Interaction Network for Unified Aspect-based Sentiment Analysis (2021.findings-acl)
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| Challenge: | Existing studies aim to integrate multiple sub-tasks into a unified ABSA model but suffer from major disadvantages . |
| Approach: | They propose a multi-task learning approach to make use of sub-tasks for a unified ABSA. |
| Outcome: | The proposed model can work well when some sub-tasks are absent, and the interactive relations among subtasks not adequate. |
3AM: An Ambiguity-Aware Multi-Modal Machine Translation Dataset (2024.lrec-main)
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Xinyu Ma, Xuebo Liu, Derek F. Wong, Jun Rao, Bei Li, Liang Ding, Lidia S. Chao, Dacheng Tao, Min Zhang
| Challenge: | Existing studies have shown that visual information in existing MMT datasets is insufficient, causing models to disregard it and overestimate their capabilities. |
| Approach: | They propose to use 3AM to create an ambiguity-aware multimodal machine translation dataset. |
| Outcome: | The proposed dataset includes more ambiguity and a greater variety of captions and images than other MMT datasets. |
A Syntax-aware Multi-task Learning Framework for Chinese Semantic Role Labeling (D19-1)
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| 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)
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| Challenge: | Existing studies show that LLMs can confidently state non-existent facts rather than answering "I don't know". |
| Approach: | They propose a multi-source evidence fusion enhanced hallucination detection and correction framework that fuses evidence from multiple sources and iteratively revises the hallucinous content. |
| Outcome: | The proposed framework detects whether the generated content contains factual errors, provides the rationale behind the judgment, and iteratively revises the hallucinated content. |
Two Intermediate Translations Are Better Than One: Fine-tuning LLMs for Document-level Translation Refinement (2025.acl-long)
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| 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)
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| Challenge: | Opinion role labeling (ORL) is a fine-grained opinion analysis task . due to the scarcity of labeled data, ORL remains challenging for data-driven methods due to its complexity and complexity. |
| Approach: | They propose to integrate syntactic knowledge into ORL models by comparing and integrating different representations and using dependency graph convolutional networks to encode parser information at different processing levels. |
| Outcome: | The proposed model achieves 4.34 higher F1 score than the current state-of-the-art. |
Rethinking Negative Instances for Generative Named Entity Recognition (2024.findings-acl)
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| Challenge: | Named Entity Recognition (NER) models are constrained by a pre-defined label set and require extensive human annotations, which limits their flexibility and adaptability to unseen tasks. |
| Approach: | They propose a Generative NER system that shows improved zero-shot performance across unseen entity domains by introducing contextual information and delineating label boundaries. |
| Outcome: | The proposed model outperforms state-of-the-art methods in zero-shot evaluation. |
Improving Disentangled Text Representation Learning with Information-Theoretic Guidance (2020.acl-main)
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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. |
Improving Chinese Named Entity Recognition with Multi-grained Words and Part-of-Speech Tags via Joint Modeling (2024.lrec-main)
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| Challenge: | Named entity recognition (CNER) is a fundamental task in natural language processing (NLP). |
| Approach: | They propose a tree parsing approach for jointly modeling Chinese named entity recognition (CNER) with multi-grained word segmentation (MWS) and POS tagging tasks. |
| Outcome: | The proposed approach achieves better or comparable performance with current methods. |
Improving Seq2Seq Grammatical Error Correction via Decoding Interventions (2023.findings-emnlp)
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| 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. |
Long Context is Not Long at All: A Prospector of Long-Dependency Data for Large Language Models (2024.acl-long)
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| Challenge: | Long-context modeling capabilities are important for large language models (LLMs) however, training LLMs with long context windows is insufficient since some samples do not exhibit strong semantic dependencies across long contexts. |
| Approach: | They propose a data mining framework ProLong that assigns each training sample with a long dependency score and ranks and filters them according to their results. |
| Outcome: | The proposed framework can rank and filter training samples that exhibit more powerful long-context modeling abilities. |
MeasHalu: Mitigation of Scientific Measurement Hallucinations for Large Language Models with Enhanced Reasoning (2026.findings-acl)
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Ruijun Huang, Zhiqiao Kang, Yuxuan Zhu, Junxiong Li, Jiahao Zhao, Minghuan Tan, Feng Jiang, Min Yang
| Challenge: | Large Language Models (LLMs) exhibit severe hallucinations, which undermine reliability of automated scientific document understanding systems. |
| Approach: | They propose a framework for mitigating scientific measurement hallucinations through enhanced reasoning and targeted optimization. |
| Outcome: | The proposed framework significantly reduces hallucination rates and improves overall accuracy on the MeasEval benchmark. |
Three Stream Based Multi-level Event Contrastive Learning for Text-Video Event Extraction (2023.emnlp-main)
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| 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)
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Dinghan Shen, Guoyin Wang, Wenlin Wang, Martin Renqiang Min, Qinliang Su, Yizhe Zhang, Chunyuan Li, Ricardo Henao, Lawrence Carin
| Challenge: | Existing deep learning architectures to model compositionality in text sequences require a large number of parameters and expensive computations. |
| Approach: | They propose two additional pooling strategies over word embeddings for improved interpretability and hierarchical pooling for spatial (n-gram) information within text sequences. |
| Outcome: | The proposed pooling strategies improve interpretability and preserve spatial (n-gram) information within text sequences. |
Sentiment Classification towards Question-Answering with Hierarchical Matching Network (D18-1)
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Chenlin Shen, Changlong Sun, Jingjing Wang, Yangyang Kang, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, Guodong Zhou
| Challenge: | Existing methods to classify QA text contain rich sentiment information. |
| Approach: | They propose a task/method to address QA sentiment analysis by annotating QA text pair with annotation guidelines. |
| Outcome: | The proposed method can learn the matching vectors of each Q-sentence, A-sentent unit. |
Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models (2024.lrec-main)
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| Challenge: | Recent advances in large language models (LLMs) have achieved great success in various NLP tasks, but the vast model parameters pose challenges in downstream fine-tuning. |
| Approach: | They propose a task-agnostic prompting strategy that analyzes each dialogue utterance before task execution to enhance LLMs' comprehension in multi-turn dialogues. |
| Outcome: | The proposed strategy outperforms other zero-shot prompts and matches or exceeds efficacy of few-shot ones. |
A Multi-Modal Context Reasoning Approach for Conditional Inference on Joint Textual and Visual Clues (2023.acl-long)
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| Challenge: | Existing methods for conditional inference on joint textual and visual clues lack multimodal context reasoning capability. |
| Approach: | They propose a multi-modal context reasoning approach that embeds textual semantics and objective image information into the pretrained language model to perform context reasoning. |
| Outcome: | The proposed approach improves on two data sets and shows 4.8% gain on the PMR. |
Socratic Human Feedback (SoHF): Expert Steering Strategies for LLM Code Generation (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are increasingly used for generating code solutions, but struggle with complex programming problems without human guidance. |
| Approach: | They use the “Socratic Feedback” paradigm to map observed feedback strategies to five stages of Socratic Questioning to identify failures in LLMs. |
| Outcome: | The proposed models solved 74% of the problems that the models initially failed to solve on their own. |
SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding (2022.coling-1)
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| Challenge: | Existing methods for dialog understanding only consider self-augmented dialogs as positive samples and treat all other dialogs like negative ones. |
| Approach: | They propose a tree-structured pre-trained conversation model which learns dialog representations from limited labeled dialogs and large-scale unlabeled dialog corpora via semi-supervised contrastive pre-training. |
| Outcome: | The proposed model can achieve state-of-the-art results on the DialoGLUE benchmark. |
TemplateGEC: Improving Grammatical Error Correction with Detection Template (2023.acl-long)
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| Challenge: | Existing methods for grammatical error correction (GEC) have been developed. |
| Approach: | They propose a method which integrates the detection labels from a Seq2Edit model to construct a template as the input. |
| Outcome: | The proposed method can perform human-in-the-loop error correction tasks. |
MuCPAD: A Multi-Domain Chinese Predicate-Argument Dataset (2022.naacl-main)
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| 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)
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| 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)
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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. |
M-CNER: A Corpus for Chinese Named Entity Recognition in Multi-Domains (L18-1)
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| 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)
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| Challenge: | In inference-time scaling, Chain-of-Thought (CoT) data is scarce or even unavailable. |
| Approach: | They propose a method which establishes an inference cycle to synthesize user queries and CoT data. |
| Outcome: | The proposed method achieves a 75.4% pass rate and a 79.6% win rate using small models in StableToolBench. |
Adaptive Weighting for Neural Machine Translation (C18-1)
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| 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. |
MelTrim: Coarse-to-Fine Data Pruning for Speech Classification (2026.findings-acl)
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Shaobo Wang, Tianle Niu, Xuan Ouyang, Xintong Li, Zhengkun Ge, Yue Min, Xiaoqian Liu, Hankun Wang, Linfeng Zhang
| Challenge: | Unlike image or text classification, speech classification tasks are particularly challenging due to the difficulty in capturing the acoustic, semantic, and contextual representations. |
| Approach: | They propose a dataset pruning method that coarsely filters redundant samples using DBSCAN clustering on Mel-Frequency Cepstral Coefficients (MFCC) features. |
| Outcome: | The proposed method achieves 49.5% improvement in WA on the MEAD dataset and 41.9% reduction in EER on speaker identification tasks. |
DuSQL: A Large-Scale and Pragmatic Chinese Text-to-SQL Dataset (2020.emnlp-main)
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| Challenge: | Existing text-to-SQL parsing methods mainly focus on English, but there is no labeled data available for the language . a larges-scale and pragmatic Chinese dataset is used for cross-domain text- to-Sql task . |
| Approach: | They propose a larges-scale Chinese dataset for a cross-domain text-to-SQL task . they analyze questions from several representative applications and use an effective data construction framework . |
| Outcome: | The proposed dataset contains 200 databases, 813 tables, and 23,797 question/SQL pairs. |
Unlocking Recursive Thinking of LLMs: Alignment via Refinement (2025.findings-acl)
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| Challenge: | Existing methods for recursive reasoning are limited due to lack of expert-curated data. |
| Approach: | They propose a method that unlocks the potential of Large Language Models for recursive reasoning through long-form Chain of Thought. |
| Outcome: | The proposed method outperforms preference optimization methods on the openAI o1-series models by 20% on 3k synthetic samples. |
Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing (2021.emnlp-main)
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| Challenge: | Existing approaches to parse text-to-SQL data are lacking labeled data for unseen evaluation databases. |
| Approach: | They propose a framework for enhancing SQL queries by automatically producing large numbers of SQL queries based on an abstract syntax tree grammar. |
| Outcome: | The proposed framework can produce high-quality natural language questions over strong baselines. |
TInR: Exploring Tool-Internalized Reasoning in Large Language Models (2026.acl-long)
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| Challenge: | Existing methods rely on external tool documentation during reasoning, leading to tool mastery difficulty, tool size constraints, and inference inefficiency. |
| Approach: | They propose a tool-internalized reasoning framework for unified reasoning and tool usage that integrates external tools into Large Language Models (LLMs) to address these issues, they propose 'tool-internet-based' reasoning. |
| Outcome: | The proposed method achieves superior performance across in-domain and out-of-domain settings, highlighting its effectiveness and efficiency. |
L-CiteEval: A Suite for Evaluating Fidelity of Long-context Models (2025.acl-long)
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| 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)
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| 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)
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| 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. |
APGN: Adversarial and Parameter Generation Networks for Multi-Source Cross-Domain Dependency Parsing (2021.findings-emnlp)
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| Challenge: | Existing models for dependency parsing use labeled training data for several fixed domains, but performance drops when labeles only exist for several out-domains. |
| Approach: | They propose a model for multi-source cross-domain dependency parsing that uses a parameter generation network and adversarial network for learning domain-invariant representations. |
| Outcome: | The proposed model improves cross-domain parsing performance by about 2 points over strong BERT-enhanced baselines over a recently released dataset for multi-domain dependency parse. |
LLMBox: A Comprehensive Library for Large Language Models (2024.acl-demos)
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Tianyi Tang, Hu Yiwen, Bingqian Li, Wenyang Luo, ZiJing Qin, Haoxiang Sun, Jiapeng Wang, Shiyi Xu, Xiaoxue Cheng, Geyang Guo, Han Peng, Bowen Zheng, Yiru Tang, Yingqian Min, Yushuo Chen, Jie Chen, Ranchi Zhao, Luran Ding, Yuhao Wang, Zican Dong, Xia Chunxuan, Junyi Li, Kun Zhou, Xin Zhao, Ji-Rong Wen
| Challenge: | a library to facilitate the development, use, and evaluation of large language models (LLMs) is presented. |
| Approach: | They propose a unified library to facilitate the development, use and evaluation of large language models (LLMs). |
| Outcome: | The proposed library is based on extensive experiments in a variety of evaluation settings. |
Ruler: A Model-Agnostic Method to Control Generated Length for Large Language Models (2024.findings-emnlp)
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| 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)
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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. |
Mitigating Language Confusion through Inference-time Intervention (2025.coling-main)
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| Challenge: | Existing methods to address the problem of language confusion are incontext learning and supervised fine-tuning (SFT) however, they consume context window space and require extensive data collection. |
| Approach: | They propose a language-sensitive intervention that detects and assesses language confusion without additional complex mechanisms. |
| Outcome: | The proposed method detects language confusion and assesses content quality without additional complex mechanisms. |
from Benign import Toxic: Jailbreaking the Language Model via Adversarial Metaphors (2025.acl-long)
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| Challenge: | Recent studies have exposed the risk of Large Language Models (LLMs) generating harmful content by jailbreak attacks. |
| Approach: | They propose a framework that exploits AdVersArial meTAphoR to induce LLMs to calibrate harmful metaphors for jailbreaking. |
| Outcome: | The proposed framework can successfully jailbreak Large Language Models (LLMs) by leveraging the AdVersArial meTAphoR (AVATAR) framework achieves state-of-the-art attack success rate across multiple advanced LLMs. |
A Training-free LLM-based Approach to General Chinese Character Error Correction (2025.acl-long)
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| 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. |
Quantification of Large Language Model Distillation (2025.acl-long)
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Sunbowen Lee, Junting Zhou, Chang Ao, Kaige Li, Xeron Du, Sirui He, Haihong Wu, Tianci Liu, Jiaheng Liu, Hamid Alinejad-Rokny, Min Yang, Yitao Liang, Zhoufutu Wen, Shiwen Ni
| Challenge: | Existing studies have revealed the robustness degra-dation caused by data distillation. |
| Approach: | They propose a framework to evaluate and quantify model distillation . they aim to identify identity cognition contradictions and analyse multi-granularity response similarities across models to measure the extent of homogenization. |
| Outcome: | The proposed framework addresses two key aspects: (1) Identifying identity cognition contradictions to assess discrepancies in how models perceive and represent identity-related information; (2) Analyzing multi-granularity response similarities across models to measure the extent of homogenization. |
CopyNE: Better Contextual ASR by Copying Named Entities (2024.acl-long)
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| Challenge: | Existing approaches to transcribe contextual named entities (NEs) treat entities as tokens and generate them token-by-token, which may result in incomplete transcriptions of entities. |
| Approach: | They propose a mechanism that can copy entities from the NE dictionary and reduce errors during entity transcription. |
| Outcome: | The proposed mechanism can copy entities from the NE dictionary, reducing errors during entity transcription, ensuring the completeness of the entity. |
MoZIP: A Multilingual Benchmark to Evaluate Large Language Models in Intellectual Property (2024.lrec-main)
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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 . |
Stance Detection on Social Media with Background Knowledge (2023.emnlp-main)
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| Challenge: | Existing studies of stance detection focus on learning stance information about specific targets from context, but in real-world scenarios, we usually have a certain understanding of a target when we express our stance on it. |
| Approach: | They propose to take the background knowledge of the target into account for better stance detection by categorizing it into episodic and discourse knowledge categories and a heuristic retrieval algorithm based on the topic to retrieve the Wikipedia documents relevant to the sample. |
| Outcome: | The proposed framework achieves state-of-the-art on four benchmark datasets showing that the proposed framework is able to detect stances in-target and zero-shot scenarios. |
DynamicKV: Task-Aware Adaptive KV Cache Compression for Long Context LLMs (2025.findings-emnlp)
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| Challenge: | Existing KV cache compression methods enforce a fixed pattern, neglecting task-specific characteristics, which hampers the effective retention of essential information while discarding less important tokens. |
| Approach: | They propose a Task-Aware KV cache mechanism that dynamically adjusts the KV caching size across different layers based on the characteristics of the tasks. |
| Outcome: | The proposed method surpasses state-of-the-art methods by 11% on the LongBench dataset even under extreme compression (0.9%) |
Scaling Laws Across Model Architectures: A Comparative Analysis of Dense and MoE Models in Large Language Models (2024.emnlp-main)
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| Challenge: | a study of large language models (LLMs) reveals the transferability and discrepancies of scaling laws between Dense and MoE models. |
| Approach: | They investigate the transferability and discrepancies of scaling laws between Dense Models and Mixture of Experts models. |
| Outcome: | The results show that the power-law scaling framework also applies to MoE Models . |
Taming Actor-Observer Asymmetry in Agents via Dialectical Alignment (2026.acl-long)
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| 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)
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| 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)
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| 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. |
VideoVista-CulturalLingo: 360° Horizons-Bridging Cultures, Languages, and Domains in Video Comprehension (2025.acl-long)
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| Challenge: | Existing video evaluation benchmarks focus on a single language, typically English, and feature videos rooted in Western cultural contexts. |
| Approach: | They propose a video evaluation benchmark designed to bridge cultural, linguistic, and domain divide in video comprehension. |
| Outcome: | The proposed video evaluation benchmark bridges cultural, linguistic, and domain divides . existing benchmarks only feature videos from YouTube, Shutterstock, or established video datasets based on cultural diversity . |
Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning (D19-1)
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Jingjing Wang, Changlong Sun, Shoushan Li, Jiancheng Wang, Luo Si, Min Zhang, Xiaozhong Liu, Guodong Zhou
| Challenge: | Recent neural networks have shown promising results on Document-level Aspect Sentiment Classification (DASC) however, these approaches often offer little transparency w.r.t. their inner working mechanisms and lack interpretability. |
| Approach: | They propose a Hierarchical Reinforcement Learning approach to DASC that incorporates clause selection and word selection strategies to tackle the data noise problem. |
| Outcome: | The proposed approach over the state-of-the-art approaches shows impressive performance over the current baselines. |
A Survey of Generative Information Extraction (2025.coling-main)
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| Challenge: | Information Extraction (IE) is a popular and fundamental task in natural language processing. |
| Approach: | They first review generative information extraction methods based on pre-trained language models and large language models focusing on their adaptation and generalization capabilities. |
| Outcome: | The proposed methods are based on pre-trained language models and large language models, and emphasize the importance of model collaboration. |
CoTJudger: A Graph-Driven Framework for Automatic Evaluation of Chain-of-Thought Efficiency and Redundancy in LRMs (2026.findings-acl)
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Siyi Li, Jiajun Shi, Shiwen Ni, Ge Zhang, Shuaimin Li, Shijian Wang, Zhoufutu Wen, Yizhi LI, Hamid Alinejad-Rokny, Jiaheng Liu, Min Yang, Wenhao Huang
| Challenge: | Existing evaluations emphasize final accuracy or coarse token counts, and lack automated tools to separate essential logic from structural redundancy. |
| Approach: | They propose a graph-driven framework that quantifies reasoning efficiency by converting free-form CoTs into directed dependency graphs and extracting the Shortest Effective Path needed to reach a correct solution. |
| Outcome: | Evaluating 21 LRMs, the proposed framework quantifies reasoning efficiency by converting free-form CoTs into directed dependency graphs and extracting the Shortest Effective Path (SEP) needed to reach a correct solution. |
LLMs Can Also Do Well! Breaking Barriers in Semantic Role Labeling via Large Language Models (2025.findings-acl)
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| 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)
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| Challenge: | Large Language Models (LLMs) have demonstrated incredible capabilities in understanding, generating, and manipulating languages. |
| Approach: | They propose a general SParse Efficient Editing MoDel which can fulfill diverse editing requirements through a single model while maintaining low computational costs. |
| Outcome: | The proposed model can fulfill diverse editing requirements through a single model while maintaining low computational costs. |
Efficient Sparse Attention needs Adaptive Token Release (2024.findings-acl)
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| 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)
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| 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)
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| 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)
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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. |
The Digital Dunning-Kruger Effect: Decoupling Hallucinations via Geometric Hidden-state Observation for Semantic Truthfulness (2026.acl-long)
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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%. |
Escaping the Echo Trap: On Credit Assignment Failure in Multi-turn LLM Self-Reflection (2026.acl-long)
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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)
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Longhui Zhang, Jiahao Wang, Meishan Zhang, GaoXiong Cao, Ensheng Shi, Mayuchi Mayuchi, Jun Yu, Honghai Liu, Jing Li, Min Zhang
| Challenge: | Existing training methods, such as direct instruction fine-tuning, overlook hierarchical relationships among acceleration patterns. |
| Approach: | They propose a new training paradigm that uses bidirectional tree editing and progressive code acceleration learning to improve LLMs’ CA capabilities. |
| Outcome: | The proposed training paradigm outperforms prompt-enhanced GPT-4 and current training-based methods on average across five programming languages. |
From Awareness to Adaptability: Enhancing Tool Utilization for Scientific Reasoning (2025.findings-acl)
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| Challenge: | Existing approaches enhance reasoning through Chain-of-Thought, Program-ofThough, and Tool-Integration. |
| Approach: | They propose a tool-awareness training method that leverages both forward and backward data generation strategies to strengthen the model’s conscious and selective tool utilization in multi-step reasoning tasks. |
| Outcome: | The proposed method improves the model's tool utilization capabilities, including proactivity and execution success rates. |
BatonVoice: An Operationalist Framework for Enhancing Controllable Speech Synthesis with Linguistic Intelligence from LLMs (2026.acl-long)
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Yue Wang, Ruotian Ma, Xingyu Chen, Zhengliang Shi, Morunliu Yang, Wanshun Chen, Huang Liu, Jiadi Yao, Xin He, Qu Yang, Qingxuan Jiang, Fanghua Ye, Juntao Li, Zhaopeng Tu, Xiaolong Li, Liefeng Bo, Min Zhang
| Challenge: | Existing approaches often fail to leverage the linguistic intelligence of Large Language Models (LLMs) Existing models lack the ability to follow text instructions for controllable Text-to-Speech (TTS). |
| Approach: | They propose a framework where an LLM acts as a conductor, understanding user instructions and generating a textual plan - explicit vocal features. |
| Outcome: | The proposed model outperforms open- and closed-source models in speech synthesis and achieves zero-shot cross-lingual generalization. |
Safety Alignment via Constrained Knowledge Unlearning (2025.acl-long)
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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)
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| 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)
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| 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. |
Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional Network (2022.acl-long)
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| Challenge: | Existing studies on multimodal sarcasm detection using textual and visual information have been limited to text-only approaches. |
| Approach: | They propose to construct a cross-modal graph for each multi-modal instance to explicitly draw the ironic relations between textual and visual modalities. |
| Outcome: | The proposed model achieves state-of-the-art in multi-modal sarcasm detection. |
Exploring and Mitigating Shortcut Learning for Generative Large Language Models (2024.lrec-main)
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| 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. |
A Neural Divide-and-Conquer Reasoning Framework for Image Retrieval from Linguistically Complex Text (2023.acl-long)
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| Challenge: | Pretrained Vision-Language Models (VLMs) have achieved remarkable performance in image retrieval from text, but their performance drops drastically when confronted with linguistically complex texts. |
| Approach: | They propose an end-to-end Neural Divide-and-Conquer Reasoning framework for linguistically complex texts that they struggle to comprehend. |
| Outcome: | The proposed framework significantly improves performance in complex image-text reasoning problem. |
Towards Text-Image Interleaved Retrieval (2025.acl-long)
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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. |
Chinese Spoken Named Entity Recognition in Real-world Scenarios: Dataset and Approaches (2024.findings-acl)
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| 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)
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| 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. |
Locate-and-Focus: Enhancing Terminology Translation in Speech Language Models (2025.acl-long)
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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. |
Open-ended Long Text Generation via Masked Language Modeling (2023.acl-long)
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| 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. |
Semi-supervised Domain Adaptation for Dependency Parsing (P19-1)
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| Challenge: | Currently, most studies on cross-domain parsing focus on unsupervised domain adaptation . however, unsupervised approaches make limited progress due to the intrinsic difficulty of both domain adaptation and parse. |
| Approach: | They propose a semi-supervised domain adaptation problem for Chinese dependency parsing by using newly-annotated large-scale domain-aware datasets. |
| Outcome: | The proposed method is more effective than direct corpus concatenation and multi-task learning. |