Papers by Yue Zhang
SOUL: Towards Sentiment and Opinion Understanding of Language (2023.emnlp-main)
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| Challenge: | Sentiment analysis models often fail to capture the broader complexities of sentiment analysis. |
| Approach: | They propose a task to evaluate sentiment understanding through two subtasks . they annotate a new dataset comprising 15,028 statements from 3,638 reviews . |
| Outcome: | The proposed task evaluates sentiment understanding through two subtasks . it is a challenging task for both small and large language models, with performance gaps of up to 27% . |
UniCodec: Unified Audio Codec with Single Domain-Adaptive Codebook (2025.acl-long)
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Yidi Jiang, Qian Chen, Shengpeng Ji, Yu Xi, Wen Wang, Chong Zhang, Xianghu Yue, ShiLiang Zhang, Haizhou Li
| Challenge: | Existing neural audio codecs are not capable of handling multi-domain audio data . et al., 2023) integrate speech modality with text-based large language models . |
| Approach: | They propose a unified audio codec with a single codebook to support multi-domain audio data . they propose combining a mix-of-experts strategy and a partitioned domain-adaptive codebook method . |
| Outcome: | The proposed codec outperforms existing codecs on acoustic and semantic representation capabilities. |
Leveraging Local and Global Patterns for Self-Attention Networks (P19-1)
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| Challenge: | Existing approaches to integrate local and global information into self-attention networks have been criticized for overlooking neighboring information. |
| Approach: | They propose a hybrid attention mechanism to leverage local and global information . they use a gating scalar to integrate both sources of information based on local contexts . |
| Outcome: | The proposed approach improves on translation tasks and shows that the two types of contexts are complementary. |
Less is More: Mitigating Multimodal Hallucination from an EOS Decision Perspective (2024.acl-long)
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| Challenge: | Extensive research has shed light on the origins of multimodal hallucinations, including the inability of vision encoders to represent finegrained visual details and model reliance on inherent parametric knowledge such as language priors and statistical biases. |
| Approach: | They propose to use EOS to terminate generation of large multimodal models by comparing the generated text with the image to mitigate multimodal hallucinations. |
| Outcome: | The proposed method significantly improves the hallucination performance of Large Multimodal Models without additional data or knowledge. |
Towards Faithful Dialogues via Focus Learning (2023.acl-long)
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| Challenge: | Existing knowledge-grounded models rely on elaborate data engineering or increasing the model’s parameters ignoring to track the tokens that significantly influence losses, which is decisive for the optimization direction of the model in each iteration. |
| Approach: | They propose a novel learning approach that adjusts the contribution of each token to the optimization direction by directly scaling the corresponding objective loss. |
| Outcome: | The proposed approach achieves the new state-of-the-art results and generates more reliable responses while maintaining training stability. |
Multiscale Collaborative Deep Models for Neural Machine Translation (2020.acl-main)
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| Challenge: | Neural machine translation models with deeper neural networks are difficult to train. |
| Approach: | They propose a MultiScale Collaborative framework to boost gradient back-propagation . they let each encoder block learn a fine-grained representation and enhance it . |
| Outcome: | The proposed framework outperforms baseline models on translation tasks with three translation directions and achieves a BLEU score of 30.56 on the English-to-German task. |
Sentence-State LSTM for Text Representation (P18-1)
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| Challenge: | LSTMs have been shown to suffer from various limitations due to their sequential nature. |
| Approach: | They propose to model hidden states of all words simultaneously at each recurrent step rather than one word at a time. |
| Outcome: | The proposed model has strong representation power, giving competitive performances compared to stacked BiLSTM models with similar parameter numbers. |
TC–RAG: Turing–Complete RAG’s Case study on Medical LLM Systems (2025.acl-long)
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Xinke Jiang, Yue Fang, Rihong Qiu, Haoyu Zhang, Yongxin Xu, Hao Chen, Wentao Zhang, Ruizhe Zhang, Yuchen Fang, Xinyu Ma, Xu Chu, Junfeng Zhao, Yasha Wang
| Challenge: | Existing approaches to RAG neglect system state variables, resulting in poor performance and erroneous knowledge accumulation. |
| Approach: | They propose a framework that incorporates a Turing Complete System to manage state variables and manage retrieval halting. |
| Outcome: | The proposed framework improves on seven real-world healthcare datasets and shows that it is more accurate than existing methods. |
Denoising Enhanced Distantly Supervised Ultrafine Entity Typing (2023.findings-acl)
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| Challenge: | Recent work on distantly supervised (DS) ultra-fine entity typing has received significant attention . however, DS data is noisy and often suffers from missing or wrong labeling issues resulting in low precision and low recall. |
| Approach: | They propose a noise model to estimate unknown labeling noise distribution over input contexts and noisy type labels and a model to train on denoised data. |
| Outcome: | The proposed model outperforms baseline methods on the Ultra-Fine entity typing dataset and OntoNotes dataset. |
TestNUC: Enhancing Test-Time Computing Approaches and Scaling through Neighboring Unlabeled Data Consistency (2025.acl-long)
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Henry Peng Zou, Zhengyao Gu, Yue Zhou, Yankai Chen, Weizhi Zhang, Liancheng Fang, Yibo Wang, Yangning Li, Kay Liu, Philip S. Yu
| Challenge: | Test-time computing approaches that leverage additional computational resources during inference have been proven effective in enhancing large language model performance. |
| Approach: | They propose a linearly scaling approach that leverages local consistency of neighboring unlabeled data to improve test-time predictions. |
| Outcome: | The proposed approach outperforms baseline methods such as prompting and self-consistency across eight datasets and performs robustly across embedding models. |
MSEarth: A Multimodal Benchmark for Earth Science Phenomenon Discovery with MLLMs (2026.acl-long)
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Xiangyu Zhao, Wanghan Xu, Bo Liu, Yuhao Zhou, Fenghua Ling, Ben Fei, Xiaoyu Yue, Lei Bai, Wenlong Zhang, Xiao-Ming Wu
| Challenge: | Existing datasets often rely on synthetic data or figure-caption pairs, failing to capture the depth and complexity of geoscientific reasoning. |
| Approach: | They propose a multimodal scientific dataset and benchmark curated from open-access publications. |
| Outcome: | MSEarth features over 289K figures with captions enriched by contextual discussions and reasoning from original papers. |
SecFormer: Fast and Accurate Privacy-Preserving Inference for Transformer Models via SMPC (2024.findings-acl)
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| Challenge: | a growing number of cloud-based inference services are relying on SMPC to protect data privacy. |
| Approach: | They propose a framework for Privacy-Preserving Inference for Transformer models that eliminates exponential and maximum operations in PPI without sacrificing model performance. |
| Outcome: | The proposed framework outperforms MPCFormer in terms of performance and efficiency . it is 3.57 and 3.58 times faster than PUMA for BERTBASE and BERTLARGE . |
A Survey on Asking Clarification Questions Datasets in Conversational Systems (2023.acl-long)
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| Challenge: | Existing studies on Asking Clarification Questions (ACQs) are incomparable due to inconsistent data, experimental setups and evaluation strategies. |
| Approach: | They analyse the current research status on Asking Clarification Questions (ACQs) and propose a set of evaluation metrics and benchmarks for multiple ACQs-related tasks. |
| Outcome: | The proposed techniques are compared with the available datasets and evaluated against benchmarks. |
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. |
The APVA-TURBO Approach To Question Answering in Knowledge Base (C18-1)
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| Challenge: | Existing query languages for question answering over knowledge bases are not capable of processing queries presented in human language directly. |
| Approach: | They advocate a new model architecture that includes a verification mechanism for checking the correctness of predicted relations. |
| Outcome: | The proposed approach dramatically improves the question answering performance. |
An AMR-based Link Prediction Approach for Document-level Event Argument Extraction (2023.acl-long)
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| Challenge: | Recent work has introduced Abstract Meaning Representation (AMR) for Document-level Event Argument Extraction (Doc-level EAE) however, in these works AMR is used only implicitly, for instance, as additional features or training signals. |
| Approach: | They propose a novel AMR-based graph structure which uses graph neural networks to find event arguments from unstructured text. |
| Outcome: | The proposed graph structure outperforms the state-of-the-art models by 3.63pt and 2.33pt F1 and reduces inference time by 56%. |
POLYCHARTQA: Benchmarking Large Vision-Language Models with Multilingual Chart Question Answering (2026.acl-long)
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| Challenge: | Existing chart understanding benchmarks are overwhelmingly English-centric, limiting their accessibility and relevance to global audiences. |
| Approach: | They propose a multilingual chart question answering benchmark that enables efficient multilingual generation via data translation and code reuse. |
| Outcome: | The proposed benchmark systematically evaluates multilingual chart understanding on state-of-the-art LVLMs and shows a significant performance gap between English and other languages. |
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. |
HalluAudio: A Comprehensive Benchmark for Hallucination Detection in Large Audio-Language Models (2026.acl-long)
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| Challenge: | Existing studies on hallucination focus on text or vision, while few audio-oriented studies are limited in scale, modality coverage, and diagnostic depth. |
| Approach: | They propose a large-scale benchmark for evaluating hallucinations across speech, sound, and music. |
| Outcome: | The proposed model improves hallucination rate, yes/no bias, error-type analysis, and refusal rate. |
Synergetic Event Understanding: A Collaborative Approach to Cross-Document Event Coreference Resolution with Large Language Models (2024.acl-long)
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| Challenge: | Existing approaches to cross-document event coreference resolution are prone to learning simple co-occurrences due to the complexity of contexts. |
| Approach: | They propose a collaborative approach to cross-document event coreference resolution that leverages both a universally capable LLM and a task-specific SLM. |
| Outcome: | The proposed approach surpasses the performance of both large and small language models individually, underscoring its effectiveness in diverse scenarios. |
Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking (2022.acl-long)
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| Challenge: | Existing approaches to model the relations between domains and slots fail to address these issues and can be generalized to unseen domains. |
| Approach: | They propose a Dynamic Schema Graph Fusion Network which generates a dynamic schema graph to explicitly fuse prior slot-domain membership relations and dialogue-aware dynamic slot relations. |
| Outcome: | The proposed model outperforms existing methods on benchmark datasets showing that it can extract users' goals or intentions as dialogue states and keep them updated over the whole dialogue. |
Expectation Confirmation Preference Optimization for Multi-Turn Conversational Recommendation Agent (2025.findings-acl)
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Xueyang Feng, Jingsen Zhang, Jiakai Tang, Wei Li, Guohao Cai, Xu Chen, Quanyu Dai, Yue Zhu, Zhenhua Dong
| Challenge: | Recent advances in Large Language Models (LLMs) have propelled the development of Conversational Recommendation Agents (CRAs). |
| Approach: | They propose a multi-turn preference optimization paradigm that leverages Expectation Confirmation Theory to explicitly model the evolution of user satisfaction throughout multi-turned dialogues. |
| Outcome: | The proposed paradigm eliminates the significant sampling overhead of existing MTPO methods while ensuring the optimization process drives meaningful improvements. |
Prefix Text as a Yarn: Eliciting Non-English Alignment in Foundation Language Model (2024.findings-acl)
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| Challenge: | supervised fine-tuning (SFT) has been a straightforward approach for tailoring the output of foundation large language models (LLMs) to specific preferences. |
| Approach: | They propose a training-free alignment method that uses minimal prior tokens to bridge the foundation LLM and the SFT LLM. |
| Outcome: | The proposed method achieves comparable performance without training on machine translation and part-of-speech tagging across seven languages. |
Open Domain Event Extraction Using Neural Latent Variable Models (P19-1)
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| Challenge: | Existing work on extracting events from news documents focuses on a set of pre-specified event types. |
| Approach: | They propose a latent variable neural model which is scalable to large corpus. |
| Outcome: | The proposed model performs better than the state-of-the-art method for event schema induction. |
Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective (2025.naacl-long)
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Shenglai Zeng, Jiankun Zhang, Bingheng Li, Yuping Lin, Tianqi Zheng, Dante Everaert, Hanqing Lu, Hui Liu, Hui Liu, Yue Xing, Monica Xiao Cheng, Jiliang Tang
| Challenge: | Existing studies have shown that LLMs struggle to identify the boundaries of their own knowledge and tend to prioritize external information over internal knowledge learned during pre-training. |
| Approach: | They conduct a comprehensive analysis of LLM representation behaviors and demonstrate the significance of using representations in knowledge checking. |
| Outcome: | The proposed classifiers improve performance even when dealing with noisy knowledge databases. |
Leveraging Dependency Forest for Neural Medical Relation Extraction (D19-1)
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| Challenge: | Existing methods for medical relation extraction use dependency syntax as a source of features. |
| Approach: | They propose a method to extract relational information from medical literature by using dependency forests. |
| Outcome: | The proposed method outperforms the standard tree-based methods in the medical domain. |
Revisiting Data Reconstruction Attacks on Real-world Dataset for Federated Natural Language Understanding (2024.lrec-main)
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Zhuo Zhang, Jintao Huang, Xiangjing Hu, Jingyuan Zhang, Yating Zhang, Hui Wang, Yue Yu, Qifan Wang, Lizhen Qu, Zenglin Xu
| Challenge: | Existing DRA methods fail to accurately recover the original text of real-world privacy data. |
| Approach: | They propose to use a real-world privacy dataset to examine the performance of federated learning (FL) methods. |
| Outcome: | The proposed method improves on a real-world privacy dataset and shows that the tokens within a recovery sentence are disordered and intertwined with tokens from other sentences in the same training batch. |
Can Generative Pre-trained Language Models Serve As Knowledge Bases for Closed-book QA? (2021.acl-long)
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| Challenge: | Existing work is limited in using small benchmarks with high test-train overlaps. |
| Approach: | They construct a dataset of closed-book QA using SQuAD and investigate the performance of BART. |
| Outcome: | Experiments show that pre-trained language models can achieve high performance on closed-book QA tasks. |
Multi-Granularity Optimization for Non-Autoregressive Translation (2022.emnlp-main)
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| Challenge: | Non-autoregressive machine translation suffers severe performance deterioration due to the naive independence assumption. |
| Approach: | They propose a method which collects model behaviours on translation segments of various granularities and integrates feedback for backpropagation to reduce latency. |
| Outcome: | Experiments on four benchmark datasets show that the proposed method outperforms baseline models trained with cross-entropy loss and achieves the best performance on WMT’16 EnRo and highly competitive results on WTM’14 EnDe. |
LOViS: Learning Orientation and Visual Signals for Vision and Language Navigation (2022.coling-1)
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| Challenge: | Existing Transformer-based VLN agents entangle orientation and vision information, which limits the learning of each information source. |
| Approach: | They propose to design a navigation agent with explicit Orientation and Vision modules . they use a set of pre-training tasks to feed the modules into the model . |
| Outcome: | The proposed model improves on R2R and R4R datasets and achieves state-of-the-art results. |
AdaTooler-V: Adaptive Tool-Use for Images and Videos (2026.findings-acl)
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Chaoyang Wang, Kaituo Feng, Dongyang Chen, Zhongyu Wang, Zhixun Li, Sicheng Gao, Meng Meng, Xu Zhou, Manyuan Zhang, Yuzhang Shang, Xiangyu Yue
| Challenge: | Existing models exhibit blind tool-use reasoning patterns, which significantly increases inference overhead and degrades model performance. |
| Approach: | They propose an MLLM that performs adaptive tool-use by determining whether a visual problem truly requires tools. |
| Outcome: | The proposed model outperforms existing methods in visual reasoning tasks. |
1+1>2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators? (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have been recognized for their impressive capabilities in natural language processing (NLP). |
| Approach: | They propose a method to enhance the multilingual performance of Large Language Models by aggregating knowledge from diverse languages. |
| Outcome: | The proposed method reduces the performance disparity across languages and offers valuable insights for further exploration. |
PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction (2021.acl-long)
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| Challenge: | Chinese spelling correction (CSC) is a task to detect and correct spelling errors in texts. |
| Approach: | They propose a Pre-trained masked Language model with Misspelled knowledgE (PLOME) which jointly learns how to understand language and correct spelling errors. |
| Outcome: | The proposed model outperforms state-of-the-art methods on widely used benchmarks and achieves superior performance against existing models. |
TRAMS: Training-free Memory Selection for Long-range Language Modeling (2023.findings-emnlp)
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| Challenge: | Existing methods like Transformer-XL are plagued by ineffective memory selections due to the high number of tokens involved in attention calculation. |
| Approach: | They propose a plug-and-play strategy that selects tokens participating in attention calculation based on one simple metric and ignores the other ones. |
| Outcome: | The proposed strategy keeps tokens with high attention scores and ignores the other ones on word-level and character-level benchmarks without additional training or adding additional parameters. |
StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding (2023.emnlp-main)
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Cheng Jiayang, Lin Qiu, Tsz Chan, Tianqing Fang, Weiqi Wang, Chunkit Chan, Dongyu Ru, Qipeng Guo, Hongming Zhang, Yangqiu Song, Yue Zhang, Zheng Zhang
| Challenge: | Analogy-making between narratives is crucial for human reasoning . despite its importance, there has been limited research on story analogies . |
| Approach: | They construct a large-scale story-level analogy corpus with 24K story pairs . they find that the tasks are incredibly difficult for large language models such as ChatGPT . |
| Outcome: | The proposed corpus contains 24K story pairs from diverse domains with human annotations on two similarities from the extended Structure-Mapping Theory. |
A Survey on LLM-based Conversational User Simulation (2026.eacl-long)
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Bo Ni, Yu Wang, Leyao Wang, Branislav Kveton, Franck Dernoncourt, Yu Xia, Hongjie Chen, Reuben Luera, Samyadeep Basu, Subhojyoti Mukherjee, Puneet Mathur, Nesreen K. Ahmed, Junda Wu, Li Li, Huixin Zhang, Ruiyi Zhang, Tong Yu, Sungchul Kim, Jiuxiang Gu, Zhengzhong Tu, Alexa Siu, Zichao Wang, Seunghyun Yoon, Nedim Lipka, Namyong Park, Zihao Lin, Trung Bui, Yue Zhao, Tyler Derr, Ryan A. Rossi
| Challenge: | Recent advances in large language models (LLMs) have enabled high-fidelity generation of synthetic user conversation. |
| Approach: | They propose a taxonomy covering user granularity and simulation objectives . they analyze core techniques and evaluation methodologies to help them understand the latest developments . |
| Outcome: | The proposed model enables high-fidelity generation of synthetic user conversation. |
Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs (2020.tacl-1)
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| Challenge: | Recent graph-to-text models generate text from graph data using global or local aggregation . global node encoding allows explicit communication between two distant nodes, but fails to capture long-range relationships. |
| Approach: | They propose to combine global and local aggregation to learn node representations . they propose to use global and locally encoding to learn contextualized node embeddings based on graph data . |
| Outcome: | The proposed models outperform state-of-the-art models on two graph-to-text datasets by 18.01 and 63.69 points. |
Prompt-Tuned Muti-Task Taxonomic Transformer (PTMTTaxoFormer) (2024.emnlp-industry)
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| Challenge: | Existing methods for Hierarchical Text Classification (HTC) are expensive and require explicit injection of the hierarchy, verbalizers, and/or prompt engineering. |
| Approach: | They propose a hierarchical text classification system that uses a single classifier to predict one or more topics using differentiable prompts and labels that are learnt through backpropagation. |
| Outcome: | The proposed model outperforms existing models on several benchmarks that span a range of topics consistently. |
Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs (2024.findings-emnlp)
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Junjie Wang, Mingyang Chen, Binbin Hu, Dan Yang, Ziqi Liu, Yue Shen, Peng Wei, Zhiqiang Zhang, Jinjie Gu, Jun Zhou, Jeff Pan, Wen Zhang, Huajun Chen
| Challenge: | Recent studies have attempted to enhance the performance of large language models (LLMs) in complex question-answering (QA) tasks by combining step-wise planning with external retrieval. |
| Approach: | They propose a framework for enhancing LLMs’ planning capabilities by using planning data derived from knowledge graphs (KGs). |
| Outcome: | The proposed framework improves LLMs’ planning capabilities by using knowledge graphs (KGs) the proposed framework is compared with existing frameworks on multiple datasets and shows that it is effective for large language models. |
BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers (2024.emnlp-main)
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Ran Xu, Wenqi Shi, Yue Yu, Yuchen Zhuang, Yanqiao Zhu, May Dongmei Wang, Joyce Ho, Chao Zhang, Carl Yang
| Challenge: | Developing effective biomedical retrieval models is important for excelling at knowledge-intensive biomedically tasks but still challenging due to the lack of sufficient publicly annotated biomedic data and computational resources. |
| Approach: | They propose a series of dense retrievers for enhancing biomedical retrieval via unsupervised pre-training on large biomedically corpora, followed by instruction fine-tuning on a combination of labeled datasets and synthetic pairs. |
| Outcome: | Experiments on 5 biomedical tasks across 11 datasets confirm the performance of the retrieval model on various biomedically demanding tasks. |
Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Assistance (2026.acl-long)
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| Challenge: | Current approaches to writing effective rebuttals are limited by the direct-to-text generation problem . authors must accurately decipher reviewer intent while ensuring every response is firmly anchored in verifiable manuscript details. |
| Approach: | They propose a framework that reframes rebuttal generation as an evidence-centric planning task. |
| Outcome: | The proposed framework outperforms baselines in coverage, faithfulness, and strategic coherence. |
Distributed Marker Representation for Ambiguous Discourse Markers and Entangled Relations (2023.acl-long)
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| Challenge: | Discourse markers are natural representations of discourse in our daily language. |
| Approach: | They propose to use unlimited discourse marker data to learn a Distributed Marker Representation by bridging markers with sentence pairs. |
| Outcome: | The proposed model outperforms existing models on the implicit discourse relation recognition task and provides strong interpretability. |
A Rationale-Centric Framework for Human-in-the-loop Machine Learning (2022.acl-long)
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| Challenge: | Experimental results show that RDL leads to significant prediction benefits on both in-distribution and out-of-district tests, especially for few-shot learning scenarios. |
| Approach: | They propose a rational-centric framework with human-in-the-loop to exploit spurious associations and bias models towards generally applicable underlying distributions. |
| Outcome: | The proposed framework leads to significant prediction benefits on in-distribution and out-of-district tests, compared to state-of the-art benchmarks. |
AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language Models (2022.naacl-main)
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| Challenge: | Existing methods for fine-tuning pre-trained language models ignore the potential of unlabeled data. |
| Approach: | They propose a framework that allows users to unleash the power of unlabeled data via self-training. |
| Outcome: | The proposed framework outperforms active learning and self-training baselines and improves the label efficiency of PLM fine-tuning by 56.2% on average. |
Contrastive Data and Learning for Natural Language Processing (2022.naacl-tutorials)
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| Challenge: | Current NLP models heavily rely on effective representation learning algorithms. |
| Approach: | This tutorial introduces contrastive learning and provides an introduction to the techniques. |
| Outcome: | This tutorial provides an introduction to the fundamentals of contrastive learning approaches and the theory behind them. |
COCO-DR: Combating Distribution Shift in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learning (2022.emnlp-main)
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| Challenge: | Using COCO-DR, we combat distribution shifts between source training tasks and target scenarios. |
| Approach: | They propose a method to combat distribution shifts between source training tasks and target scenarios by COtinuous COtrastive learning. |
| Outcome: | The proposed method outperforms existing models on BEIR and the giant GPT-3 embedding model with 500x more parameters. |
RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimization (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have impressive performance but face high computational costs and latency, limiting their deployment in resource-constrained settings. |
| Approach: | They propose a robust RAG framework for large language models via Margin-aware Preference Optimization to enhance the accuracy and reliability of SLMs. |
| Outcome: | The proposed framework surpasses state-of-the-art benchmarks on three open-domain question answering tasks. |
3DS: Medical Domain Adaptation of LLMs via Decomposed Difficulty-based Data Selection (2025.emnlp-main)
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Hongxin Ding, Yue Fang, Runchuan Zhu, Xinke Jiang, Jinyang Zhang, Yongxin Xu, Weibin Liao, Xu Chu, Junfeng Zhao, Yasha Wang
| Challenge: | Effective domain adaptation typically involves supervised fine-tuning on carefully selected instruction-tuned data. |
| Approach: | They propose a model-centric data selection framework that aligns data selection with the model’s knowledge distribution to improve model performance. |
| Outcome: | The proposed framework outperforms existing methods by up to 2.97% accuracy in the healthcare domain. |
Retrieval Augmented Fact Verification by Synthesizing Contrastive Arguments (2024.acl-long)
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| Challenge: | Existing methods to verify claim credibility rely on embedded knowledge or unreliable context. |
| Approach: | They propose retrieval augmented fact verification through the synthesis of contrasting arguments (RAFTS) they use an embedding model to identify informative demonstrations and in-context prompts to generate the prediction and explanation. |
| Outcome: | The proposed method outperforms existing methods with smaller LLMs or unreliable contexts. |
G-Transformer for Document-Level Machine Translation (2021.acl-long)
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| Challenge: | Existing work extends translation unit from single sentence to multiple sentences. |
| Approach: | They propose to introduce locality assumption as an inductive bias into Transformer and reduce the hypothesis space of attention from target to source. |
| Outcome: | The proposed model achieves state-of-the-art BLEU scores on three benchmark datasets. |
Dynamic Energy-Based Contrastive Learning with Multi-Stage Knowledge Verification for Event Causality Identification (2025.emnlp-main)
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| Challenge: | Existing methods for event causal identification rely on rule-based or random sampling strategies, which introduce spurious causal positives. |
| Approach: | They propose an ECI method enhanced by Dynamic Energy-based Contrastive Learning with multi-stage knowledge verification which generates high-quality contrastive samples and effectively suppresses spurious causal disturbances. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two benchmarks. |
RLET: A Reinforcement Learning Based Approach for Explainable QA with Entailment Trees (2022.emnlp-main)
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| Challenge: | Existing structured reasoning frameworks lack internal decision probability and cannot model the tree as a whole. |
| Approach: | They propose a Reinforcement Learning based Entailment Tree generation framework that is trained using the cumulative signals across the whole tree. |
| Outcome: | The proposed framework offers explicit deductions with entailment steps in a tree structure. |
Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences (2020.acl-main)
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| Challenge: | a monolingual speaker can learn to translate by looking up a bilingual dictionary . a novel task of machine translation (MT) is based on no parallel sentences but can refer to a ground-truth bilingual dictionary and large-scale monolingual corpora. |
| Approach: | They propose a task of machine translation that uses a bilingual dictionary and large-scale monolingual corpora to translate a monolingual speaker. |
| Outcome: | The proposed task is based on a bilingual dictionary and large scale monolingual corpora, while being independent on parallel sentences. |
EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records (2024.emnlp-main)
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Wenqi Shi, Ran Xu, Yuchen Zhuang, Yue Yu, Jieyu Zhang, Hang Wu, Yuanda Zhu, Joyce Ho, Carl Yang, May Dongmei Wang
| Challenge: | EHRAgent enables clinicians to interact with EHRs using natural language . reliance on rule-based conversion systems often necessitates additional training or effort from data engineers. |
| Approach: | They propose a large language model agent that generates and executes code in natural language to facilitate clinicians in directly interacting with EHRs. |
| Outcome: | The proposed agent outperforms the strongest baseline by up to 29.6% in success rate on three real-world EHR datasets. |
Two Local Models for Neural Constituent Parsing (C18-1)
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| Challenge: | Non-local features have been shown crucial for statistical parsing, but local models can give highly competitive accuracies thanks to the power of dense neural input representations. |
| Approach: | They propose to use local neural models for constituent parsing to capture dependencies between sub output structures and to exploit non-local features. |
| Outcome: | The proposed model achieves labeled bracketing F1 scores of 92.4% on PTB and 87.3% on CTB 5.1. |
Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus (2023.emnlp-main)
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Tianhang Zhang, Lin Qiu, Qipeng Guo, Cheng Deng, Yue Zhang, Zheng Zhang, Chenghu Zhou, Xinbing Wang, Luoyi Fu
| Challenge: | Existing methods for detecting hallucinations in LLMs rely on external knowledge for reference retrieval or require sampling multiple responses for consistency verification. |
| Approach: | They propose a reference-free, uncertainty-based method for detecting hallucinations in Large Language Models that imitates human focus in factuality checking from three aspects: focus on the most informative keywords; focus on unreliable tokens in historical context; focus of token properties such as token type and token frequency. |
| Outcome: | The proposed method achieves state-of-the-art performance across all evaluation metrics and eliminates the need for additional information. |
Direct Value Optimization: Improving Chain-of-Thought Reasoning in LLMs with Refined Values (2025.emnlp-main)
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| Challenge: | Existing offline preference optimization methods rely on preference labels to optimize large language models. |
| Approach: | They propose an offline method for enhancing large language models in reasoning tasks that utilizes value signals at individual reasoning steps. |
| Outcome: | The proposed framework outperforms offline preference optimization techniques by 4% to 6% on math reasoning, commonsense reasoning, and coding tasks. |
RefuteBench: Evaluating Refuting Instruction-Following for Large Language Models (2024.findings-acl)
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| Challenge: | The application scope of large language models (LLMs) is expanding . however, evaluating whether models can respond to user feedback has not been thoroughly analyzed. |
| Approach: | They propose a benchmark to assess whether large language models can respond to refuting feedback and adhere to user demands throughout the conversation. |
| Outcome: | The proposed benchmark covers tasks such as question answering, machine translation, and email writing. |
CogToM: A Comprehensive Theory of Mind Benchmark inspired by Human Cognition for Large Language Models (2026.acl-long)
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Haibo Tong, Zeyang Yue, Feifei Zhao, Erliang Lin, Lu Jia, Ruolin Chen, Yinqian Sun, Qian Zhang, Yi Zeng
| Challenge: | Existing benchmarks for Large Language Models (LLMs) are limited to false belief tasks, highlighting bottlenecks in specific dimensions. |
| Approach: | They propose a benchmark to evaluate Large Language Models' Theory of Mind capabilities . they evaluate 8000 bilingual instances across 46 paradigms and validated by 49 human annotators . |
| Outcome: | The proposed benchmark reveals performance heterogeneities and bottlenecks in 22 representative models. |
Mining Evidences for Concept Stock Recommendation (N18-1)
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| Challenge: | a recent announcement of a state plan to build a new economic region has led to the rise of hundreds of stocks . concepts can be useful for investors to find out relevant concept stocks for making investment decisions . a chinese research team uses deep learning to mine evidences from large textual data . |
| Approach: | They use distributed word similarities and deep reinforcement learning to learn a strategy of topic expansion from large scale textual data. |
| Outcome: | The proposed method outperforms a baseline method on two Chinese stock market datasets. |
PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning (2026.acl-long)
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Jingcheng Hu, Yinmin Zhang, Shijie Shang, Xiaobo Yang, Yue Peng, Zhewei Huang, Hebin Zhou, Xin Wu, Jie Cheng, Fanqi Wan, Xiangwen Kong, Chengyuan Yao, Kaiwen Yan, Ailin Huang, Hongyu Zhou, Qi Han, Zheng Ge, Xiangyu Zhang, Heung-Yeung Shum
| Challenge: | Parallel Coordinated Reasoning (PaCoRe) overcomes a central limitation of contemporary language models: their inability to scale test-time compute (TTC) far beyond sequential reasoning under a fixed context window. |
| Approach: | They propose a training-and-inference framework to overcome a central limitation of language models: their inability to scale test-time compute (TTC) under a fixed context window. |
| Outcome: | The proposed model scales to multi-million-token effective TTC without exceeding context limits. |
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. |
MasRouter: Learning to Route LLMs for Multi-Agent Systems (2025.acl-long)
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| Challenge: | Multi-agent systems (MAS) powered by Large Language Models (LLMs) have been demonstrated to push the boundaries of LLM capabilities, yet they often face significant costs and challenges in dynamic LLM selection. |
| Approach: | They propose a multi-agent system routing solution that integrates all components of MAS into a unified routing framework. |
| Outcome: | The proposed solution is high-performing, cost-effective, and efficient . it reduces overhead by up to 52.07 compared to current methods on HumanEval . |
Z-LaVI: Zero-Shot Language Solver Fueled by Visual Imagination (2022.emnlp-main)
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| Challenge: | Large-scale pretrained language models suffer from reporting bias, describing the lack of explicit commonsense knowledge in written text. |
| Approach: | They propose to endow language models with visual imagination capabilities by recalling existing images and synthesizing nonexistent images via text-to-image generation. |
| Outcome: | The proposed model improves the performance of existing language models across a diverse set of language tasks. |
ProgGen: Generating Named Entity Recognition Datasets Step-by-step with Self-Reflexive Large Language Models (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) exhibit remarkable adaptability across domains, but they are often not suitable for structured knowledge extraction tasks such as named entity recognition (NER). |
| Approach: | They propose a method that instructs LLMs to self-reflect on the specific domain and generates domain-relevant attributes for creating attribute-rich training data. |
| Outcome: | The proposed method produces NER datasets in domains with domain-relevant attributes and generates entity terms and NER context data around these entities. |
From Model-centered to Human-Centered: Revision Distance as a Metric for Text Evaluation in LLMs-based Applications (2024.findings-acl)
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| Challenge: | Existing evaluation metrics for large language models yield numerical scores that ignore user experience. |
| Approach: | They propose a metric that suggests revision edits that mimic the human writing process . their results show that the metric offers more insightful feedback and distinguishes between texts . |
| Outcome: | The proposed metric can provide a self-explained text evaluation result in a human-understandable manner beyond the context-independent score. |
ExploraCoder: Advancing Code Generation for Multiple Unseen APIs via Planning and Chained Exploration (2025.acl-long)
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| Challenge: | Large language models face intrinsic limitations in coding with unseen APIs in training corpora. |
| Approach: | They propose a training-free framework that empowers LLMs to invoke multiple unseen APIs in code solution by planning a complex problem into several API invocation subtasks and experimenting with correct API usage at intermediate steps. |
| Outcome: | The proposed framework significantly improves performance for models lacking prior API knowledge, achieving 11.99% over retrieval-based approaches and 17.28% over pretraining-based methods in pass@10. |
A Pilot Study for Chinese SQL Semantic Parsing (D19-1)
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| Challenge: | Existing datasets to map natural language text into SQL are limited in their use in question-to-sql mapping. |
| Approach: | They propose to use a Chinese-based semantic parser to map natural language text into SQL. |
| Outcome: | The proposed dataset compares a character-based parser with a word embedding scheme for Chinese . the results show that the parsers are subject to segmentation errors and cross-lingual embedders are useful for text-to-SQL mapping. |
DART: Leveraging Multi-Agent Disagreement for Tool Recruitment in Multimodal Reasoning (2026.eacl-long)
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Nithin Sivakumaran, Justin Chen, David Wan, Yue Zhang, Jaehong Yoon, Elias Stengel-Eskin, Mohit Bansal
| Challenge: | a key strength of human intelligence is the ability to debate and discuss reasoning with others. |
| Approach: | They propose a multi-agent framework that uses disagreements between visual agents to identify useful visual tools that can resolve inter-agency disagreement. |
| Outcome: | The proposed framework beats the strongest baseline on A-OKVQA and MMMU, respectively. |
Time-for-Accuracy: Formalizing Chain-of-Thought as an Expansion of Logical Depth (2026.findings-acl)
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| Challenge: | Chain-of-thought (CoT) prompting can improve multi-step reasoning, but it is unclear what kind of additional sequential computation longer traces actually enable. |
| Approach: | They propose a deletion-based measure of step necessity under a specified inference interface to operationalize realized depth beyond raw length. |
| Outcome: | The proposed method combines effective logical depth with Bennett's logical depth to show that it is more efficient than a linear model. |
LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey (2026.findings-acl)
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Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu, Jizhou Guo, Yankai Chen, Chunyu Miao, Hoang H Nguyen, Yue Zhou, Weizhi Zhang, Liancheng Fang, Hanrong Zhang, Fangxin Wang, Pengfei Zhang, Langzhou He, Yangning Li, Dongyuan Li, Renhe Jiang, Philip S. Yu
| Challenge: | Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents. |
| Approach: | They propose to integrate human-provided information, feedback, or control into the agent system to enhance system performance, reliability, and safety. |
| Outcome: | The proposed systems improve system performance, reliability, and safety by integrating human-provided information, feedback, or control into the agent system. |
Query-Aware Graph Attention for Precise Subgraph Retrieval in Knowledge-Augmented Reasoning (2026.findings-acl)
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| Challenge: | Existing Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) systems insufficiently model the interaction between query semantics and relation types, resulting in imprecise subgraph retrieval and unstable reasoning. |
| Approach: | They propose a retrieval framework that integrates query semantics and relation embeddings directly into the attention mechanism. |
| Outcome: | Experiments on WebQSP and CWQ establish new state-of-the-art results in both Triple Recall and Answer Recall. |
Intent-Aware and Hate-Mitigating Counterspeech Generation via Dual-Discriminator Guided LLMs (2024.lrec-main)
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| Challenge: | Hate speech is an aggressive expression that incites hatred towards specific groups based on their group identity. |
| Approach: | They propose an LLMs-based framework for counterspeech generation that uses intent-aware discriminators to decode intents of LLM models. |
| Outcome: | The proposed framework matches intents with hate mitigation intents and performs well. |
HyKGE: A Hypothesis Knowledge Graph Enhanced RAG Framework for Accurate and Reliable Medical LLMs Responses (2025.acl-long)
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Xinke Jiang, Ruizhe Zhang, Yongxin Xu, Rihong Qiu, Yue Fang, Zhiyuan Wang, Jinyi Tang, Hongxin Ding, Xu Chu, Junfeng Zhao, Yasha Wang
| Challenge: | Recent approaches suffer from insufficient and repetitive knowledge retrieval, tedious and time-consuming query parsing, and monotonous knowledge utilization. |
| Approach: | They propose a retrieval-augmented generation framework which leverages LLMs’ powerful reasoning capacity to compensate for the incompleteness of user queries. |
| Outcome: | The proposed framework improves the accuracy and reliability of Large Language Models (LLMs) by combining the rich knowledge of LLMs with Hypothesis Outputs. |
Vision-and-Language Navigation with Analogical Textual Descriptions in LLMs (2025.emnlp-main)
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| Challenge: | Existing zero-shot LLM-based Vision-and-Language Navigation agents either encode images as textual scene descriptions, potentially oversimplifying visual details, or process raw image inputs, which can fail to capture abstract semantics required for high-level reasoning. |
| Approach: | They propose to integrate large language models into embodied AI models by incorporating textual descriptions that facilitate analogical reasoning across images from multiple perspectives. |
| Outcome: | The proposed approach improves the agent’s contextual understanding on the R2R dataset, showing that it can make better decisions based on the LLMs. |
Out-of-Distribution Generalization in Natural Language Processing: Past, Present, and Future (2023.emnlp-main)
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Linyi Yang, Yaoxian Song, Xuan Ren, Chenyang Lyu, Yidong Wang, Jingming Zhuo, Lingqiao Liu, Jindong Wang, Jennifer Foster, Yue Zhang
| Challenge: | Existing literature on the generalization of machine learning models to out-of-distribution data is lacking. |
| Approach: | They propose to present the first comprehensive review of recent progress, methods, and evaluations on the generalization challenge from an OOD perspective in natural language understanding. |
| Outcome: | The proposed survey provides the first comprehensive review of recent progress, methods, and evaluations on the generalization challenge from an OOD perspective in natural language understanding. |
Entity Enhanced BERT Pre-training for Chinese NER (2020.emnlp-main)
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| Challenge: | Character-level BERT pre-trained in Chinese suffers from lacking lexicon information, which shows effectiveness for Chinese NER. |
| Approach: | They propose a semi-supervised method to integrate lexicon into pre-trained LMs in Chinese . they extract an entity lexiconal from raw text and integrate it into BERT . |
| Outcome: | The proposed method is highly effective and achieves the best results on a news dataset and two datasets annotated by the authors. |
Investigating Non-local Features for Neural Constituency Parsing (2022.acl-long)
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| Challenge: | Constituency parsers have been able to achieve competitive performance by using local features. |
| Approach: | They propose to inject non-local features into the training process of a local span-based parser by predicting constituent n-gram non-local patterns and ensuring consistency between constituents and local constituents. |
| Outcome: | The proposed method outperforms the self-attentive parser in multi-lingual and zero-shot cross-domain settings. |
Knowledge Enhanced Fine-Tuning for Better Handling Unseen Entities in Dialogue Generation (2021.emnlp-main)
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| Challenge: | Existing methods for dialogue generation use an external knowledge base to generate appropriate responses. |
| Approach: | They propose to use an external knowledge base to generate appropriate responses for unseen entities. |
| Outcome: | Experiments on two dialogue corpus show that pre-trained models perform poorly with unseen entities. |
Teaching Large Language Models to Translate on Low-resource Languages with Textbook Prompting (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive results in Machine Translation by following instructions, even without training on parallel data. |
| Approach: | They propose a Translate After LEarNing Textbook approach which aims to enhance LLMs’ ability to translate low-resource languages by learning from a textbook. |
| Outcome: | The proposed approach improves translation performance by 14.8% using 112 low-resource languages from FLORES-200 with two LLMs: ChatGPT and BLOOMZ. |
DIDS: Domain Impact-aware Data Sampling for Large Language Model Training (2025.emnlp-main)
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Weijie Shi, Jipeng Zhang, Yaguang Wu, Jingzhi Fang, Shibo Zhang, Yao Zhao, Hao Chen, Ruiyuan Zhang, Yue Cui, Jia Zhu, Sirui Han, Jiajie Xu, Xiaofang Zhou
| Challenge: | Existing approaches for optimizing domain-level sampling strategies struggle with maintaining intra-domain consistency and accurately measuring domain impact. |
| Approach: | They propose to use a Fisher-Information Matrix-guided metric to measure domain impact to ensure intra-domain consistency and accuracy. |
| Outcome: | The proposed model achieves 3.4% higher average performance while maintaining comparable training efficiency. |
HiGen: Hierarchy-Aware Sequence Generation for Hierarchical Text Classification (2024.eacl-long)
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| Challenge: | Hierarchical text classification is a complex subtask under multi-label text classification . the relevance of document sections can vary based on the hierarchy level, necessitating a dynamic document representation. |
| Approach: | They propose a text-generation-based framework that uses language models to encode dynamic text representations. |
| Outcome: | The proposed framework surpasses existing methods while handling data and mitigating class imbalance. |
From Selection to Generation: A Survey of LLM-based Active Learning (2025.acl-long)
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Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie, Junda Wu, Xintong Li, Ryan Aponte, Hanjia Lyu, Joe Barrow, Hongjie Chen, Franck Dernoncourt, Branislav Kveton, Tong Yu, Ruiyi Zhang, Jiuxiang Gu, Nesreen K. Ahmed, Yu Wang, Xiang Chen, Hanieh Deilamsalehy, Sungchul Kim, Zhengmian Hu, Yue Zhao, Nedim Lipka, Seunghyun Yoon, Ting-Hao Kenneth Huang, Zichao Wang, Puneet Mathur, Soumyabrata Pal, Koyel Mukherjee, Zhehao Zhang, Namyong Park, Thien Huu Nguyen, Jiebo Luo, Ryan A. Rossi, Julian McAuley
| Challenge: | Large Language Models (LLMs) have been used for selection and training of data for active learning. |
| Approach: | They propose an intuitive taxonomy that categorizes LLM-based active learning techniques and discuss the transformative roles they can play in the active learning loop. |
| Outcome: | The proposed model can generate entirely new data instances and provide more cost-effective annotations with fewer labeled data instances. |
Video-MMMU: Evaluating Knowledge Acquisition from Multidisciplinary Professional Videos (2026.acl-long)
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| Challenge: | Existing video benchmarks do not evaluate the knowledge acquisition capabilities of Large Multimodal Models (LMMs) existing video benchmark focuses on static, general visual understanding tasks, without evaluating whether models can acquire knowledge dynamically. |
| Approach: | They propose a multi-modal, multi-discipline, multitrack benchmark that evaluates Large Multimodal Models’ ability to acquire knowledge from college-level, educational videos. |
| Outcome: | The proposed benchmark reveals a substantial gap between human learners and current Large Multimodal Models (LMMs) and focuses on improving their learning efficiency. |
Towards Better Graph-based Cross-document Relation Extraction via Non-bridge Entity Enhancement and Prediction Debiasing (2024.findings-acl)
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| Challenge: | Existing studies on relation extraction ignore non-bridge entities, leading to bias during inference. |
| Approach: | They propose a graph-based cross-document Relation Extraction model with non-bridge entity enhancement and prediction debiasing that integrates non-cross entities with target entities and bridge entities. |
| Outcome: | The proposed model outperforms baseline models on open and closed datasets. |
Lost in Literalism: How Supervised Training Shapes Translationese in LLMs (2025.acl-long)
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| Challenge: | Large language models exhibit translationese errors and generate unexpected unnatural translations . Neural machine translation (NMT) has become the dominant method in machine translation research . |
| Approach: | They evaluate the prevalence of translationese in LLM-generated translations and investigate its roots during supervised fine-tuning. |
| Outcome: | The proposed methods reduce translationese while improving translation naturalness . the proposed methods are validated by human evaluations and automatic metrics . |
Non-Autoregressive Document-Level Machine Translation (2023.findings-emnlp)
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| Challenge: | Existing non-autoregressive translation models struggle with document context and handling discourse phenomena. |
| Approach: | They propose a simple but effective design of sentence alignment between source and target to improve their performance on document-level machine translation. |
| Outcome: | The proposed model achieves high acceleration on documents and sentence alignment significantly enhances their performance. |
Beyond Single-View Detection: A Dual-Space Reasoning Framework for Interpretable Harmful Meme Understanding (2026.acl-long)
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| Challenge: | Existing methods for identifying harmful memes rely on modal alignment or black-box classifiers . BPDMoE-Hate provides visual explanations for viewpoint selection and hierarchical structuring . |
| Approach: | They propose a framework that conceptualizes harmful meme detection as a process of "viewpoint decoupling and hierarchical fusion" they propose BPDMoE-Hate, which generates adversarial binary perspectives via VLMs and incorporates an adaptive viewpoint gating to facilitate viewpoint selection. |
| Outcome: | The proposed framework surpasses existing methods in performance and provides visual explanations for viewpoint selection and hierarchical structuring. |
Cold-Start Data Selection for Better Few-shot Language Model Fine-tuning: A Prompt-based Uncertainty Propagation Approach (2023.acl-long)
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| Challenge: | Pre-trained language models (PLMs) have achieved competitive performance with limited labeled data for many NLP tasks. |
| Approach: | They propose a prompt-based data selection method for pre-trained language models fine-tuning under cold-start scenarios. |
| Outcome: | The proposed method outperforms the strongest cold-start data selection baselines on six text classification datasets with 128 labels. |
Visual Goal-Step Inference using wikiHow (2021.emnlp-main)
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| Challenge: | Past work in NLP examined the task of goal-step inference for textual goals . wikiHow dataset shows that goal-step inference is challenging for state-of-the-art models . |
| Approach: | They propose a task where a model is given a textual goal and must choose which of four images represents a plausible step towards that goal. |
| Outcome: | The proposed task is challenging for state-of-the-art multimodal models and can be transferred to other datasets. |
LongVideoAgent: Multi-Agent Reasoning with Long Videos (2026.acl-long)
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| Challenge: | a key emerging challenge is robust long video understanding, authors say . current methods compress content into lossy summaries or rely on limited toolsets . |
| Approach: | They propose a multi-agent framework where a master LLM coordinates a grounding agent and a vision agent to extract targeted textual observations. |
| Outcome: | The proposed model outperforms strong non-agent baselines on episode-level datasets . the proposed model significantly outperformed existing models on other datasets. |
A Unified Supervised and Unsupervised Dialogue Topic Segmentation Framework Based on Utterance Pair Modeling (2025.naacl-long)
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| Challenge: | Unsupervised methods for dialogue topic segmentation are difficult to surpass due to short sentences, serious references and non-standard language. |
| Approach: | They propose a method to divide a dialogue into different topic paragraphs to better understand its structure and content. |
| Outcome: | The proposed method achieves the best results on multiple benchmark datasets across different scenarios. |
AlignBench: Benchmarking Chinese Alignment of Large Language Models (2024.acl-long)
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Xiao Liu, Xuanyu Lei, Shengyuan Wang, Yue Huang, Andrew Feng, Bosi Wen, Jiale Cheng, Pei Ke, Yifan Xu, Weng Lam Tam, Xiaohan Zhang, Lichao Sun, Xiaotao Gu, Hongning Wang, Jing Zhang, Minlie Huang, Yuxiao Dong, Jie Tang
| Challenge: | Effective evaluation of alignment for emerging Chinese LLMs is still significantly lacking, calling for real-scenario grounded, open-ended, challenging and automatic evaluations tailored for alignment. |
| Approach: | They propose a multi-dimensional benchmark for evaluating LLMs’ alignment in Chinese with 8 main categories, 683 real-scenario rooted queries and corresponding human verified references. |
| Outcome: | The benchmark uses a human-in-the-loop data curation pipeline, 683 real-scenario rooted queries and human verified references. |
Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis (2021.acl-long)
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| Challenge: | Existing approaches to improve performance of deep neural models are limited by the nature of spurious patterns in the data. |
| Approach: | They propose to use augmented data to generate spurious patterns in NLP models . they propose to generate counterfactual data for data augmentation and explanation . |
| Outcome: | The proposed approach improves performance on augmented data and on human-generated data. |
Which Sense Dominates Multisensory Semantic Understanding? A Brain Decoding Study (2024.lrec-main)
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| Challenge: | Decoding semantic meanings from brain activity is open to multisensory stimulation, as word meanings can be delivered by both auditory and visual inputs. |
| Approach: | They aim to develop a computational model to probing what information from the act of language understanding is represented in human brain. |
| Outcome: | The proposed model dissociates multisensory integration of word understanding into written text, spoken text and image perception respectively, exploring the decoding efficiency and reliability of unisensory information in the brain representation. |
SalaMAnder: Shapley-based Mathematical Expression Attribution and Metric for Chain-of-Thought Reasoning (2025.findings-emnlp)
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Yue Xin, Chen Shen, Shaotian Yan, Xiaosong Yuan, Yaoming Wang, Xiaofeng Zhang, Chenxi Huang, Jieping Ye
| Challenge: | Chain-of-Thought prompting improves the math reasoning capability of large language models. |
| Approach: | They propose a method for attribution of component-level contributions in CoT reasoning using Shapley value and a stratified sampling algorithm that significantly reduces computational complexity. |
| Outcome: | The proposed method reduces computational complexity and provides robust correlations with model performance. |
RHGN: Relation-gated Heterogeneous Graph Network for Entity Alignment in Knowledge Graphs (2023.findings-acl)
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| Challenge: | Existing methods for entity alignment fail to account for heterogeneity among KGs and distinction between KG entities and relations. |
| Approach: | They propose a Relation-gated Heterogeneous Graph Network (RHGN) that uses a relation-gate based convolutional layer to distinguish relations and entities in the KG. |
| Outcome: | Extensive experiments on four datasets show that the proposed method is superior to state-of-the-art methods. |
Musical Score Understanding Benchmark: Evaluating Large Language Models’ Comprehension of Complete Musical Scores (2026.acl-long)
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Congren Dai, Yue Yang, Krinos Li, Huichi Zhou, Shijie Liang, Zhang Bo, Enyang Liu, Ge Jin, Hongran An, Haosen Zhang, Peiyuan Jing, KinHei Lee, Zhenxuan Zhang, Xiaobing Li, Maosong Sun
| Challenge: | Existing benchmarks for musical score understanding are narrow in scope, focusing on isolated fragments, short excerpts, or multiple-choice formulations, rather than supporting holistic reasoning over entire scores. |
| Approach: | They propose a benchmark for score-level musical understanding across textual and visual modalities. |
| Outcome: | The musical score understanding benchmark contains 1,800 question-answer pairs from works by Bach, Beethoven, Chopin, Debussy, and others. |
On Compositional Generalization of Neural Machine Translation (2021.acl-long)
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| Challenge: | Modern neural machine translation models have shown competitive performance in benchmarks such as WMT, but there are significant issues such as robustness, domain generalization, etc. |
| Approach: | They propose a benchmark dataset for NMT models from the perspective of compositional generalization and quantitatively analyze the results. |
| Outcome: | The proposed model performs well under traditional metrics, but is low in out-of-domain and low-resource conditions. |
Gradient-Guided Multi-Judge Prompt Optimization (2026.acl-long)
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ChenZhuo Zhao, Xinda Wang, Pu Zhao, Yue Huang, Junting Lu, Ziqian Liu, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Existing approaches to prompt optimization trade off signal quality against computational cost. |
| Approach: | They propose a framework that uses a first-order gradient approximation to score segment importance in a continuous masking direction. |
| Outcome: | The proposed framework improves efficiency and robustness by using a first-order gradient approximation to score segment importance in a continuous masking direction. |
GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-Distribution Generalization Perspective (2023.findings-acl)
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Linyi Yang, Shuibai Zhang, Libo Qin, Yafu Li, Yidong Wang, Hanmeng Liu, Jindong Wang, Xing Xie, Yue Zhang
| Challenge: | Pre-trained language models (PLMs) have improved generalization performance but the out-of-distribution (OOD) generalization problem remains a challenge in many NLP tasks. |
| Approach: | They propose to create a benchmark for evaluating out-of-distribution (OOD) generalization in NLP models. |
| Outcome: | The proposed benchmarks highlight the importance of OOD robustness and provide insights on how to measure it and improve it. |
MuTual: A Dataset for Multi-Turn Dialogue Reasoning (2020.acl-main)
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| Challenge: | Existing non-task oriented dialogue systems can yield a relevant and fluent response, but sometimes make logical mistakes because of weak reasoning capabilities. |
| Approach: | They propose a dataset for multi-turn dialogue reasoning that uses annotated dialogues to train a machine to handle various reasoning problems. |
| Outcome: | Empirical results show that state-of-the-art methods only reach 71%, far behind human performance of 94%. |
End-to-End AMR Coreference Resolution (2021.acl-long)
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| Challenge: | Existing work on AMR focuses on individual sentences, but there is a need for multi-sentence AMRs. |
| Approach: | They propose to use an end-to-end AMR coreference resolution model to generate multi-sentence AMRs. |
| Outcome: | The proposed model reduces error propagation and is more robust for both in- and out-domain situations. |
RECALL: REpresentation-aligned Catastrophic-forgetting ALLeviation via Hierarchical Model Merging (2025.emnlp-main)
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Bowen Wang, Haiyuan Wan, Liwen Shi, Chen Yang, Peng He, Yue Ma, Haochen Han, Wenhao Li, Tiao Tan, Yongjian Li, Fangming Liu, Gong Yifan, Sheng Zhang
| Challenge: | Existing models that require task labels or performance trade-offs are susceptible to catastrophic forgetting. |
| Approach: | They propose a representation-aware model merging framework for continual learning without access to historical data. |
| Outcome: | The proposed framework outperforms baselines in knowledge retention and generalization across five NLP tasks and multiple continual learning scenarios. |
Human-in-the-loop Robotic Grasping Using BERT Scene Representation (2022.coling-1)
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| Challenge: | Existing approaches for robotic grasping in cluttered scenes are expensive and lack structure information. |
| Approach: | They propose a human-in-the-loop framework for robotic grasping in cluttered scenes . they substitute scene-graph representation with a text representation of the scene using BERT . |
| Outcome: | The proposed framework outperforms object-agnostic and scene-graph based methods on robots and physical robots. |
Memory-Based Invariance Learning for Out-of-Domain Text Classification (2023.emnlp-main)
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| Challenge: | Existing approaches to learning invariant representations rely on the assumption that training and test sets come from the same domain. |
| Approach: | They propose to extend a classification model trained on multiple source domains to an unseen target domain by using key-value memory. |
| Outcome: | The proposed method improves on sentiment analysis and natural language inference tasks. |
On Commonsense Cues in BERT for Solving Commonsense Tasks (2021.findings-acl)
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| Challenge: | Pre-trained language models can capture syntactic features, semantic information and factual knowledge, but structured commonsense knowledge is not captured well. |
| Approach: | They quantitatively investigate the presence of structural commonsense cues in BERT when solving commonsensense tasks and the importance of such cue for the model prediction. |
| Outcome: | The presence of commonsense knowledge is positively correlated to the model accuracy. |
Enhancing Event Causality Identification with LLM Knowledge and Concept-Level Event Relations (2025.coling-main)
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| Challenge: | Existing methods to identify causal relationships between events often overlook the dependencies between similar events. |
| Approach: | They propose an ECI method enhanced by LLM Knowledge and Concept-Level Event Relations (LKCER) the method constructs a conceptual-level heterogeneous event graph by leveraging local contextual information of related event mentions. |
| Outcome: | The proposed method outperforms previous state-of-the-art methods on both benchmarks, EventStoryLine and Causal-TimeBank. |
Data-Efficient Selection via Grammatical Complexity in Continual Pre-training of Domain-Specific LLMs (2025.emnlp-main)
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Yizhou Ying, Geng Zhang, Cui Danxin, Chengyu Du, Guanglei Yue, Sihang Jiang, Jiaqing Liang, Yifei Fu, Hailin Hu, Yanghua Xiao
| Challenge: | Existing data selection strategies for continual pre-training of large language models often rely on scarce labeled data or computationally expensive LLMs. |
| Approach: | They propose an annotation-independent data selection framework for CPT that evaluates grammatical complexity using lexical diversity and syntactic complexity. |
| Outcome: | The proposed framework outperforms baselines on a financial dataset and surpasses full-data training by 1.7% using only 20% of the data. |
Beyond Query Memorization: Large Language Model Routing with Query Decomposition and Historical Matching (2026.acl-long)
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Bo Lv, Jingbo Sun, Jianwei Lv, Chen Tang, Shaojie Zhang, Nayu Liu, Guoxin Yu, Zihao Li, Qichao Zhang, Dongbin Zhao, Ping Luo, Yue Yu
| Challenge: | Existing routing methods rely on direct mapping from queries to models based on surface-level features, leading to poor generalizability on out-of-distribution data. |
| Approach: | They propose a new routing framework that recasts the routing task as a matching process of sifting similar queries from historical logs. |
| Outcome: | The proposed framework improves matching accuracy while lowering inference costs . it decouples linguistic surface forms from task-intrinsic requirements . |
Inducing Target-Specific Latent Structures for Aspect Sentiment Classification (2020.emnlp-main)
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| Challenge: | Aspect-level sentiment analysis aims to classify the sentiment polarity of an aspect or a target in a comment . graph convolutional networks can be used to classifice aspect terms in syllables . |
| Approach: | They propose to combine word dependency graphs and latent graphs to create latent models . they propose to model the interaction between the aspect and its surrounding contexts . |
| Outcome: | The proposed model can complement syntactic features with latent semantic dependencies. |
Leibniz: Theory-of-Mind Driven Neuro-Symbolic Logical Reasoning via Multi-Agent Collaboration (2026.acl-long)
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| Challenge: | Existing methods for logical reasoning with large language models suffer from insufficient rule semantic grounding and weak rule application mechanisms. |
| Approach: | They propose a theory-of-mind driven neuro-symbolic reasoning framework that integrates natural language and symbolic representations throughout the reasoning process. |
| Outcome: | The proposed model surpasses state-of-the-art models in reasoning accuracy and flexibility. |
I²B-LPO: Latent Policy Optimization via Iterative Information Bottleneck (2026.acl-long)
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Huilin Deng, Hongchen Luo, Yue Zhu, Long Li, Zhuoyue Chen, Xinghao Zhao, Ming LI, Chuyang Zhao, Jihai Zhang, MengChang Wang, Yang Cao, Yu Kang
| Challenge: | Existing methods for large language model reasoning suffer from exploration collapse due to the semantic homogeneity of random rollouts. |
| Approach: | They propose to use latent policy optimization via iterative information bottleneck to optimize reasoning trajectories by diversifying reasoning . |
| Outcome: | Empirical results show that the proposed method achieves state-of-the-art performance with margins of up to 5.3% in accuracy and 7.4% in diversity metrics. |
SudokuFill: A Multi-Agent Progressive Filling Framework for Document-Level Scientific Information Extraction (2026.findings-acl)
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Yang Li, Yajiao Wang, Yu Zhang, Yuanzhe Zhang, Maodi Hu, Mengting Zhang, Xi Sun, Hua Yue, Zhixiong Zhang
| Challenge: | Scientific information extraction (SciIE) is a key bottleneck for turning unstructured papers into computable knowledge bases. |
| Approach: | They propose a scientific information extraction framework that solves a Sudoku problem as a progressive filling problem. |
| Outcome: | The proposed framework outperforms the GPT-4o model on a document-level adjuvant dataset. |
CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)
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Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan
| Challenge: | Existing language evaluation benchmarks for English are limited to English . lack of such benchmarks makes it difficult to replicate success in other languages . |
| Approach: | They introduce a large-scale Chinese language understanding evaluation benchmark . the benchmark uses a set of current state-of-the-art pre-trained Chinese models . |
| Outcome: | The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is released . the benchmark evaluates models across a wide range of tasks on original Chinese text . existing language evaluation benchmarks are mostly limited to English . |
Aerial Vision-and-Dialog Navigation (2023.findings-acl)
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| Challenge: | Aerial visionand-dialling navigation (AVDN) is a new approach to autonomous drones that can converse with humans and follow natural language commands to complete tasks. |
| Approach: | They propose to use Aerial Visionand-Dialog Navigation (AVDN) to navigate a drone via natural language conversation by collecting a dataset of over 3k recorded navigation trajectories with asynchronous human-human dialogs between commanders and followers. |
| Outcome: | The proposed system can converse with humans and follow natural language commands to fly to the expected destination. |
Representation Purification for End-to-End Speech Translation (2025.coling-main)
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| Challenge: | Existing approaches to enhance speech translation focus on enhancing knowledge transfer . factors in speech that are not relevant to translation content, such as timbre and rhythm, often limit the efficiency of knowledge transfer. |
| Approach: | They propose a framework that excludes content-agnostic perturbations from speech representations to mitigate their negative impact on ST. |
| Outcome: | The proposed framework significantly improves translation performance across all translation directions in three settings and achieves preeminent performance under a *transcript-free* setting. |
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. |
Quantifying the Impact of Structured Output Format on Large Language Models through Causal Inference (2026.findings-eacl)
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| Challenge: | Prior studies have examined the impact of structured output on LLMs’ generation quality, often presenting one-way findings. |
| Approach: | They propose to derive five potential causal structures characterizing the influence of structured output on LLMs’ generation using one assumed and two guaranteed constraints. |
| Outcome: | The proposed pipeline can be extended to other modules and is not limited to structured output but can be used in industrial applications. |
NCRF++: An Open-source Neural Sequence Labeling Toolkit (P18-4)
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| Challenge: | Existing statistical approaches to neural sequence labeling have been used for many tasks. |
| Approach: | They describe a toolkit for neural sequence labeling that provides a CRF inference layer for quick implementation. |
| Outcome: | The toolkit is based on PyTorch and can be run on GPUs. |
AdaSwitch: Adaptive Switching between Small and Large Agents for Effective Cloud-Local Collaborative Learning (2024.emnlp-main)
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Hao Sun, Jiayi Wu, Hengyi Cai, Xiaochi Wei, Yue Feng, Bo Wang, Shuaiqiang Wang, Yan Zhang, Dawei Yin
| Challenge: | Recent advances in large language models have been remarkable . users face a choice between using cloud-based LLMs for generation quality or local-based ones for lower computational cost . |
| Approach: | They propose a new LLM utilization paradigm that facilitates collaborative operation . they evaluate AdaSwitch across 7 benchmarks and compare it to other LLMs . |
| Outcome: | The proposed model improves performance of local and cloud agents across 7 benchmarks . it achieves competitive results compared to the cloud agent while utilizing less computational overhead. |
Routing with Generated Data: Annotation-Free LLM Skill Estimation and Expert Selection (2026.acl-long)
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Tianyi Niu, Justin Chen, Genta Indra Winata, Shi-Xiong Zhang, Supriyo Chakraborty, Sambit Sahu, Yue Zhang, Elias Stengel-Eskin, Mohit Bansal
| Challenge: | Existing approaches typically assume access to ground-truth labeled data . Existing methods require a classifier to select models given an input . |
| Approach: | They propose a routing setting where routers are trained exclusively on generated queries and answers from LLMs. |
| Outcome: | The proposed router outperforms the best query-answer router by 4.6% absolute accuracy when trained on weak generator data. |
Exploiting Sentiment and Common Sense for Zero-shot Stance Detection (2022.coling-1)
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| Challenge: | Existing stance detection models use sentiment and commonsense knowledge to classify stance toward documents and topics . obtaining rich annotated data in stance detector is time-consuming and laborintensive . |
| Approach: | They propose to use sentiment and commonsense knowledge to boost transferability of stance detection model by using sentiment and similar knowledge. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on the zero-shot and few-shot benchmark datasets. |
ZeroStance: Leveraging ChatGPT for Open-Domain Stance Detection via Dataset Generation (2024.findings-acl)
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| Challenge: | Until recently, zero-shot stance detection was limited to in-domain tasks. |
| Approach: | They propose a method for stance detection that trains a model that can generalize well to unseen targets across multiple domains. |
| Outcome: | The proposed method generalizes well to unseen targets across multiple domains over baselines on most benchmarks. |
LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models (2026.acl-long)
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Ming Zhang, Yujiong Shen, Jingyi Deng, Yuhui Wang, Huayu Sha, Kexin Tan, Qiyuan Peng, Yue Zhang, Junzhe Wang, Shichun Liu, Yueyuan Huang, Jingqi Tong, Changhao Jiang, Yilong Wu, Zhihao Zhang, Mingqi Wu, Mingxu Chai, Zhiheng Xi, Shihan Dou, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing evaluation of Large Language Models on static benchmarks is vulnerable to data contamination and leaderboard overfitting. |
| Approach: | LLMEval-Fair framework provides a framework for dynamic evaluation of Large Language Models . evaluators use a proprietary bank of 220k graduate-level questions to analyze model data . |
| Outcome: | LLMEval-Fair provides robust and credible evaluation framework for Large Language Models . it provides a strong empirical validation for the dynamic evaluation paradigm . |
FedPETuning: When Federated Learning Meets the Parameter-Efficient Tuning Methods of Pre-trained Language Models (2023.findings-acl)
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| Challenge: | Existing research on federated learning (FL) for pre-trained language models (PLMs) with increasing concerns about data privacy, enterprises or institutions are not allowed to collect data from end devices or local clients to a centralized server for fine-tuning PLMs. |
| Approach: | They investigate the parameter-efficient tuning of pre-trained language models (PLMs) and develop a federated benchmark for four representative PETuning methods . |
| Outcome: | The proposed method can defend against privacy attacks and maintain acceptable performance with reducing heavy resource consumption. |
Beyond Single-Value Metrics: Evaluating and Enhancing LLM Unlearning with Cognitive Diagnosis (2025.findings-acl)
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Yicheng Lang, Kehan Guo, Yue Huang, Yujun Zhou, Haomin Zhuang, Tianyu Yang, Yao Su, Xiangliang Zhang
| Challenge: | Large Language Models (LLMs) have been used to remove harmful knowledge and undesirable capabilities. |
| Approach: | They propose a framework that leverages Cognitive Diagnosis Modeling to evaluate LLM unlearning. |
| Outcome: | The proposed framework enhances evaluation and facilitates removal of harmful abilities. |
Suggest-Verify-Revise: A Three-Stage Document-Level Event Causality Identification with Narrative Consistency (2026.acl-long)
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| Challenge: | Existing methods for document-level Event Causality Identification rely on local semantic similarity for independent event-pair discrimination . Existing approaches ignore the influence of the overall narrative backbone in the propagation of causal dependencies and the role differentiation of events within multi-cause/multi-effect structures. |
| Approach: | They propose a suggest-verify-revise approach for document-level Event Causality Identification with narrative consistency (SVRECI) they integrate heuristic causal suggestions generated by an LLM with structural suggestions derived from hypergraph modeling . |
| Outcome: | The proposed approach outperforms existing methods on event-storylines and Causal-TimeBank datasets. |
Keys to Robust Edits: From Theoretical Insights to Practical Advances (2025.acl-long)
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| Challenge: | Existing methods for modifying parametric memory are prone to inaccuracies due to conflicting or outdated information. |
| Approach: | They propose a plug-and-play module that disentangles editing keys from native model representations and dynamically adjusts keys via contrastive learning to achieve robustness-specificity balance. |
| Outcome: | The proposed method improves over robustness tests by up to 66.4% while maintaining the success rate unaffected. |
Opinion Tree Parsing for Aspect-based Sentiment Analysis (2023.findings-acl)
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| Challenge: | Existing generative models for aspect-based sentiment analysis lack structure well-formedness guarantees and built-in elements alignments. |
| Approach: | They propose an opinion tree parsing model which parses all sentiment elements from an opinion-tree. |
| Outcome: | The proposed model is much faster than previous models and can explore correlations among sentiment elements. |
Template-Based Named Entity Recognition Using BART (2021.findings-acl)
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| Challenge: | Existing methods for fewshot NER do not make full use of knowledge transfer in NER model parameters. |
| Approach: | They propose a template-based method for NER that treats NER as a language model ranking problem in a sequence-to-sequence framework. |
| Outcome: | The proposed method achieves 92.55% F1 score on the CoNLL03 task and significantly better than fine-tuning BERT 10.88%, 15.34%, and 11.73% F1 scores on the MIT Movie, the ATIS, and the MATLAB task. |
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 . |
Reasoning is All You Need for Video Generalization: A Counterfactual Benchmark with Sub-question Evaluation (2025.findings-acl)
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Qiji Zhou, YiFan Gong, Guangsheng Bao, Hongjie Qiu, Jinqiang Li, Xiangrong Zhu, Huajian Zhang, Yue Zhang
| Challenge: | Existing multimodal benchmarks often overlook counterfactual reasoning, which is crucial for robust video understanding. |
| Approach: | They propose a multidimensional multimodal benchmark that systematically evaluates MLLMs across the abstract-concrete and perception-cognition dimensions. |
| Outcome: | The proposed model decomposes complex queries into structured sub-questions, enabling fine-grained reasoning analysis. |
MovieUN: A Dataset for Movie Understanding and Narrating (2022.findings-emnlp)
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| Challenge: | Automatic movie narration generation and narration grounding are important to provide a true movie experience for the blind and visually impaired. |
| Approach: | They propose to use movie clips as a benchmark to support automatic movie narration generation and narration grounding tasks. |
| Outcome: | The proposed methods are effective in supporting two movie-based tasks for the blind and visually impaired. |
Prompt-based Distribution Alignment for Domain Generalization in Text Classification (2022.emnlp-main)
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| Challenge: | Pretrained language models (PLMs) have achieved competitive performance on a range of NLP tasks. |
| Approach: | They propose to learn distributional invariance across source domains via alignment regularization loss functions to improve domain generalization by prompting. |
| Outcome: | Experiments on sentiment analysis and natural language inference show the effectiveness of the proposed method and achieve state-of-the-art results. |
The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG) (2024.findings-acl)
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Shenglai Zeng, Jiankun Zhang, Pengfei He, Yiding Liu, Yue Xing, Han Xu, Jie Ren, Yi Chang, Shuaiqiang Wang, Dawei Yin, Jiliang Tang
| Challenge: | Retrieval-augmented generation (RAG) is a powerful technique to facilitate language model generation with proprietary and private data, where data privacy is . a privacy issue that is currently under-explored, is posed by RAG. |
| Approach: | They propose to use retrieval-augmented generation (RAG) to facilitate language model generation with proprietary and private data where data privacy is a pivotal concern. |
| Outcome: | The proposed attack methods demonstrate that RAG can mitigate the old risks, i.e., leakage of the LLMs’ training data. |
Spotting AI’s Touch: Identifying LLM-Paraphrased Spans in Text (2024.findings-acl)
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| Challenge: | Existing work focuses on detecting (partially) AI-generated texts, but paraphrasing is commonly employed in various application scenarios for text refinement and diversity. |
| Approach: | They propose a framework for paraphrased text span detection that takes in the full text and assigns each sentence with a score indicating the paraphrasing degree. |
| Outcome: | The proposed framework can detect paraphrased text spans within a text . it takes in the full text and assigns each sentence with a score indicating the paraphrasing degree. |
HEAL: An Empirical Study on Hallucinations in Embodied Agents Driven by Large Language Models (2025.findings-emnlp)
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Trishna Chakraborty, Udita Ghosh, Xiaopan Zhang, Fahim Faisal Niloy, Yue Dong, Jiachen Li, Amit Roy-Chowdhury, Chengyu Song
| Challenge: | Large language models are increasingly being adopted as the cognitive core of embodied agents. |
| Approach: | They propose a systematic study of hallucinations in large language models . they aim to understand to what extent hallucinos occur, what types trigger them . |
| Outcome: | The proposed model can induce hallucinations up to 40 higher than base prompts . the model fails to resolve scene-task inconsistencies, the study finds . |
Dissecting Failure Dynamics in Large Language Model Reasoning (2026.acl-long)
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| Challenge: | Large Language Models achieve strong performance through extended inference-time deliberation, yet how their reasoning failures arise remains poorly understood. |
| Approach: | They propose a framework that probes and redirects critical transitions using uncertainty signals. |
| Outcome: | Empirical evaluations show that GUARD improves reasoning performance . GUard probes critical transitions and redirects them using uncertainty signals . |
COPR: Continual Human Preference Learning via Optimal Policy Regularization (2025.findings-acl)
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Han Zhang, Lin Gui, Yu Lei, Yuanzhao Zhai, Yehong Zhang, Zhuo Zhang, Yulan He, Hui Wang, Yue Yu, Kam-Fai Wong, Bin Liang, Ruifeng Xu
| Challenge: | Reinforcement Learning from Human Feedback (RLHF) is effective for aligning Large Language Models with human preferences, but its complex process limits its ability to continually learn human feedback. |
| Approach: | They propose a non-RL offline method to convert historical optimal policies into optimization constraints when continually learning new preferences. |
| Outcome: | The proposed method outperforms strong CL baselines in terms of reward-based evaluations and human assessment. |
Exploiting Rich Textual User-Product Context for Improving Personalized Sentiment Analysis (2023.findings-acl)
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| Challenge: | Typical approaches do not exploit the potential of historical reviews or do not make full use of user/product associations. |
| Approach: | They propose to use historical reviews to initialize user and product representations and incorporate textual associations via a user-product cross-context module. |
| Outcome: | The proposed method outperforms existing state-of-the-art models on IMDb, Yelp and Longformer benchmarks. |
Editing Conceptual Knowledge for Large Language Models (2024.findings-emnlp)
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Xiaohan Wang, Shengyu Mao, Shumin Deng, Yunzhi Yao, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen, Ningyu Zhang
| Challenge: | Existing knowledge editing methods can modify concept-level definitions, but they can distort instantial knowledge in LLMs, leading to poor performance. |
| Approach: | They construct a benchmark dataset ConceptEdit and establish new metrics for evaluation to investigate the editing capability of LLMs. |
| Outcome: | The proposed methods can modify concept definitions but can distort instantial knowledge in LLMs, leading to poor performance. |
Enhancing Argument Structure Extraction with Efficient Leverage of Contextual Information (2023.findings-emnlp)
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| Challenge: | Argument structure extraction (ASE) aims to identify the discourse structure of arguments within documents. |
| Approach: | They propose an Efficient Context-aware ASE model that fully exploits contextual information by augmenting modeling capacity and augmenting training data. |
| Outcome: | The proposed model can extract argumentative discourse structure from documents and reduce reliance on specific words or less informative sentences. |
LitVISTA: A Benchmark for Narrative Orchestration in Literary Text (2026.acl-long)
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Mingzhe Lu, Yiwen Wang, Yanbing Liu, Qi You, Chong Liu, Ruize Qin, Haoyu Dong, Wenyu Zhang, JiaRui Zhang, Yue Hu, Yunpeng Li
| Challenge: | Existing large language models focus on causal coherence, neglecting the complex story arcs and orchestration inherent in human narratives. |
| Approach: | They propose a high-dimensional framework for narrative orchestration that unifies human and model perspectives while jointly characterizing narrative function and structure in a common space. |
| Outcome: | The proposed framework unifies human and model perspectives while jointly characterizing narrative function and structure in a common space. |
ClinAlign: Scaling Healthcare Alignment from Clinician Preference (2026.findings-acl)
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Shiwei Lyu, Xidong Wang, Hao Zhu, Lei Liu, Chaohe Zhang, Jian Wang, Jinjie Gu, Benyou Wang, Yue Shen
| Challenge: | Existing methods for aligning open-ended outputs with fine-grained clinician preferences are weakly grounded in professional guidelines. |
| Approach: | They propose a framework to align large language models' outputs with fine-grained clinician preferences . they propose 119 broadly reusable, clinically grounded principles organized by clinical dimensions . |
| Outcome: | The proposed framework outperforms existing models on HealthBench-Hard and Deepseek-R1 and o3. |
PDF-to-Tree: Parsing PDF Text Blocks into a Tree (2024.findings-emnlp)
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| Challenge: | Existing studies try to extract one universal reading order for PDF files, however, some applications, like Retrieval Augmented Generation, require breaking long articles into sections and subsections for better indexing. |
| Approach: | They propose a new task and dataset, PDF-to-Tree, which organizes the text blocks of a PDF into a tree structure. |
| Outcome: | The proposed parser achieves 93.93% accuracy, surpassing baseline methods by 6.72%. |
From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan (2026.acl-long)
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Lei Yang, Leiyu Pan, Bojian Xiong, Renren Jin, Shaowei Zhang, Yue Chen, Ling Shi, Jiang Zhou, Junru Wu, Zhen Wang, Jianxiang Peng, Juesi Xiao, Tianyu Dong, Zhuowen Han, Zhuo Chen, Yuqi Ren, Deyi Xiong
| Challenge: | Large language models have achieved remarkable success across a wide range of tasks, yet their performance remains heavily biased toward high-resource languages. |
| Approach: | They propose a pipeline for advancing Tibetan language modeling through multilingual continual pre-training with Tibetan, Chinese, and English. |
| Outcome: | The proposed model outperforms open-source and Tibetan-focused models on diverse tasks. |
Coarse-to-Fine Pre-training for Named Entity Recognition (2020.emnlp-main)
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| Challenge: | Named Entity Recognition (NER) is a task of discovering information entities and identifying their corresponding categories. |
| Approach: | They propose a NER-specific framework to inject coarse-to-fine named entity knowledge into pre-trained models by using a remote supervision strategy. |
| Outcome: | The proposed framework achieves significant improvements against several pre-trained base-lines, demonstrating its effectiveness in label-few and low-resource scenarios. |
Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog (2020.acl-main)
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| Challenge: | Recent studies show remarkable success in end-to-end task-oriented dialog systems . however, most models rely on large training data, which is difficult to scalable for new domains with limited labeled data. |
| Approach: | They propose a shared-private network which exploits the relevance between the target domain and each domain. |
| Outcome: | The proposed model outperforms existing methods on multi-domain dialogue by 13.9% on average. |
Sentiment Forecasting in Dialog (2020.coling-main)
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| Challenge: | Existing studies on sentiment classification focus on determining polarity of existing utterances. |
| Approach: | They propose a Neural Sentiment Forecasting task which simulates the next utterance based on context and a sequence influence model to learn both pair-wise and seq-wise influence. |
| Outcome: | The proposed model outperforms existing models over several strong baselines. |
OpenFact: Factuality Enhanced Open Knowledge Extraction (2023.tacl-1)
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Linfeng Song, Ante Wang, Xiaoman Pan, Hongming Zhang, Dian Yu, Lifeng Jin, Haitao Mi, Jinsong Su, Yue Zhang, Dong Yu
| Challenge: | Existing OIE systems organize knowledge into subject-relation-object (SRO) triplets, and they use templates to extract such knowledge triplet. |
| Approach: | They propose a framework to handle expressiveness and groundedness in OpenFact . they propose to use templates, extra constraints, and adopt human efforts to ensure that most triplets contain enough details. |
| Outcome: | The proposed framework improves expressiveness and groundedness of OpenFact . it is more accurate and denser than OPIEC-Linked, which is grounded to Wikidata . |
BLADE: Benchmarking Language Model Agents for Data-Driven Science (2024.findings-emnlp)
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Ken Gu, Ruoxi Shang, Ruien Jiang, Keying Kuang, Richard-John Lin, Donghe Lyu, Yue Mao, Youran Pan, Teng Wu, Jiaqian Yu, Yikun Zhang, Tianmai Zhang, Lanyi Zhu, Mike Merrill, Jeffrey Heer, Tim Althoff
| Challenge: | Language model-based agents can be used to conduct and support data-driven science, but evaluating them on open-ended tasks is challenging due to multiple valid approaches, partially correct steps, and different ways to express the same decisions. |
| Approach: | They propose a benchmark to automatically evaluate agents’ multifaceted approaches to open-ended research questions. |
| Outcome: | BLADE evaluates agents’ multifaceted approaches to open-ended research questions using data from 12 datasets and research questions drawn from existing scientific literature. |
Exploiting Abstract Meaning Representation for Open-Domain Question Answering (2023.findings-acl)
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| Challenge: | Existing work attempts to address these challenges using Pretrained Language Models (PLMs) but the diversity of surface form expressions can hinder the model’s ability to capture accurate correlations, especially when the context is lengthy and complex. |
| Approach: | They propose a method known as Graph-as-Token (GST) to incorporate AMRs into PLMs to assist the model in understanding complex semantic information. |
| Outcome: | The proposed method outperforms existing methods and significantly improves performance on both Natural Questions and TriviaQA. |
Guardian-as-an-Advisor: Advancing Next-Generation Guardian Models for Trustworthy LLMs (2026.findings-acl)
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Yue Huang, Haomin Zhuang, Jiayi Ye, Han Bao, Yanbo Wang, Hang Hua, Siyuan Wu, Pin-Yu Chen, Xiangliang Zhang
| Challenge: | prevailing taxonomies neglect robustness and honesty, yielding safer-on-paper but less useful systems. |
| Approach: | They propose a soft-gating pipeline where a guardian predicts a binary risk label plus a concise explanation and prepends this advice to the original query for re-inference. |
| Outcome: | The proposed model maintains safety while reducing over-refusal. |
Cross-Lingual Dependency Parsing Using Code-Mixed TreeBank (D19-1)
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| Challenge: | Treebank translation is a promising method for cross-lingual transfer of syntactic dependency knowledge. |
| Approach: | They propose to map dependency arcs from source treebank to target translation according to word alignments. |
| Outcome: | Experiments on university dependency treebanks show that translated treebank translations are more effective than translated treebans. |
Learning Domain Representation for Multi-Domain Sentiment Classification (N18-1)
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| Challenge: | Training data for sentiment analysis is abundant in multiple domains, yet scarce for other domains. |
| Approach: | They propose to use domain-specific representations of input sentences to improve sentiment classification . they use a descriptor vector to map adversarially trained domain-general Bi-LSTM inputs into domain- specific representations . |
| Outcome: | The proposed model outperforms existing methods on multi-domain sentiment analysis significantly. |
ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select (2022.emnlp-main)
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| Challenge: | Our proposed method extracts N-ary relation tuples from scientific articles. |
| Approach: | They propose a method that decomposes the task into two stages . they propose modal query and modal entity selection . their results show that ReSel outperforms state-of-the-art baselines significantly . |
| Outcome: | The proposed method outperforms state-of-the-art baselines on three scientific information extraction datasets. |
Diagnosing Hidden Instabilities in Model Editing via Uncertainty Quantification (2026.acl-long)
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Zihan Gu, TianYi Zhang, Xinyan Zhang, Zhiyuan Wang, Han Zhang, Yuhao Wei, Jiacheng Lu, Tianyi Ma, Xingsheng Zhang, Hua Zhang, Yue Hu
| Challenge: | Existing methods to update large language models (LLMs) without expensive retraining are fragile under single-edit evaluation protocols. |
| Approach: | They propose a framework that characterizes activation-based editing as a constrained intervention on intermediate representations. |
| Outcome: | The proposed method reveals local knowledge conflicts invisible to existing benchmarks. |
DRTS Parsing with Structure-Aware Encoding and Decoding (2020.acl-main)
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| Challenge: | Discourse representation tree structure (DRTS) parsing is a new semantic parser which ignores structural information. |
| Approach: | They propose a structural-aware model to integrate structural information into the model . they use graph attention network (GAT) to exploit structural information for effective modeling . |
| Outcome: | The proposed model can achieve the best performance on a benchmark dataset. |
DC-MBR: Distributional Cooling for Minimum Bayesian Risk Decoding (2024.lrec-main)
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| Challenge: | Existing methods for decoding target language are degenerate, hallucinating or empty. |
| Approach: | They propose a method that tunes down the Softmax temperature to reduce autoregressive over-smoothness by label smoothing the output distributions. |
| Outcome: | The proposed method improves MBR in various settings. |
A Graph-to-Sequence Model for AMR-to-Text Generation (P18-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic formalism that encodes the meaning of a sentence as a rooted, directed graph. |
| Approach: | They propose a neural graph-to-sequence model that leverages LSTM to encode a linearized AMR structure. |
| Outcome: | The proposed model outperforms existing methods on a benchmark. |
ExpSeek: Self-Triggered Experience Seeking for Web Agents (2026.findings-acl)
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Wenyuan Zhang, Xinghua Zhang, Haiyang Yu, Shuaiyi Nie, Bingli Wu, Juwei Yue, Tingwen Liu, Yongbin Li
| Challenge: | Existing methods for integrating experience into web agents are struggling to adapt to dynamically changing contextual observations during agent-environment interaction. |
| Approach: | They propose a model that shifts experience toward step-level proactive seeking by estimating step- level entropy thresholds and designing step-Level tailored experience content. |
| Outcome: | The proposed model achieves 9.3% and 7.5% performance improvements on Qwen3-8B and 32B models across four challenging web agent benchmarks. |
DialogSum: A Real-Life Scenario Dialogue Summarization Dataset (2021.findings-acl)
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| Challenge: | Experimental results show unique challenges in dialogue summarization such as spoken terms, special discourse structures, coreferences and ellipsis, pragmatics and social common sense. |
| Approach: | They propose a large-scale labeled dialogue summarization dataset . they use state-of-the-art neural models to analyze spoken dialogue summaries . |
| Outcome: | The proposed dataset can be used to analyze spoken dialogue summarization challenges. |
XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners (2024.naacl-long)
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| Challenge: | Existing methods for active learning rely on model uncertainty or disagreement to pick unlabeled data, leading to over-confidence in superficial patterns and lack of exploration. |
| Approach: | They propose to use a bi-directional encoder and a uni-directional decoder to generate and score an explanation for low-resource text classification. |
| Outcome: | The proposed model improves on 9 strong baselines on six datasets and can generate explanations for its predictions. |
Sentient Agent as a Judge: Evaluating Higher-Order Social Cognition in Large Language Models (2026.findings-acl)
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Bang Zhang, Ruotian Ma, Qingxuan Jiang, Peisong Wang, Jiaqi Chen, Zheng Xie, Xingyu Chen, Yue Wang, Fanghua Ye, Jian Li, Yifan Yang, Zhaopeng Tu, Xiaolong Li
| Challenge: | Large language models (LLMs) have evolved from statistical sequence predictors to sophisticated autonomous agents capable of reasoning, planning, and sustaining multi-turn conversa-tions. |
| Approach: | They propose a system that instantiates a "Sentient Agent" that simulates human-like emotional changes and inner thoughts to provide a more realistic evaluation of the model in multi-turn conversations. |
| Outcome: | The proposed framework measures the agent's higher-order social cognition in multi-turn conversations. |
Cautious Next Token Prediction (2025.findings-acl)
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Yizhou Wang, Lingzhi Zhang, Yue Bai, Mang Tik Chiu, Zhengmian Hu, Mingyuan Zhang, Qihua Dong, Yu Yin, Sohrab Amirghodsi, Yun Fu
| Challenge: | Existing methods for decoding autoregressive models are temperature scaling and nucleus sampling to balance diversity and coherence. |
| Approach: | They propose a training-free decoding strategy that uses a model with a low perplexity score to select the trial with the lowest perplexities as the most probable and reliable path. |
| Outcome: | The proposed approach outperforms existing standard decoding strategies consistently by a clear margin. |
What Did You Refer to? Evaluating Co-References in Dialogue (2021.findings-acl)
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| Challenge: | Existing neural end-to-end dialogue models have limitations on exactly interpreting the linguistic structures in dialogue history context. |
| Approach: | They propose to directly measure the capability of neural end-to-end dialogue models on understanding the entity-oriented structures via question answering. |
| Outcome: | The proposed model can understand large-scale English and Chinese human human dialogues using a large-format dataset. |
RFiD: Towards Rational Fusion-in-Decoder for Open-Domain Question Answering (2023.findings-acl)
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| Challenge: | Open-domain Question Answering (ODQA) systems rely on spurious features instead of genuine causal relationships to generate answers. |
| Approach: | They propose a model that leverages the encoders of FiD to distinguish between causal relationships and spurious features and guides the decoder to generate answers informed by this discernment. |
| Outcome: | The proposed model improves on two ODQA datasets and shows that it can identify causal relationships and identify spurious features. |
Cross-domain Generalization for AMR Parsing (2022.emnlp-main)
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| Challenge: | Abstract Meaning Representation (AMR) parsing aims to predict an AMR graph from textual input. |
| Approach: | They evaluate five representative AMR parsers on five domains and analyze challenges to cross-domain parsing. |
| Outcome: | The proposed method reduces the domain distribution divergence of text and AMR features on two out-of-domain sets. |
UIOrchestra: Generating High-Fidelity Code from UI Designs with a Multi-agent System (2025.findings-emnlp)
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Chuhuai Yue, Jiajun Chai, Yufei Zhang, Zixiang Ding, Xihao Liang, Peixin Wang, Shihai Chen, Wang Yixuan, null Wangyanping, Guojun Yin, Wei Lin
| Challenge: | Recent advances in large language models have significantly improved automated code generation . however, the translation of complex mobile UI designs into high-fidelity front-end code remains a challenge . |
| Approach: | They propose a collaborative multi-agent system to reconstruct static single-page apps from mockups. |
| Outcome: | The proposed system outperforms existing methods in reconstructing complex app pages . the code and data will be released upon paper acceptance . |
Fair Federated Learning with Biased Vision-Language Models (2024.findings-acl)
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| Challenge: | Existing literature ignores the inherent group unfairness within CLIP and its ethical implications on FL applications. |
| Approach: | They propose a fairness-aware adaptation framework for CLIP in federated learning . they propose to leverage biased pre-trained VLMs to build fair FL frameworks . |
| Outcome: | The proposed framework addresses unique bias in FL, triggered by data heterogeneity . it trains a fair FL model with fairness-aware deep visual prompting (DVP) Extensive results on human face attribute recognition (FAR) applications show it outperforms state-of-the-art FL models . |
Structural Information Preserving for Graph-to-Text Generation (2020.acl-main)
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| Challenge: | Existing models that mess up or drop the core structural information of input graphs are lacking in graph-to-text generation. |
| Approach: | They propose to leverage richer training signals to guide a graph-to-text generation model by focusing on autoencoding losses and back-propagating the losses to better calibrate the model. |
| Outcome: | Experiments on two benchmarks show the proposed model over a state-of-the-art model . two types of autoencoding losses are used to back-propagate the model based on multitask training . |
From Remembering to Metacognition: Do Existing Benchmarks Accurately Evaluate LLMs? (2025.findings-emnlp)
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Geng Zhang, Yizhou Ying, Sihang Jiang, Jiaqing Liang, Guanglei Yue, Yifei Fu, Hailin Hu, Yanghua Xiao
| Challenge: | Existing benchmark datasets focus on low-level cognitive tasks while providing limited coverage of higher-level reasoning skills. |
| Approach: | They analyze the cognitive depth of popular LLM benchmarks using Bloom’s Taxonomy to evaluate both the cognitive and knowledge dimensions. |
| Outcome: | The results show that incorporating higher-level cognitive instructions into the current instruction fine-tuning process improves model performance. |
URG: A Unified Ranking and Generation Method for Ensembling Language Models (2024.findings-acl)
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| Challenge: | Existing approaches to rank and generate large language models have limited performance due to time-intensive nature of ranking process and lack of error propagation. |
| Approach: | They propose a framework that jointly ranks the outputs of Large Language Models and generates fine-grained fusion results. |
| Outcome: | The proposed framework achieves state-of-the-art (SOTA) performance on ranking and generation tasks. |
DeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet Classification via Memory Bank (2023.findings-emnlp)
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| Challenge: | Existing studies utilize social media platforms such as Twitter to build models for crisis event analysis, but semi-supervised approaches require annotating vast amounts of data and are impractical due to limited response time. |
| Approach: | They propose a method that stores and performs equal sampling for generated pseudo-labels from each class at each training iteration. |
| Outcome: | The proposed method performs better than existing methods in both in-distribution and out-of-difference settings. |
Discrete Opinion Tree Induction for Aspect-based Sentiment Analysis (2022.acl-long)
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| Challenge: | Dependency trees are used for aspect-based sentiment classification but are not optimized for aspect classification. |
| Approach: | They propose an aspect-specific and language-agnostic discrete latent opinion tree model as an alternative structure to explicit dependency trees. |
| Outcome: | The proposed model can achieve competitive performance and interpretability on six English benchmarks and one Chinese dataset. |
The Essence of Contextual Understanding in Theory of Mind: A Study on Question Answering with Story Characters (2025.acl-long)
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Chulun Zhou, Qiujing Wang, Mo Yu, Xiaoqian Yue, Rui Lu, Jiangnan Li, Yifan Zhou, Shunchi Zhang, Jie Zhou, Wai Lam
| Challenge: | Theory-of-Mind (ToM) is a psychological capability that allows humans to understand and interpret the mental states of others. |
| Approach: | They propose a CharToM-QA benchmark to assess the importance of comprehensive contextual understanding about personal backgrounds in ToM. |
| Outcome: | The proposed model outperforms existing models on 1,035 ToM questions based on classic novels and shows that educated participants perform better when they have read the novels than non-educated participants. |
Structural Adapters in Pretrained Language Models for AMR-to-Text Generation (2021.emnlp-main)
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| Challenge: | Pretrained language models (PLMs) have advanced graph-to-text generation, but efficient encoding of graph structure is challenging because of the nature of the data. |
| Approach: | They propose a method to encode graph structure into pretrained language models by training only graph structure-aware adapter parameters. |
| Outcome: | The proposed method outperforms the state-of-the-art on two AMR-to-text datasets, training only 5.1% of the adapter parameters. |
Structure-aware Domain Knowledge Injection for Large Language Models (2025.acl-long)
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| Challenge: | Structure-aware Continual Pre-Training (SCPT) and Structure-Aware Supervised Fine-Tuning (SSFT) are two-stage strategies for knowledge injection and alignment that reduces the training corpus needs to 5% while achieving 100% of traditional knowledge injection performance. |
| Approach: | They propose a method to efficiently transform foundation Large Language Models into domain specialists by using two-stage strategies: Structure-aware Continual Pre-Training and Structure-Aware Supervised Fine-Tuning. |
| Outcome: | The proposed method significantly reduces the training corpus needs to a mere 5% while achieving 100% of traditional knowledge injection performance. |
AutoEvolve: Automatically Evolving Queries for Applicable and Scalable Retrieval-Augmented Generation Benchmarking (2025.findings-emnlp)
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Ding-Chu Zhang, Xiaowen Zhang, Yue Fei, Renjun Hu, Xiao-Wen Yang, Zhi Zhou, Baixuan Li, Yu-Feng Li, Xing Shi, Wei Lin
| Challenge: | Existing automated generation methods exhibit Weak Applicability and Weak Scalability . existing methods are limited by their reliance on metadata from specific corpora . |
| Approach: | They propose an approach to generate scalable RAG benchmarks using corpus-agnostic methods . they propose a difficulty-guided metric that directs query evolution process . |
| Outcome: | The proposed approach evolves queries significantly more challenging than existing methods . it is able to dynamically increase difficulty, limiting scalability of the query . |
UniSumm and SummZoo: Unified Model and Diverse Benchmark for Few-Shot Summarization (2023.acl-long)
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| Challenge: | a new benchmark summarization model is being developed to train few-shot summarizers . a large number of summarizing tasks are required to perform well in heterogeneous datasets. |
| Approach: | They propose a few-shot summarization model pre-trained with multiple summarizing tasks . they propose 'uniSumm' to be prefix-tuned to excel at any few-shot summarisation task . |
| Outcome: | The proposed model outperforms baseline models under automatic and human evaluations and achieves comparable results in human evaluation. |
ARL2: Aligning Retrievers with Black-box Large Language Models via Self-guided Adaptive Relevance Labeling (2024.acl-long)
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| Challenge: | Existing retrievers are misaligned with large language models due to separate training processes and inherent black-box nature of LLMs. |
| Approach: | They propose a retriever learning technique that harnesses LLMs as labelers to annotate and score adaptive relevance evidence. |
| Outcome: | Extensive experiments show that ARL2 improves accuracy and reduces the cost of API calls. |
Joint models for NLP (D18-3)
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| Challenge: | This tutorial reviews main approaches to joint modeling for statistical and neural methods. |
| Approach: | This tutorial reviews main approaches to joint modeling for both statistical and neural methods. |
| Outcome: | This tutorial reviews main approaches to joint modeling for statistical and neural methods. |
ValCAT: Variable-Length Contextualized Adversarial Transformations Using Encoder-Decoder Language Model (2022.naacl-main)
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| Challenge: | Existing word-level approaches to attack text are limited to a single word . existing methods ignore interactions between consecutive words, resulting in one-to-one attacks . |
| Approach: | They propose a black-box attack framework that misleads the language model by applying variable-length contextualized transformations to the original text. |
| Outcome: | The proposed framework outperforms existing methods on classification and inference tasks. |
Chain-of-Specificity: Enhancing Task-Specific Constraint Adherence in Large Language Models (2025.coling-main)
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| Challenge: | Existing approaches to enhancing large language models fail to emphasize specific constraints and unlock the underlying knowledge. |
| Approach: | They propose a method that emphasizes specific constraints and unlocks knowledge within LLMs by iteratively emphasising on specific constraints. |
| Outcome: | The proposed method outperforms existing methods in enhancing generated content, especially in terms of specificity. |
MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark (2025.acl-long)
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Xiang Yue, Tianyu Zheng, Yuansheng Ni, Yubo Wang, Kai Zhang, Shengbang Tong, Yuxuan Sun, Botao Yu, Ge Zhang, Huan Sun, Yu Su, Wenhu Chen, Graham Neubig
| Challenge: | Recent advances in multimodal large language models have led to progress in tackling complex reasoning tasks that combine textual and visual information. |
| Approach: | They introduce a robust version of the Massive Multi-discipline Multimodal Understanding and Reasoning (MMMU) benchmark. |
| Outcome: | The proposed model performs lower on MMMU-Pro than on the previous benchmark, ranging from 16.8% to 26.9%. |
Reflecting the Male Gaze: Quantifying Female Objectification in 19th and 20th Century Novels (2024.lrec-main)
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| Challenge: | a framework for analyzing gender bias in terms of female objectification is proposed . male gaze refers to a phenomenon in which women are depicted as objects of aesthetic pleasure . |
| Approach: | They propose a framework for analyzing gender bias in terms of female objectification . they compute an agency bias score that indicates whether male entities are more likely to appear in the text as grammatical agents than female entities . |
| Outcome: | The proposed framework measures female objectification along two axes. |
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. |
Tree Communication Models for Sentiment Analysis (P19-1)
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| Challenge: | Existing methods for sentiment classification over hierarchical phrases capture only bottom-up dependencies between constituents. |
| Approach: | They propose a tree-based sentiment analysis model using graph convolutional neural network and graph recurrent neural network which allows rich information exchange between phrases constituent tree. |
| Outcome: | The proposed model outperforms existing tree-LSTMs in accuracy and efficiency, providing more consistent predictions on phrase-level sentiments. |
A Comparison between Pre-training and Large-scale Back-translation for Neural Machine Translation (2021.findings-acl)
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| Challenge: | BERT is a promising technique to improve NMT, but how it outperforms standard NMT is understudied. |
| Approach: | We compare MT engines trained with pre-trained BERT and back-translation with incrementally larger amounts of data. |
| Outcome: | The proposed technique outperforms standard NMT models on morphology and syntax. |
MedDialog: Large-scale Medical Dialogue Datasets (2020.emnlp-main)
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Guangtao Zeng, Wenmian Yang, Zeqian Ju, Yue Yang, Sicheng Wang, Ruisi Zhang, Meng Zhou, Jiaqi Zeng, Xiangyu Dong, Ruoyu Zhang, Hongchao Fang, Penghui Zhu, Shu Chen, Pengtao Xie
| Challenge: | telemedicine is a medical practice that provides patient care remotely using video conferencing tools. |
| Approach: | They build large-scale medical dialogue datasets to facilitate research . they pretrain several models on the Chinese MedDialog dataset and compare their performance . |
| Outcome: | The proposed datasets show that models trained on MedDialog can generate doctor-like medical dialogues. |
Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect (2022.coling-1)
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| Challenge: | text-to-SQL is a language processing and database-based language processing (NLP) task is to convert natural utterances into SQL queries and its practical application is to build natural language interfaces to database systems. |
| Approach: | They propose to conduct a systematic survey of text-to-SQL to examine the challenges and potential future directions. |
| Outcome: | The proposed system converts natural utterances into SQL queries and is a representative task in semantic parsing. |
Latent Variable Sentiment Grammar (P19-1)
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| Challenge: | Existing neural models do not explicitly model sentiment composition, which requires to encode sentiment class labels. |
| Approach: | They propose a sentiment grammar that captures sentiment subtype expressions by latent variables and Gaussian mixture vectors. |
| Outcome: | The proposed model outperforms vanilla neural encoders on the Stanford Sentiment Treebank benchmark. |
MEXA: Towards General Multimodal Reasoning with Dynamic Multi-Expert Aggregation (2025.findings-emnlp)
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| Challenge: | MEXA is a training-free framework that performs modality- and task-aware aggregation of multiple expert models to enable effective multimodal reasoning across diverse domains. |
| Approach: | MEXA is a training-free framework that performs modality- and task-aware aggregation of multiple expert models. |
| Outcome: | MEXA performs modality- and task-aware aggregation of multiple expert models . it generates interpretable textual reasoning outputs and reasons over them using a Large Reasoning Model (LRM) MEX A consistently delivers performance improvements over strong multimodal benchmarks . |
Multi-Cell Compositional LSTM for NER Domain Adaptation (2020.acl-main)
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| Challenge: | Named entity recognition (NER) is a challenging but practical problem. |
| Approach: | They propose a multi-cell compositional LSTM structure for multi-task learning . they model each entity type using a separate cell state . |
| Outcome: | Empirical results show that the proposed method outperforms multi-task learning methods and achieves the best results. |
Safety in Large Reasoning Models: A Survey (2025.findings-emnlp)
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Cheng Wang, Yue Liu, Baolong Bi, Duzhen Zhang, Zhong-Zhi Li, Yingwei Ma, Yufei He, Shengju Yu, Xinfeng Li, Junfeng Fang, Jiaheng Zhang, Bryan Hooi
| Challenge: | Large Reasoning Models (LRMs) have a high level of advanced reasoning capabilities, but they are vulnerable and vulnerable. |
| Approach: | This paper presents the first comprehensive survey of Large Reasoning Models . it explores the new safety risks, attacks, and defense strategies specific to LRMs based on reasoning . |
| Outcome: | The proposed study examines the safety and security risks of large reasoning models. |
PhiloGPT: A Philology-Oriented Large Language Model for Ancient Chinese Manuscripts with Dunhuang as Case Study (2024.emnlp-main)
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Yuqing Zhang, Baoyi He, Yihan Chen, Hangqi Li, Han Yue, Shengyu Zhang, Huaiyong Dou, Junchi Yan, Zemin Liu, Yongquan Zhang, Fei Wu
| Challenge: | philology requires years of professional training in extensive knowledge memorization and manual textual retrieval. |
| Approach: | They curated the PhiloCorpus-ZH, a rich collec-tion of ancient Chinese texts spanning a millennium with 30 diverse topics, including firsthand folk copies. |
| Outcome: | The PhiloCorpus-ZH corpus facilitated the development of the first LLM tailored for discovering ancient Chinese manuscripts. |
NL2Lean: Translating Natural Language into Lean 4 through Multi-Aspect Reinforcement Learning (2025.emnlp-main)
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Yue Fang, Shaohan Huang, Xin Yu, Haizhen Huang, Zihan Zhang, Weiwei Deng, Furu Wei, Feng Sun, Qi Zhang, Zhi Jin
| Challenge: | Existing formal proof assistants rely on instruction tuning and lack fine-grained structural and semantic alignment. |
| Approach: | They propose a reinforcement learning framework that enables LLMs to translate natural language into formal language such as Lean 4 . they use a model with basic translation ability to refine the model's reinforcement learning . |
| Outcome: | The proposed method outperforms baseline models on NL-to-Lean 4 tasks. |
Knowledge-Centric Hallucination Detection (2024.emnlp-main)
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Xiangkun Hu, Dongyu Ru, Lin Qiu, Qipeng Guo, Tianhang Zhang, Yang Xu, Yun Luo, Pengfei Liu, Yue Zhang, Zheng Zhang
| Challenge: | Large Language Models (LLMs) have shown impressive capabilities but a tendency to hallucinate. |
| Approach: | They propose a framework that introduces claim-triplets to represent claims in LLM responses and evaluates them against a reference. |
| Outcome: | The proposed framework outperforms prior methods by 18.2 to 27.2 points on a benchmark spanning various NLP tasks and annotated 11k claim-triplets from 2.1k responses by seven LLMs. |
RobustGEC: Robust Grammatical Error Correction Against Subtle Context Perturbation (2023.emnlp-main)
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| Challenge: | Grammatical Error Correction (GEC) systems perform well in academic benchmarks, but in practical applications they may not correct errors when users perform irrelevant modifications. |
| Approach: | They propose a benchmark to evaluate the context robustness of Grammatical Error Correction systems. |
| Outcome: | The proposed method improves the accuracy of errors corrected by human annotations. |
Causal Reasoning of Entities and Events in Procedural Texts (2023.findings-eacl)
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| Challenge: | Existing work on entity state tracking or event reasoning is limited to procedural texts. |
| Approach: | They propose a benchmark for causal reasoning of event plausibility and entity states . they represent entities as programming languages while prompting language models . |
| Outcome: | The proposed model outperforms existing models on human reasoning and event reasoning. |
DUET: Joint Exploration of User–Item Profiles in Recommendation System (2026.findings-acl)
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Yue Chen, Yifei Sun, Lu Wang, Fangkai Yang, Pu Zhao, Minjie Hong, Yifei Dong, Minghua He, Nan Hu, Jianjin Zhang, Zhiwei Dai, Yuefeng Zhan, Weihao Han, Hao Sun, Qingwei Lin, Weiwei Deng, Feng Sun, Qi Zhang, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Existing LLMs are opaque and difficult to interpret, resulting in limited interpretability. |
| Approach: | They propose an interaction-aware profile generator that jointly produces user and item profiles conditioned on both user history and item evidence. |
| Outcome: | The proposed model outperforms baselines on three real-world datasets. |
Generalized Supervised Attention for Text Generation (2021.findings-acl)
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| Challenge: | Existing supervised attention methods that use human knowledge to learn better alignments are costly or infeasible. |
| Approach: | They propose a generalized supervised attention method based on quasi alignments that are easier to obtain than ideal alignments. |
| Outcome: | The proposed framework improves generation performance and is robust against errors in attention supervision. |
Speeding up Transformer Decoding via an Attention Refinement Network (2022.coling-1)
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| Challenge: | Extensive experiments on ten WMT machine translation tasks show that the proposed model yields an average of 1.35x faster (with almost no decrease in BLEU) |
| Approach: | They propose a weighted residual network which reconstructs attention by reusing the features across layers. |
| Outcome: | The proposed model is 1.35x faster than the state-of-the-art inference model on translation tasks compared to AAN and SAN models with fewer parameter numbers . |
DivScene: Towards Open-Vocabulary Object Navigation with Large Vision Language Models in Diverse Scenes (2025.findings-emnlp)
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Zhaowei Wang, Hongming Zhang, Tianqing Fang, Ye Tian, Yue Yang, Kaixin Ma, Xiaoman Pan, Yangqiu Song, Dong Yu
| Challenge: | Large Vision-Language Models (LVLMs) have achieved significant progress in tasks like visual question answering and document understanding. |
| Approach: | They introduce DivScene, a large-scale dataset with 4,614 houses across 81 scene types and 5,707 kinds of target objects. |
| Outcome: | The proposed dataset provides a much greater diversity of target objects and scene types than existing datasets, enabling a comprehensive task evaluation. |
From Query to Counsel: Structured Reasoning with a Multi-Agent Framework and Dataset for Legal Consultation (2026.acl-long)
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| Challenge: | Legal consultation question answering presents unique challenges compared to traditional legal QA tasks . |
| Approach: | They propose a framework that converts queries into a legal element graph . jurisMA supports dynamic routing, statutory grounding, and stylistic optimization . |
| Outcome: | The proposed framework outperforms general-purpose and legal-domain LLMs across multiple lexical and semantic metrics. |
Self-Generated Critiques Boost Reward Modeling for Language Models (2025.naacl-long)
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Yue Yu, Zhengxing Chen, Aston Zhang, Liang Tan, Chenguang Zhu, Richard Yuanzhe Pang, Yundi Qian, Xuewei Wang, Suchin Gururangan, Chao Zhang, Melanie Kambadur, Dhruv Mahajan, Rui Hou
| Challenge: | Existing reward models produce scalar scores and struggle to incorporate critiques in a natural language format. |
| Approach: | They propose a framework that predicts critiques and rewards using self-generated critiques without extra supervision. |
| Outcome: | The proposed framework improves reward modeling accuracy by 3.7%-7.3% compared to standard reward models and LLM judges. |
Beyond Single-Audio: Advancing Multi-Audio Processing in Audio Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing evaluations of audio large language models focus on single audio inputs, but real-world applications often require processing multiple audio streams simultaneously. |
| Approach: | They propose a multi-audio evaluation benchmark that combines 20 audio inputs from 11 audio tasks to capture audio context. |
| Outcome: | The proposed model outperforms baseline models and achieves high data efficiency without human annotations. |
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. |
Mere Contrastive Learning for Cross-Domain Sentiment Analysis (2022.coling-1)
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| Challenge: | Existing approaches to cross-domain sentiment analysis are labor-intensive and time-consuming. |
| Approach: | They propose a modified contrastive objective with in-batch negative samples to allow sentence representations from the same class to be pushed closer while those from the different classes become further apart in the latent space. |
| Outcome: | The proposed model can achieve state-of-the-art in cross-domain and multi-domain sentiment analysis tasks while transferring knowledge learned in the source domain to the target domain. |
Bidirectional Generative Framework for Cross-domain Aspect-based Sentiment Analysis (2023.acl-long)
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| Challenge: | Aspect-based sentiment analysis (ABSA) is a task of analyzing people's sentiments at the aspect level. |
| Approach: | They propose a unified bidirectional generative framework to tackle cross-domain ABSA tasks . the framework trains a model in both text-to-label and label-totext directions . |
| Outcome: | The proposed framework trains a model in both label-to-label and label- to-text directions to learn domain-agnostic features. |
Non-autoregressive Text Editing with Copy-aware Latent Alignments (2023.emnlp-main)
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| Challenge: | Seq2Edit approaches still face several challenges such as inflexibility in generation and difficulty in generalizing to other languages. |
| Approach: | They propose a non-autoregressive text editing method that models the edit process with latent CTC alignments and introduces the copy operation into the edit space. |
| Outcome: | The proposed method outperforms existing Seq2Edit models and achieves similar or even better results than Seq1Edit with over 4 speedup. |
FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition (2022.coling-1)
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| Challenge: | Existing approaches for few-shot Named Entity Recognition (NER) are evaluated mainly under in-domain settings, but little is known about how these models perform in cross-domain NER using labeled in- domain examples. |
| Approach: | They propose to use a rationale-centric data augmentation method to improve model generalization ability by allowing model to learn from a few labeled examples in a new target domain. |
| Outcome: | The proposed method improves the performance of cross-domain NER tasks compared to the counterfactual data augmentation and prompt-tuning methods. |
Does it Make Sense? And Why? A Pilot Study for Sense Making and Explanation (P19-1)
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| Challenge: | Existing benchmarks measure common sense knowledge indirectly or without reasoning. |
| Approach: | They propose a benchmark to test whether a system can differentiate natural language statements that make sense from those that do not make sense. |
| Outcome: | The proposed benchmarks show that models trained on large corpora perform better than humans on some benchmarks. |
Semantic-based Pre-training for Dialogue Understanding (2022.coling-1)
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| Challenge: | Pre-trained language models are weak in understanding the main semantic meaning of a dialogue context. |
| Approach: | They propose a semantic-based framework that leverages explicit semantic knowledge to capture the core semantic information in dialogues during pre-training. |
| Outcome: | The proposed model is superior to existing models on chit-chats and task-oriented dialogues. |
MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning (2020.findings-emnlp)
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| Challenge: | Existing work on knowledge graphs infers a missing relationship between entities with a multi-hop rule . Empirical results show that our multi-chain multi-homing (MCMH) rules yield superior results compared to the standard single-chain approaches. |
| Approach: | They propose to use a generalized form of multi-hop rules to learn generalized rules efficiently . they propose to select a small set of relation chains as a rule and evaluate confidence . |
| Outcome: | The proposed method outperforms the existing methods and the existing frameworks. |
SAM3-I: Segment Anything with Instructions (2026.acl-long)
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Jingjing Li, Yue Feng, Yuchen Guo, Jincai Huang, Wei Ji, Qi Bi, Yongri Piao, Miao Zhang, Xiaoqi Zhao, Qiang Chen, Shihao Zou, Huchuan Lu, Li Cheng
| Challenge: | Existing methods for concept-level grounding and instruction-level reasoning use coarse representations and iterative mask filtering. |
| Approach: | They propose an instruction-following extension of the Segment Anything Model 3 family that unifies concept-level grounding and instruction-level reasoning within a single segmentation framework. |
| Outcome: | Experiments show that SAM3-I achieves appealing performance across referring and reasoning-based segmentation while maintaining its strong concept recall ability. |
ReEfBench: Quantifying the Reasoning Efficiency of LLMs (2026.acl-long)
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| Challenge: | Existing methods for Chain-of-Thought evaluations do not distinguish between genuine reasoning and mere verbosity. |
| Approach: | They propose a framework for the non-intrusive, comprehensive process-centric evaluation of reasoning grounded in First-Order Logic. |
| Outcome: | The proposed framework identifies four distinct behavioral prototypes and diagnoses the failure modes. |
FlowSearch: Advancing Deep Research with Dynamic Structured Knowledge Flow (2026.acl-long)
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Yusong Hu, Runmin Ma, Yue Fan, Jinxin Shi, Zongsheng Cao, Yuhao Zhou, Jiakang Yuan, Shuaiyu Zhang, Shiyang Feng, Xiangchao Yan, Shufei Zhang, Wenlong Zhang, Lei Bai, Bo Zhang
| Challenge: | FlowSearch is a multi-agent framework that actively constructs and evolves a dynamic structured knowledge flow to drive subtask execution and reasoning. |
| Approach: | They propose a multi-agent framework that actively constructs and evolves a dynamic structured knowledge flow to drive subtask execution and reasoning. |
| Outcome: | The proposed framework achieves competitive performance on GAIA, HLE, GPQA and TRQA benchmarks and is available to download. |
PolicyLLM: Towards Excellent Comprehension of Public Policy for Large Language Models (2026.findings-acl)
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Han Bao, Penghao Zhang, Yue Huang, Zhengqing Yuan, Yanchi Ru, SU Rui, Yujun Zhou, Xiangqi Wang, Kehan Guo, Nitesh V Chawla, Yanfang Ye, Xiangliang Zhang
| Challenge: | Large Language Models (LLMs) are increasingly integrated into real-world decision-making, but their ability to comprehend and reason about policy-related content remains underexplored. |
| Approach: | They propose a bilingual benchmark evaluating policy comprehension comprising 21K cases across a broad spectrum of policy areas. |
| Outcome: | The proposed model shows stronger performance on application-oriented policy tasks than on memorization or conceptual understanding, and yields the highest accuracy on structured reasoning tasks. |
Large Language Models Are Involuntary Truth-Tellers: Exploiting Fallacy Failure for Jailbreak Attacks (2024.emnlp-main)
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| Challenge: | Existing research has shown that large language models have difficulty discerning the veracity of their intrinsic answers. |
| Approach: | They propose a jailbreak attack method that generates an aligned language model for malicious output. |
| Outcome: | The proposed method achieves competitive performance with more harmful outputs. |
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. |
GUI-Bee: Align GUI Action Grounding to Novel Environments via Autonomous Exploration (2025.emnlp-main)
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| Challenge: | Recent work of GUI action grounding fine-tunes data from pre-trained MLLMs, but data is limited to specific GUI environments. |
| Approach: | They propose to use a GUI-based agent to collect environment-specific data and fine-tune GUI grounding models with the collected data. |
| Outcome: | The proposed model can be extended to other GUI environments to improve performance. |
Design Challenges and Misconceptions in Neural Sequence Labeling (C18-1)
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| Challenge: | Existing neural sequence labeling models have been used for many tasks such as POS tagging, chunking and named entity recognition (NER). |
| Approach: | They propose to replicate twelve neural sequence labeling models and compare them to three benchmarks to find out which models are effective and which are inconsistent. |
| Outcome: | The proposed models are compared on NER, Chunking, and POS tagging 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. |
Online Back-Parsing for AMR-to-Text Generation (2020.emnlp-main)
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| Challenge: | Abstract meaning representation (AMR) is a semantic graph representation that abstracts meaning away from a sentence. |
| Approach: | They propose a decoder that back predicts projected AMR graphs on target sentences . their results show superiority over previous state-of-the-art decoded graph Transformer . |
| Outcome: | The proposed model outperforms the state-of-the-art model on two AMR benchmarks. |
Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach (2021.naacl-main)
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| Challenge: | Fine-tuned pre-trained language models (LMs) have enormous success in many natural language processing tasks, but they still require excessive labeled data in the fine-tuning stage. |
| Approach: | They propose a framework to enable fine-tuning pre-trained language models with weak supervision without any labeled data. |
| Outcome: | The proposed framework outperforms the strongest baseline and achieves competitive performance with fully-supervised fine-tuning methods. |
COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval (2021.emnlp-main)
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| Challenge: | 16K FAQ items scraped from 55 credible websites . 32 human-annotated FAQ items for each query. |
| Approach: | They present a large, challenging dataset for FAQ retrieval for COVID-19 . they use a FAQ bank, Query Bank and Relevance Set to evaluate the dataset . |
| Outcome: | The proposed model achieves 48.8 under P@5 and is compared with other datasets. |
ExeCoder: Empowering Large Language Models with Executability Representation for Code Translation (2025.emnlp-main)
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Minghua He, Yue Chen, Fangkai Yang, Pu Zhao, Wenjie Yin, Yu Kang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Existing code translation models only learn the contextual semantics of code during pre-training, neglecting executability information closely related to the execution state of the code. |
| Approach: | They propose an LLM specifically designed for code translation called ExeCoder . it uses executability representations such as functional semantics and syntax structures to enhance LLMs' capabilities. |
| Outcome: | The proposed model outperforms existing open-source code translation models on two metrics. |
PromptGen: Automatically Generate Prompts using Generative Models (2022.findings-naacl)
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| Challenge: | Recent prompt learning has received significant attention, where downstream tasks are reformulated to the mask-filling task with the help of a textual prompt. |
| Approach: | They propose a model PromptGen which can automatically generate prompts conditional on the input sentence. |
| Outcome: | The proposed model outperforms baseline models on the knowledge probing LAMA benchmark. |
Read Anywhere Pointed: Layout-aware GUI Screen Reading with Tree-of-Lens Grounding (2024.emnlp-main)
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| Challenge: | Existing models for GUI understanding ignore a key GUI-referring task: screen reading based on user-indicated points. |
| Approach: | They propose a Tree-of-Lens agent that constructs a Hierarchical Layout Tree based on user input points and a GUI screenshot. |
| Outcome: | The proposed agent can interpret the Screen Point-and-Read task on mobile, web, and operating systems. |
Challenges to Open-Domain Constituency Parsing (2022.findings-acl)
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| Challenge: | Existing findings on cross-domain constituency parsing are only made on a limited number of domains. |
| Approach: | They manually annotate a high-quality constituency treebank containing five domains and analyze challenges to open-domain constituency parsing using a set of linguistic features. |
| Outcome: | The proposed model significantly improves the performance of the proposed model on the domain-variant features. |
Tables as Texts or Images: Evaluating the Table Reasoning Ability of LLMs and MLLMs (2024.findings-acl)
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| Challenge: | Recent years have witnessed an explosion of Large Language Models (LLMs), with impressive performance on various NLP tasks. |
| Approach: | They propose to use image-based representations to compare LLMs' performance on table-related tasks such as question-answering and fact-checking to determine their effectiveness. |
| Outcome: | The proposed model performs better on image-based representations than on text-based models. |
Movie101: A New Movie Understanding Benchmark (2023.acl-long)
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| Challenge: | Existing methods to narrate movies with no actors are difficult to implement in real situations . a new metric is proposed to provide the best correlation with human evaluation . |
| Approach: | They propose a large-scale Chinese movie benchmark to help visually impaired enjoy movies . they propose metric called Movie Narration Score (MNScore) which achieves best correlation with human evaluation. |
| Outcome: | The proposed method outperforms baselines and the existing methods. |
Can We Steer Reasoning Direction by Thinking Intervention? (2025.findings-emnlp)
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| Challenge: | Large Reason Models suffer from overthinking and erroneous reasoning problems due to the lack of fine-grained control over their reasoning behaviors. |
| Approach: | They propose a paradigm to enable fine-grained control over LRMs’ reasoning behaviors by aligning reasoning trajectories with specific cognitive patterns. |
| Outcome: | The proposed paradigm achieves integration intervention throughout model reasoning processes. |
Unveiling the Deficiencies of Pre-trained Text-and-Layout Models in Real-world Visually-rich Document Information Extraction (2026.findings-eacl)
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Chong Zhang, Yixi Zhao, Yulu Xie, Chenshu Yuan, Yi Tu, Ya Guo, Mingxu Chai, Ziyu Shen, Yue Zhang, Qi Zhang
| Challenge: | PTLMs have shown remarkable success in multiple information extraction tasks . however, their performance in real-world scenarios falls short of expectations . |
| Approach: | They propose to use an entity-centric dataset to evaluate PTLMs' performance . they find that inadequate annotations in benchmark datasets lead to spurious correlations . |
| Outcome: | The proposed dataset disentangles the falsely-coupled segment and entity annotations that arises from the block-level annotation of FUNSD. |
Breaking Down and Building Up: Mixture of Skill-Based Vision-and-Language Navigation Agents (2026.acl-long)
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| Challenge: | Vision-and-Language Navigation (VLN) is a subfield of embodied AI that integrates natural language understanding, visual perception, and sequential decision-making to allow autonomous agents to navigate and interact within visual environments. |
| Approach: | They propose a modular framework that introduces structured, skill-based reasoning into Transformer-based VLN agents. |
| Outcome: | The proposed framework decomposes navigation into atomic skills handled by a specialized agent. |
Dual Cache for Long Document Neural Coreference Resolution (2023.acl-long)
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| Challenge: | Recent studies show the effectiveness of cache-based neural coreference resolution models on long documents. |
| Approach: | They propose a hybrid cache that integrates two eviction policies to capture global and local entities separately and improves F1 score of coreference by 0.7 5.7pt. |
| Outcome: | The proposed model outperforms existing models on four benchmarks while saving up to 83% of inference time. |
Porous Lattice Transformer Encoder for Chinese NER (2020.coling-main)
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| Challenge: | Existing methods to integrate word boundary information into character-level Chinese NER are inefficient and lack semantic interaction. |
| Approach: | They propose an extension of transformer encoder that is tailored for ChineseNER to incorporate lexicons into character-level Chinese NER by lattices. |
| Outcome: | The proposed extension performs 11.4 times faster than state-of-the-art methods while retaining the rich long-term dependencies. |
Modeling Layout Reading Order as Ordering Relations for Visually-rich Document Understanding (2024.emnlp-main)
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Chong Zhang, Yi Tu, Yixi Zhao, Chenshu Yuan, Huan Chen, Yue Zhang, Mingxu Chai, Ya Guo, Huijia Zhu, Qi Zhang, Tao Gui
| Challenge: | Existing models of layout reading order do not convey the complete reading order information in the layout. |
| Approach: | They propose to model layout reading order as ordering relations over layout elements . they propose a reading-order-relation-enhancing pipeline to improve model performance . |
| Outcome: | The proposed model outperforms existing models on a visual-rich document dataset and on eight cross-domain VrD-IE/QA tasks without targeted optimization. |
PEAR: Planner-Executor Agent Robustness Benchmark (2026.findings-eacl)
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| Challenge: | Existing studies examine isolated attack surfaces or specific scenarios, leaving a lack of holistic understanding of MAS vulnerabilities. |
| Approach: | They propose a benchmark to evaluate the utility and vulnerability of planner–executor MAS. |
| Outcome: | The proposed benchmark evaluates planner–executor MAS on a widely adopted design. |
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. |
AwarenessBench: Assessing Cognitive Capabilities of Language Models (2026.acl-long)
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Xiaojian Li, Rongwu Xu, Tianyun Zhang, Yue Wang, Shuo Chen, Qiner Lyu, Briana Zhang, Peiran Yang, Kyle Xue Chen, Haoyuan Shi, Yu Wang, Wei Xu
| Challenge: | Language models exhibit increasingly consciousness-like behaviors, requiring a baseline to evaluate their cognitive abilities. |
| Approach: | They propose a benchmark to assess the cognitive abilities of language models (LMs) they compare 18 state-of-the-art LMs to human models in metacognition, self-awareness, social awareness and situational awareness . |
| Outcome: | Evaluating 18 state-of-the-art LMs, they find they consistently surpass baselines . but most models fall short in metacognition and self-awareness, the study finds . |
Categorizing Semantic Representations for Neural Machine Translation (2022.coling-1)
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| Challenge: | Modern neural machine translation models suffer limitation in compositional generalization, resulting in weakened translation performance on unseen compounds. |
| Approach: | They propose to introduce categorization to the contextualized representations to improve generalization by reducing sparsity and overfitting. |
| Outcome: | The proposed method reduces compositional generalization error rates by 24% on a dedicated MT dataset. |
RiskLab: A Controlled Toolkit for Probing Emergent Risks in LLM-Based Multi-Agent Systems (2026.acl-demo)
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Yu Jiang, Wenjie Wang, Yue Huang, Yanbo Wang, Zhenhong Zhou, Xiuying Chen, Yang Liu, Pin-Yu Chen, Wei Wang, Xiangliang Zhang
| Challenge: | Recent advances in large language model (LLM) agents have accelerated deployment of multi-agent systems for complex tasks. |
| Approach: | They propose an open-source toolkit for instantiating, probing, and measuring emergent risks in LLM-based multi-agent systems under controlled conditions. |
| Outcome: | The proposed toolkit is based on a structured topology–environment–protocol–agent–task quintuple enabling reproducible studies of how communication structure, coordination mechanisms, and incentives shape system-level risks. |
Show Me More Details: Discovering Hierarchies of Procedures from Semi-structured Web Data (2022.acl-long)
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| Challenge: | Existing work has treated procedures as shallow structures without modeling the parent-child relation. |
| Approach: | They propose to construct an open-domain hierarchical knowledge-base (KB) of procedures based on wikiHow . they link steps in an article to other articles with similar goals, recursively building the KB . |
| Outcome: | The proposed method significantly outperforms baselines according to automatic evaluation, human judgment, and application to downstream tasks such as instructional video retrieval. |
ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrieval (2023.findings-acl)
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| Challenge: | Recent studies show that large pretrained language models can generate training data with no task-specific or cross-task data. |
| Approach: | They propose a retrieval-enhanced framework to create training data from a general-domain unlabeled corpus. |
| Outcome: | The proposed framework achieves 4.3% gain over baselines and saves 70% of time compared with baselines using large language models. |
FRVA: Fact-Retrieval and Verification Augmented Entailment Tree Generation for Explainable Question Answering (2024.findings-acl)
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| Challenge: | Existing methods for generating a entailment tree exhibit the reasoning chains from knowledge facts to predicted answers, but they have large fact search spaces and error accumulation problems resulting in the generation of invalid steps. |
| Approach: | They propose a Fact-Retrieval and Verification Augmented bidirectional entailment tree generation method that contains two systems. |
| Outcome: | The proposed method outperforms existing models and achieves state-of-the-art performance in fact selection and structural correctness. |
UrbanVideo-Bench: Benchmarking Vision-Language Models on Embodied Intelligence with Video Data in Urban Spaces (2025.acl-long)
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Baining Zhao, Jianjie Fang, Zichao Dai, Ziyou Wang, Jirong Zha, Weichen Zhang, Chen Gao, Yue Wang, Jinqiang Cui, Xinlei Chen, Yong Li
| Challenge: | Large multimodal models exhibit remarkable intelligence, yet their embodied cognitive abilities during motion in open-ended urban aerial spaces remain to be explored. |
| Approach: | They propose a benchmark to evaluate whether large multimodal models can process continuous first-person visual observations like humans. |
| Outcome: | The proposed model can process first-person visual observations like humans, enabling recall, perception, reasoning, and navigation. |
LLM-as-a-Coauthor: Can Mixed Human-Written and Machine-Generated Text Be Detected? (2024.findings-naacl)
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Qihui Zhang, Chujie Gao, Dongping Chen, Yue Huang, Yixin Huang, Zhenyang Sun, Shilin Zhang, Weiye Li, Zhengyan Fu, Yao Wan, Lichao Sun
| Challenge: | Current research focuses on purely MGT detection without adequately addressing mixed scenarios including AI-revised Human-Written Text (HWT) and human-revealed MGT. |
| Approach: | They define mixtext, a form of mixed text involving both AI and human-generated content, and then use a MixSet dataset to assess their effectiveness. |
| Outcome: | The proposed detectors struggle to identify mixtext, particularly in dealing with subtle modifications and style adaptability. |
RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation (2024.emnlp-demo)
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Xuanwang Zhang, Yun-Ze Song, Yidong Wang, Shuyun Tang, Xinfeng Li, Zhengran Zeng, Zhen Wu, Wei Ye, Wenyuan Xu, Yue Zhang, Xinyu Dai, Shikun Zhang, Qingsong Wen
| Challenge: | Existing research on Retrieval Augmented Generation (RAG) does not address the problem of hallucinations and real-time updating of knowledge. |
| Approach: | They propose a modular open-source library to equip LLMs with external knowledge. |
| Outcome: | The proposed approach reduces the need for expensive open-source tools and lacks fair comparisons between novel RAG algorithms. |
Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach (2020.acl-main)
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| Challenge: | incorporating syntactic structure into language models has been a challenge since the 1990s. |
| Approach: | They propose to use syntactic information to integrate syntastic structure into neural language models by providing ground truth parse trees as additional training signals. |
| Outcome: | The proposed model achieves lower perplexity and better quality when ground truth parse trees are provided as training signals. |
AutoFigure-Edit: Generating Editable Scientific Illustrations via Reference-Guided Styling (2026.acl-demo)
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Zhen Lin, Qiujie Xie, Minjun Zhu, Shichen Li, QiYao Sun, Enhao Gu, Yiran Ding, Ke Sun, Fang Guo, Panzhong Lu, Zhiyuan Ning, Yixuan Weng, Yue Zhang
| Challenge: | Existing automated systems for scientific illustrations are limited in editability, stylistic controllability, and efficiency. |
| Approach: | They propose an end-to-end system that generates fully editable scientific illustrations from long-form scientific text while enabling flexible style adaptation through user-provided reference images. |
| Outcome: | The proposed system generates fully editable scientific illustrations from long-form scientific texts while enabling flexible style adaptation through user-provided reference images. |
Layer-Wise Multi-View Learning for Neural Machine Translation (2020.coling-main)
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| Challenge: | Existing approaches to neural machine translation are limited to the topmost encoder layer’s context representation and cannot perceive the lower encoder layers. |
| Approach: | They propose a layer-wise multi-view learning approach to solve this problem by incorporating an auxiliary view into the model. |
| Outcome: | The proposed model can achieve stable results over multiple strong baselines and is agnostic to network architectures. |
CENTAUR: Bridging the Impossible Trinity of Privacy, Efficiency, and Performance in Privacy-Preserving Transformer Inference (2025.acl-long)
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Jinglong Luo, Guanzhong Chen, Yehong Zhang, Shiyu Liu, Hui Wang, Yue Yu, Xun Zhou, Yuan Qi, Zenglin Xu
| Challenge: | Existing privacy-preserving Transformer Inference frameworks suffer from high computational overhead and performance losses. |
| Approach: | They propose a framework that integrates random permutations and SMPC to address the "impossible trinity" CENTAUR resists diverse data reconstruction attacks and boosts inference speed by 5.030.4 times . |
| Outcome: | CENTAUR achieves an unprecedented balance between privacy, efficiency, and performance. |
SConU: Selective Conformal Uncertainty in Large Language Models (2025.acl-long)
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| Challenge: | Existing frameworks fail to identify outliers that violate the exchangeability assumption, leading to unbounded miscoverage rates and unactionable prediction sets. |
| Approach: | They propose a method that implements significance tests to determine whether a given sample deviates from the uncertainty distribution of the calibration set. |
| Outcome: | The proposed approach facilitates rigorous management of miscoverage rates across single-domain and interdisciplinary contexts, and enhances the efficiency of predictions. |
MobileWorld: Benchmarking Autonomous Mobile Agents in Agent-User Interactive and MCP-Augmented Environments (2026.acl-long)
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Quyu Kong, Xu Zhang, Zhenyu Yang, Nolan Gao, Chen Liu, Panrong Tong, Chenglin Cai, Hanzhang Zhou, Jianan Zhang, Liangyu Chen, Zhidan Liu, Steven Hoi, Yue Wang
| Challenge: | AndroidWorld is the dominant mobile GUI agent evaluation benchmark, but its success rates are low . despite reproducible emulator environment, it lacks key application categories such as e-commerce and enterprise communication. |
| Approach: | They propose a benchmark for mobile GUI agents that reflects real-world usage through long-horizon, cross-application workflows. |
| Outcome: | The proposed framework achieves over 90% success rates, while AndroidWorld is the dominant benchmark. |
Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents (2021.acl-short)
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| Challenge: | Faceted summarization provides briefings of a document from different perspectives. |
| Approach: | They propose a faceted summarization benchmark built on Emerald journal articles . they propose faceted models that bring structure into faceted documents . |
| Outcome: | The proposed benchmark is based on Emerald journal articles and covers a diverse range of domains. |
Does Chinese BERT Encode Word Structure? (2020.coling-main)
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| Challenge: | Existing work has focused on analyzing the features captured by representative models such as BERT . however, little work has investigated word features for character languages such as Chinese . |
| Approach: | They investigate Chinese BERT using attention weight distribution statistics and probing tasks to understand word features. |
| Outcome: | The proposed model improves syntactic, semantic and word sense knowledge on a wide range of NLP tasks. |
Graph Pre-training for AMR Parsing and Generation (2022.acl-long)
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| Challenge: | Abstract meaning representation (AMR) highlights the core semantic information of text in a graph structure. |
| Approach: | They propose two graph auto-encoding strategies for graph-to-graph pre-training and four tasks to integrate text and graph information during pre-tuning to improve structure awareness. |
| Outcome: | The proposed model is superior to pre-trained language models on AMR parsing and AMR-to-text generation tasks. |
Cross-Lingual Pitfalls: Automatic Probing Cross-Lingual Weakness of Multilingual Large Language Models (2025.acl-long)
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| Challenge: | Large language models have achieved remarkable success in Natural Language Processing, yet their cross-lingual consistency remains a significant challenge. |
| Approach: | They propose a method to identify cross-lingual weaknesses in Large Language Models . they construct bilingual question pairs that expose performance discrepancies between English and target languages . |
| Outcome: | The proposed method uncovers over 50% accuracy drops in target languages across models. |
Knowledge Conflicts for LLMs: A Survey (2024.emnlp-main)
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| Challenge: | This survey examines knowledge conflicts for large language models (LLMs) this survey aims to shed light on strategies for improving the robustness of LLMs . |
| Approach: | They focus on three categories of knowledge conflicts: context-memory, inter-context, and intra-membry conflict. |
| Outcome: | The findings highlight the challenges faced by large language models when blending contextual and parametric knowledge. |
SR-LLM: Rethinking the Structured Representation in Large Language Model (2025.acl-long)
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Jiahuan Zhang, Tianheng Wang, Ziyi Huang, Yulong Wu, Hanqing Wu, DongbaiChen DongbaiChen, Linfeng Song, Yue Zhang, Guozheng Rao, Kaicheng Yu
| Challenge: | Structured representations have long been pivotal in computational linguistics, but their role remains ambiguous in the Large Language Models (LLMs) era. |
| Approach: | They propose a framework that integrates structured representations into LLMs from training-free and training-dependent perspectives. |
| Outcome: | The proposed framework integrates structured representations through natural language descriptions in LLM prompts while augmenting the model’s inference capability through fine-tuning on linguistically described structured representation. |
Alleviating Hallucinations of Large Language Models through Induced Hallucinations (2025.findings-naacl)
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| Challenge: | Existing studies have shown that large language models generate inaccurate or fabricated information, a phenomenon known as hallucinations. |
| Approach: | They propose a simple strategy to induce-then-contrast decode LLMs to enhance their factuality . they first induce hallucinations from the original model and penalize them . |
| Outcome: | The proposed strategy improves factuality of large language models across task formats, model sizes, and model families. |
How Likely Do LLMs with CoT Mimic Human Reasoning? (2025.coling-main)
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| Challenge: | Using chain-of-thought to elicit reasoning capabilities is not always effective and accurate. |
| Approach: | They compare the reasoning process of LLMs with humans to understand the causal chain . they find that LLM deviates from the ideal causal chain, resulting in spurious correlations . |
| Outcome: | The proposed method does not improve performance or accurately represent reasoning processes in LLMs. |
ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extraction (2024.findings-acl)
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Henry Zou, Vinay Samuel, Yue Zhou, Weizhi Zhang, Liancheng Fang, Zihe Song, Philip Yu, Cornelia Caragea
| Challenge: | Existing datasets for attribute value extraction focus on explicit attribute values while neglecting the implicit ones. |
| Approach: | They present a multimodal dataset for implicit attribute value extraction that includes AVE and multimodality. |
| Outcome: | The proposed dataset includes 68k training and 1.6k testing data across five domains. |
Targeted Exploration via Unified Entropy Control for Reinforcement Learning (2026.findings-acl)
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| Challenge: | Existing methods for group relative policy optimization suffer from entropy collapse . Existing exploration methods introduce additional bias or variance during exploration, making it difficult to maintain stability. |
| Approach: | They propose a framework that provides targeted mechanisms for exploration and stabilization. |
| Outcome: | The proposed framework expands search space on difficult prompts while preventing entropy growth uncontrollably. |
Learning Target-Specific Representations of Financial News Documents For Cumulative Abnormal Return Prediction (C18-1)
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| Challenge: | Recent work considers learning dense representations for news titles and abstracts . text representations can address the sparsity of discrete indicators in statistical models . |
| Approach: | They propose to use news abstracts to combine the most informative sentences in news content to learn dense representations for text elements. |
| Outcome: | The proposed model can be used to estimate abnormal returns of companies when compared to titles and abstracts. |
Explicit Syntactic Guidance for Neural Text Generation (2023.acl-long)
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| Challenge: | Existing text generation models follow the sequence-to-sequence paradigm . generative grammar suggests humans generate language by learning language grammar . |
| Approach: | They propose a syntax-guided generation schema that searches the syntax tree in a top-down direction. |
| Outcome: | The proposed method outperforms autoregressive baselines on paraphrase generation and machine translation. |
Dissecting Logical Reasoning in LLMs: A Fine-Grained Evaluation and Supervision Study (2025.findings-emnlp)
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Yujun Zhou, Jiayi Ye, Zipeng Ling, Yufei Han, Yue Huang, Haomin Zhuang, Zhenwen Liang, Kehan Guo, Taicheng Guo, Xiangqi Wang, Xiangliang Zhang
| Challenge: | Existing benchmarks that rely on final-answer accuracy fail to capture the quality of the reasoning process. |
| Approach: | They propose a fine-grained evaluation framework that assesses logical reasoning across three dimensions: overall accuracy, stepwise soundness, and representation-level probing. |
| Outcome: | The proposed framework assesses logical reasoning across three dimensions: overall accuracy, stepwise soundness, and representation-level probing. |
On the Role of Pre-trained Language Models in Word Ordering: A Case Study with BART (2022.coling-1)
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| Challenge: | Existing work uses linear models and neural networks for word ordering, yet pre-trained language models have not been studied in word ordering. |
| Approach: | They propose a constrained language generation task using unordered words as input. |
| Outcome: | The proposed model is able to perform better than existing models and proves to be reliable. |
Leveraging Context Information for Natural Question Generation (N18-2)
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| Challenge: | Existing work for natural question generation ignores the input passage or hard-codes answer positions. |
| Approach: | They propose a model that matches the answer with the passage before generating a question. |
| Outcome: | The proposed model outperforms the state-of-the-art model using rich features. |
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. |
Relation-aware Ensemble Learning for Knowledge Graph Embedding (2023.emnlp-main)
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| Challenge: | Existing methods to explore semantics of knowledge graphs have been proposed to explore these semantics in distinct ways. |
| Approach: | They propose to leverage existing methods in relation-aware manner to learn an ensemble by leveraging existing methods. |
| Outcome: | The proposed method has the same computation cost as general ensemble methods but with much better performance on benchmark datasets. |
Pro-Woman, Anti-Man? Identifying Gender Bias in Stance Detection (2024.findings-acl)
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| Challenge: | Gender bias has been widely observed in NLP models, which can perpetuate harmful stereotypes and discrimination. |
| Approach: | They construct a dataset to measure gender bias in stance detection using 36k samples . they find that all models are gender-biased and prone to classify sentences that contain male nouns as Against and those with female noun as Favor . |
| Outcome: | The proposed dataset shows that all models are gender-biased and prone to classify sentences that contain male nouns as Against and those with female noun as Favor. |
Automatic Evaluation of Attribution by Large Language Models (2023.findings-emnlp)
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| Challenge: | Generative large language models (LLMs) incorporate external references to generate and support claims. however, evaluating the attribution remains an open problem. |
| Approach: | They investigate automatic evaluation of attribution given by large language models . they define different types of attributed errors and then explore two approaches . |
| Outcome: | The proposed methods highlight promising signals and challenges. |
MAGE: Machine-generated Text Detection in the Wild (2024.acl-long)
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Yafu Li, Qintong Li, Leyang Cui, Wei Bi, Zhilin Wang, Longyue Wang, Linyi Yang, Shuming Shi, Yue Zhang
| Challenge: | Existing research has focused on evaluating detection methods for specific domains or language models. |
| Approach: | They build a testbed to detect texts from diverse human writings and LLMs using different detection methods. |
| Outcome: | Empirical results show that the top performing detector can identify 84.12% out-of-domain texts generated by a new LLM, indicating the feasibility for application scenarios. |
What Have We Achieved on Non-autoregressive Translation? (2024.findings-acl)
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| Challenge: | Existing studies have shown that non-autoregressive (NAT) methods underperform autoregressive methods (AT) however, their evaluation using BLEU has been shown to weakly correlate with human annotations. |
| Approach: | They propose to evaluate four representative NAT methods using BLEU to narrow the performance gap between autoregressive and autoregressive translations. |
| Outcome: | The proposed methods underperform NAT and autoregressive methods under more reliable evaluation metrics. |
Sentiment Analysis in the Era of Large Language Models: A Reality Check (2024.findings-naacl)
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| Challenge: | Sentiment analysis (SA) has been a long-standing research area in natural language processing. |
| Approach: | They propose a benchmark to evaluate LLMs' SA abilities and propose 'sentiEval' benchmark to be used for a more comprehensive evaluation. |
| Outcome: | The proposed benchmark outperforms small language models on 26 datasets on 13 tasks and compared them with LLMs trained on domain-specific datasets. |
SeaLLMs - Large Language Models for Southeast Asia (2024.acl-demos)
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Xuan-Phi Nguyen, Wenxuan Zhang, Xin Li, Mahani Aljunied, Zhiqiang Hu, Chenhui Shen, Yew Ken Chia, Xingxuan Li, Jianyu Wang, Qingyu Tan, Liying Cheng, Guanzheng Chen, Yue Deng, Sen Yang, Chaoqun Liu, Hang Zhang, Lidong Bing
| Challenge: | Existing large language models favor high-resource languages, such as English, at the expense of low-resourced and regional languages. |
| Approach: | They propose a series of language models that specifically focuses on Southeast Asian languages. |
| Outcome: | SeaLLM models outperform ChatGPT-3.5 in non-Latin languages by large margins . linguistic disparity impedes access to state-of-the-art AI technologies for non-English-speaking populations . |
Mitigating the Privacy Issues in Retrieval-Augmented Generation (RAG) via Pure Synthetic Data (2025.emnlp-main)
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Shenglai Zeng, Jiankun Zhang, Pengfei He, Jie Ren, Tianqi Zheng, Hanqing Lu, Han Xu, Hui Liu, Yue Xing, Jiliang Tang
| Challenge: | Existing literature suggests that RAG systems may face privacy issues when the retrieval process involves private data. |
| Approach: | They propose a two-stage synthetic data generation paradigm that uses attributes to preserve contextual information from the original data. |
| Outcome: | The proposed approach preserves key contextual information from the original data while reducing privacy risks. |
Hierarchically-Refined Label Attention Network for Sequence Labeling (D19-1)
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| Challenge: | Conditional random fields (CRF) is a powerful model for statistical sequence labeling, but it does not give much information gain over strong neural encoding. |
| Approach: | They propose a hierarchically-refined label attention network which captures potential long-term label dependency by giving each word incrementally refined label distributions with hierarchical attention. |
| Outcome: | The proposed model improves POS tagging accuracy and speeds up training and testing compared to the current model. |
CFlowPsyD: An Analysis-Enhanced Dataset for Asynchronous Psychological Counseling through Self-Optimizing Multi-Agent Framework (2026.findings-acl)
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| Challenge: | Asynchronous psychological counseling (APC) is a crucial mental health service modality that transcends temporal and spatial constraints. |
| Approach: | They propose a self-optimizing multi-agent framework for counseling dialogue generation, CFlowPsy, which utilizes real anonymized counseling cases as seed data to synthesize diverse problem-solving-oriented APC conversations through large language models. |
| Outcome: | The proposed framework synthesizes diverse problem-solving-oriented APC conversations through large language models. |
Empirical Prior for Text Autoencoders (2024.findings-emnlp)
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| Challenge: | Variational Autoencoders (VAE) are used to train generative models with latent variables. |
| Approach: | They propose a transition from Variational Autoencoders (VAE) to text autoencodeurs (AE) which model a compact latent space and preserves the capability of the language model itself. |
| Outcome: | The proposed method generates higher quality and more diverse text than the VAE-based Transformer baselines, and is more efficient than previous approaches. |
LexMatcher: Dictionary-centric Data Curation for LLM-based Machine Translation (2024.findings-emnlp)
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| Challenge: | emergence of large language models (LLMs) has brought about new opportunities for machine translation. |
| Approach: | They propose a method for data curation that supplements the infrequent senses of polysemous words. |
| Outcome: | The proposed method outperforms established baselines on the WMT2022 test sets and is applicable to other pre-trained models. |
Dynamics of Instruction Fine-Tuning for Chinese Large Language Models (2025.coling-main)
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| Challenge: | Instruction tuning is a burgeoning method to elicit the general intelligence of Large Language Models. |
| Approach: | They investigate the effects of data quantity, model size, and data construction methods on instruction tuning for Chinese LLMs. |
| Outcome: | The proposed model includes over 40,000 high-quality instruction instances covering ten underlying abilities. |
Exploring Reasoning Reward Model for Agents (2026.findings-acl)
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Kaixuan Fan, Kaituo Feng, Manyuan Zhang, Tianshuo Peng, Zhixun Li, Yilei Jiang, Shuang Chen, Xiangyu Yue
| Challenge: | Existing methods for agentic reinforcement learning rely on sparse outcome-based reward for training, leading to suboptimal results. |
| Approach: | They propose an agent-based reward model that produces structured feedback for agentic trajectories, including an explicit reasoning trace and a focused critique. |
| Outcome: | The proposed model produces structured feedback for agentic trajectories including an explicit reasoning trace, a focused critique, and an overall score that evaluates process performance. |
ZPR2: Joint Zero Pronoun Recovery and Resolution using Multi-Task Learning and BERT (2020.acl-main)
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| Challenge: | Zero pronoun recovery and resolution aim at recovering the dropped pronounce and pointing out its anaphoric mentions. |
| Approach: | They propose to solve two tasks together to recover the dropped pronoun and point out its anaphoric mentions. |
| Outcome: | The proposed model outperforms previous state of the arts benchmarks on two benchmarks. |
COSMOS: Connectivity-Oriented Submodular Maximization for Optimal Subgraph Retrieval (2026.acl-long)
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| Challenge: | Existing paradigms treat facts independently or employ myopic search, failing to optimize collective subgraph utility. |
| Approach: | They propose a framework that formalizes evidence retrieval as a constrained submodular maximization problem. |
| Outcome: | The proposed framework captures the trade-off between information relevance and structural complexity. |
AdaPrompt: Adaptive Model Training for Prompt-based NLP (2022.findings-emnlp)
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| Challenge: | Prompt-based learning can tackle zero-shot and few-shot NLP tasks . authors propose a method that makes use of pre-trained language models . |
| Approach: | They propose to map NLP tasks into natural language prompts, which are then filled by pre-trained language models. |
| Outcome: | The proposed method outperforms standard prompt-based methods in few-shot settings. |
Self-Training with Differentiable Teacher (2022.findings-naacl)
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| Challenge: | Existing methods for self-training are interpreted as teacher-student frameworks, where the teacher generates pseudo-labels and the student makes predictions. |
| Approach: | They propose a differentiable self-training method that treats teacher-student as a Stackelberg game where a leader is always in a more advantageous position than a follower. |
| Outcome: | The proposed model outperforms existing methods on semi- and weakly-supervised learning tasks on semi and weak supervised tasks. |
Entity Relation Extraction as Dependency Parsing in Visually Rich Documents (2021.emnlp-main)
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| Challenge: | Existing studies on key information extraction from visually rich documents focus on labeling the text within bounding boxes, while relations between words are unexplored. |
| Approach: | They propose to use a dependency parsing model to extract semantic entities from visually rich documents by combining entity labeling and relation extraction tasks. |
| Outcome: | The proposed model achieves 65.96% F1 score on the FUNSD dataset. |
YEDDA: A Lightweight Collaborative Text Span Annotation Tool (P18-4)
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| Challenge: | Existing annotation tools do not consider post-annotation quality analysis due to inter-annotator disagreement. |
| Approach: | They propose a lightweight but efficient open-source tool for text span annotation that can be used for collaborative user annotation and administrator evaluation and analysis. |
| Outcome: | The proposed system reduces the annotation time by half compared with existing tools and the time can be compressed by 16.47% through intelligent recommendation. |
Unveiling Attractor Cycles in Large Language Models: A Dynamical Systems View of Successive Paraphrasing (2025.acl-long)
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| Challenge: | Dynamical systems theory provides a framework for understanding iterative processes and evolution over time. |
| Approach: | They propose to apply this perspective to large language models which iteratively map input text to output text and re-express meaning with linguistic variation. |
| Outcome: | The proposed model reveals that paraphrases re-express meaning with linguistic variation limiting linguistic diversity . |
Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning (2022.acl-long)
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| Challenge: | Weakly-supervised learning (WSL) has shown promising results in addressing label scarcity on many NLP tasks, but manual designing a comprehensive, high-quality set of labeling rules is tedious and difficult. |
| Approach: | They propose a weakly-supervised learning model that iterates and discovers new labeling rules from data to improve the WSL model. |
| Outcome: | The proposed model outperforms state-of-the-art models on four tasks and bridges the gaps with fully supervised models. |
Semantic Representation for Dialogue Modeling (2021.acl-long)
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| Challenge: | Existing models for dialogue modeling lack ability to represent core semantics, such as ignoring important entities. |
| Approach: | They develop an algorithm to construct dialogue-level AMR graphs from sentence-level data and explore two ways to incorporate AMRs into dialogue modeling. |
| Outcome: | The proposed model is superior to existing models on dialogue understanding and response generation tasks. |
One2Set + Large Language Model: Best Partners for Keyphrase Generation (2024.emnlp-main)
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| Challenge: | Existing selection methods make redundant selections, causing poor recall and accuracy. |
| Approach: | They propose a framework to generate keyphrases from a one2set-based model and an LLM as selector. |
| Outcome: | The proposed framework surpasses state-of-the-art models in absent keyphrase prediction. |
A Rank-Based Similarity Metric for Word Embeddings (P18-2)
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| Challenge: | Word Embeddings have become a standard for word representations, with vector cosine being the only similarity metric. |
| Approach: | They propose to use rank-based similarity estimation metrics to measure word similarity . they find WE outperforms vector cosine in the recent outlier detection task . |
| Outcome: | The proposed rank-based measure outperforms vector cosine in the recent outlier detection task. |
Consistency Regularization Training for Compositional Generalization (2023.acl-long)
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| Challenge: | Existing neural models have difficulty generalizing to unseen combinations of seen components. |
| Approach: | They propose to improve the capability of Transformer on compositional generalization by consistency regularization training without modifying model architectures. |
| Outcome: | The proposed model performs well on semantic parsing and machine translation benchmarks. |
LMR-BENCH: Evaluating LLM Agent’s Ability on Reproducing Language Modeling Research (2025.emnlp-main)
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Shuo Yan, Ruochen Li, Ziming Luo, Zimu Wang, Daoyang Li, Liqiang Jing, Kaiyu He, Peilin Wu, Juntong Ni, George Michalopoulos, Yue Zhang, Ziyang Zhang, Mian Zhang, Zhiyu Chen, Xinya Du
| Challenge: | Large language model (LLM) agents have demonstrated remarkable potential in advancing scientific discovery, but their capability in reproducing code from research papers remains underexplored. |
| Approach: | They propose to evaluate LLM agents' ability to reproduce scientific research papers by analyzing code reproduction tasks from 23 research papers published in top-tier NLP venues. |
| Outcome: | The proposed benchmark systematically evaluates the capability of large language model (LLM) agents on code reproduction from Language Modeling Research. |
GEMINI: Controlling The Sentence-Level Summary Style in Abstractive Text Summarization (2023.emnlp-main)
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| Challenge: | Existing models that mimic human summarization techniques are difficult to imitate. |
| Approach: | They propose an adaptive model that integrates a rewriter and a generator to mimic the sentence rewriting and abstracting techniques. |
| Outcome: | The proposed model outperforms baselines on WikiHow and on other datasets. |
A Survey on Open Information Extraction from Rule-based Model to Large Language Model (2024.findings-emnlp)
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Liu Pai, Wenyang Gao, Wenjie Dong, Lin Ai, Ziwei Gong, Songfang Huang, Li Zongsheng, Ehsan Hoque, Julia Hirschberg, Yue Zhang
| Challenge: | Open Information Extraction (OpenIE) is a key NLP task aimed at extracting structured information from unstructured text sources. |
| Approach: | They propose to categorize OpenIE into rule-based, neural, and pre-trained large language models and discuss each within a chronological framework. |
| Outcome: | The paper categorizes OpenIE approaches into rule-based, neural, and pre-trained large language models, discussing each within a chronological framework. |
Contrastive Attention Mechanism for Abstractive Sentence Summarization (D19-1)
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| Challenge: | Existing attention mechanisms for abstractive sentence summarization are based on rule-based methods and large-scale training corpora. |
| Approach: | They propose a contrastive attention mechanism that extends the sequence-to-sequence framework for abstractive sentence summarization task. |
| Outcome: | The proposed mechanism improves the state-of-the-art on the abstractive sentence summarization task. |
MelTrim: Coarse-to-Fine Data Pruning for Speech Classification (2026.findings-acl)
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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. |
ALLabel: Three-stage Active Learning for LLM-based Entity Recognition using Demonstration Retrieval (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) are increasingly used to solve the entity recognition task. |
| Approach: | They propose a framework to select the most informative and representative samples for LLM in-context learning. |
| Outcome: | The proposed framework outperforms baselines on three specialized domain datasets. |
Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation (2023.acl-long)
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Yulong Chen, Huajian Zhang, Yijie Zhou, Xuefeng Bai, Yueguan Wang, Ming Zhong, Jianhao Yan, Yafu Li, Judy Li, Xianchao Zhu, Yue Zhang
| Challenge: | Existing work on cross-lingual summarization (CLS) does not consider crosslingual sources for summarizing. |
| Approach: | They propose a cross-lingual conversation summarization benchmark that explicitly considers source context. |
| Outcome: | The proposed method surpasses baselines on ConvSumX and 3 widely-used manual annotations. |
Financial Sentiment Analysis: An Investigation into Common Mistakes and Silver Bullets (2020.coling-main)
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| Challenge: | Recent dominance of machine learning-based natural language processing methods has overemphasized model accuracies rather than studying the reasons behind their errors. |
| Approach: | They investigate the error patterns of some widely acknowledged sentiment analysis methods in the finance domain. |
| Outcome: | The proposed models are based on the existing models and have important clues for improving them. |
What Have We Achieved on Text Summarization? (2020.emnlp-main)
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| Challenge: | Existing methods for text summarization have been investigated, but there are still gaps between them and human professionals. |
| Approach: | They analyze 8 major sources of errors on 10 representative summarization models manually. |
| Outcome: | Aiming to gain more understanding of summarization systems with respect to their strengths and limitations on a fine-grained syntactic and semantic level, we use 8 major sources of errors on 10 representative summarizing models. |
Solving Aspect Category Sentiment Analysis as a Text Generation Task (2021.emnlp-main)
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| Challenge: | Existing methods for Aspect category sentiment analysis use pre-trained language models to learn aspect category-specific representations. |
| Approach: | They propose to make use of pre-trained language models by casting the ACSA tasks into natural language generation tasks, using natural language sentences to represent the output. |
| Outcome: | The proposed method gives the best reported results, having large advantages in few-shot and zero-shot settings. |
Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter (2021.acl-long)
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| Challenge: | Existing methods for Chinese sequence labelling only fuse lexicon features via a shallow and random initialized sequence layer and do not integrate them into the bottom layers of BERT. |
| Approach: | They propose a Lexicon Enhanced BERT model which integrates external lexicon knowledge into BERT layers directly by a lexiccon Adapter layer. |
| Outcome: | The proposed model integrates external lexicon knowledge into BERT layers directly by a Lexicon Adapter layer. |
DFAMS: Dynamic-flow guided Federated Alignment based Multi-prototype Search (2026.acl-long)
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Zhibang Yang, Xinke Jiang, Rihong Qiu, Ruiqing Li, Yihang Zhang, Yue Fang, Yongxin Xu, Hongxin Ding, Xu Chu, Junfeng Zhao, Yasha Wang
| Challenge: | Existing methods for ambiguous queries struggle to retrieve high-quality documents . DFAMS outperforms advanced FR methods by 14.37% in knowledge classification accuracy . |
| Approach: | They propose a framework that leverages dynamic information flow to identify latent query intents and construct semantically aligned knowledge partitions for accurate retrieval across heterogeneous sources. |
| Outcome: | The proposed framework outperforms existing methods in classification accuracy and retrieval recall tests. |
Enhancing Generalization in Natural Language Inference by Syntax (2020.findings-emnlp)
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| Challenge: | Pre-trained language models such as BERT have the state-of-the-art performance on natural language inference (NLI). |
| Approach: | They propose to use dependency trees to enhance generalization of BERT in a natural language inference task by leveraging on a graph convolutional network to represent a syntax-based matching graph with heterogeneous matching patterns. |
| Outcome: | The proposed method makes BERT more robust on syntactic changes. |
LogiCoT: Logical Chain-of-Thought Instruction Tuning (2023.findings-emnlp)
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| Challenge: | Recent work on self-instruction tuning has focused on enhancing the general proficiency of models. |
| Approach: | They propose a new instruction-tuning dataset for Logical Chain-of-Thought reasoning with GPT-4 that harvests instructions for prompting GPT to generate chain-of thought rationales. |
| Outcome: | The proposed dataset enables the model to generate chain-of-thought rationales with GPT-4. |
AMR Parsing with Latent Structural Information (2020.acl-main)
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| Challenge: | Abstract Meaning Representations (AMRs) capture sentence-level semantics structural representations to broad-coverage natural sentences. |
| Approach: | They investigate parsing AMR with explicit dependency structures and interpretable latent structures. |
| Outcome: | The proposed model achieves best results on both AMR 2.0 and AMR 1.0 . the proposed model has been adopted in downstream NLP tasks, including text summarization and question answering. |
DeepReview: Improving LLM-based Paper Review with Human-like Deep Thinking Process (2025.acl-long)
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| Challenge: | Existing Large Language Models (LLMs) face limited domain expertise, hallucinated reasoning, and a lack of structured evaluation. |
| Approach: | They propose a multi-stage framework to emulate expert reviewers by incorporating structured analysis, literature retrieval, and evidence-based argumentation. |
| Outcome: | The proposed model outperforms CycleReviewer-70B with fewer tokens and achieves 88.21% and 80.20% win rates. |
The Curse of Verbalization: How Presentation Order Constrains LLM Reasoning (2026.findings-eacl)
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| Challenge: | Xu et al., 2025) found that LLMs struggle when programs execute in an unaligned order. |
| Approach: | They propose to use esoteric programming languages to evaluate LLMs' reasoning abilities. |
| Outcome: | The proposed model improves reasoning performance across state-of-the-art models by restructuring problems to align the presentation order with the order of utilization. |
Evidence-Driven Retrieval Augmented Response Generation for Online Misinformation (2024.naacl-long)
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| Challenge: | Existing methods to generate counter-misinformation responses are often trained end-to-end without external knowledge, resulting in subpar text quality and excessively repetitive responses. |
| Approach: | They propose retrieval augmented response generation for online misinformation (RARG) that collects supporting evidence and generates counter-misinformation responses via reinforcement learning from human feedback. |
| Outcome: | The proposed method outperforms baselines with extensive experiments with in- and cross-domain datasets and consistently generates high-quality counter-misinformation responses. |
ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees (2024.findings-emnlp)
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Zhiyuan Wang, Jinhao Duan, Lu Cheng, Yue Zhang, Qingni Wang, Xiaoshuang Shi, Kaidi Xu, Heng Tao Shen, Xiaofeng Zhu
| Challenge: | Uncertainty quantification (UQ) in natural language generation tasks remains an open challenge . however, black-box uncertainty measures require investigating with the proliferation of LLMs served via APIs. |
| Approach: | They propose a conformal uncertainty measure and a method to transform heuristic uncertainty notions into rigorous prediction sets. |
| Outcome: | Empirical results show that the proposed method outperforms state-of-the-art methods and can provide reliable guarantees for open-ended NLG tasks. |
Movie101v2: Improved Movie Narration Benchmark (2025.acl-long)
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| Challenge: | Automatic movie narration aims to generate video-aligned plot descriptions to assist visually impaired audiences. |
| Approach: | They propose to break down the ultimate goal of automatic movie narration into three stages . they propose a large-scale, bilingual dataset with enhanced data quality . |
| Outcome: | The proposed dataset breaks down the goal of automatic movie narration into three stages . achieving applicable movie narration is a fascinating goal that requires significant research . |
TableLlama: Towards Open Large Generalist Models for Tables (2024.naacl-long)
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| Challenge: | Existing methods for interpreting, augmenting, and querying semi-structured tables require pretraining on tables or special model architecture design. |
| Approach: | They construct a dataset with a variety of tables and tasks for instruction tuning and evaluating LLMs. |
| Outcome: | The proposed model achieves comparable or better performance on 7 out of 8 in-domain tasks compared with the base model on 6 out-of-domain datasets. |
Learning to Generalize for Cross-domain QA (2023.findings-acl)
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| Challenge: | Existing methods for QA are hampered by increased training costs . current methods suffer significant performance degradation when applied to out-of-domain examples. |
| Approach: | They propose a method that combines prompting methods and linear probing with fine-tuning strategy, which does not entail additional cost. |
| Outcome: | The proposed method outperforms state-of-the-art baselines with an average increase in F1 score of 4.5%-7.9%. |
SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup (2020.emnlp-main)
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| Challenge: | Existing active sequence labeling methods use the queried samples alone in each iteration, which is inefficient for leveraging human annotations. |
| Approach: | They propose a data augmentation method to augment queried samples by generating extra labeled sequences in each iteration. |
| Outcome: | The proposed method improves the standard active sequence labeling method by 2.27%–3.75% in terms of F1 scores. |
ECON: On the Detection and Resolution of Evidence Conflicts (2024.emnlp-main)
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Cheng Jiayang, Chunkit Chan, Qianqian Zhuang, Lin Qiu, Tianhang Zhang, Tengxiao Liu, Yangqiu Song, Yue Zhang, Pengfei Liu, Zheng Zhang
| Challenge: | Recent studies have shown that AI generated content is more likely to dominate search results, making it difficult to detect when compared to human-produced content. |
| Approach: | They propose a method for generating diverse, validated evidence conflicts to simulate real-world misinformation scenarios. |
| Outcome: | The proposed method enables the detection of conflicting information in real-world scenarios and shows that weaker models struggle with similar answer conflicts while stronger models show robust performance. |
Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings (2022.acl-long)
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| Challenge: | Contextualized embeddings are expensive and resource-demanding, hence environmentally unfriendly. |
| Approach: | They propose a method to convert contextualized embeddings from pre-trained models into static embeddables using synonym knowledge and weighted vector distribution. |
| Outcome: | The proposed method outperforms baseline embeddings by a large margin through extrinsic and intrinsic tasks. |
HyperMem: Hypergraph Memory for Long-Term Conversations (2026.acl-long)
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| Challenge: | Existing approaches to long-term memory management rely on pairwise relations, causing fragmented retrieval. |
| Approach: | They propose a hypergraph-based hierarchical memory architecture that explicitly models high-order associations using hyperedges. |
| Outcome: | Experiments show that HyperMem achieves state-of-the-art performance with 92.73% accuracy for long-term conversations. |
Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts (2023.emnlp-main)
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| Challenge: | Existing methods for large language models (LLMs) have been used to prompt different reasoning thoughts, such as Chain of Thought and Program of Though. |
| Approach: | They propose a framework that prompts large language models with diverse reasoning thoughts by iterating between different prompting methods. |
| Outcome: | The proposed framework is able to generate multiple reasoning thoughts in 10 popular math reasoning datasets and is orthogonal to recent work that makes improvements on single reasoning methods and can generalise to logical reasoning domain. |
MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning (2023.acl-long)
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| Challenge: | Existing methods for misinformation detection are limited by data scarcity . existing methods fail to detect early-stage misinformation on emerging topics . |
| Approach: | They propose a meta learning based approach for domain adaptive few-shot misinformation detection that leverages limited target examples to provide feedback and guide the knowledge transfer from the source to the target domain. |
| Outcome: | The proposed method improves performance on real-world datasets with reduced parameters. |
Multi-Document Event Extraction Using Large and Small Language Models (2025.emnlp-main)
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| Challenge: | Existing approaches to multi-document event extraction have limited attention . despite its practical significance, this task has inherent challenges . |
| Approach: | They propose a collaborative framework that integrates large language models for multi-step reasoning and fine-tuned small language models to handle key subtasks. |
| Outcome: | The proposed framework outperforms existing methods and provides new insights into collaborative reasoning to tackle the complexities of multi-document event extraction. |
Task Calibration: Calibrating Large Language Models on Inference Tasks (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have shown impressive zero-shot performance on inference tasks, however, they may suffer from spurious correlations between input texts and output labels, which limits their ability to reason based purely on general language understanding. |
| Approach: | They propose a zero-shot and inference-only calibration method inspired by mutual information which recovers LLM performance through task reformulation. |
| Outcome: | The proposed calibration method improves on 13 benchmarks and prompt templates and can be integrated with other calibration methods. |
PerSphere: A Comprehensive Framework for Multi-Faceted Perspective Retrieval and Summarization (2025.acl-long)
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| Challenge: | Experimental results show that the main challenge lies in long context and perspective extraction. |
| Approach: | They propose a benchmark to facilitate multi-faceted perspective retrieval and summarization . they propose measurable metrics to evaluate the comprehensiveness of the retrieval pipeline . |
| Outcome: | The proposed system breaks free from information silos by combining two opposing claims . it can be used to extract multiple perspectives and improve performance on the platform . |
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. |
Anchoring the Cache: Mitigating Contextual Hallucination in KV-Compressed Long-Context Summarization (2026.acl-long)
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| Challenge: | Recent studies show that KV cache compression can increase hallucination scores in LLMs . modern LLM models support extremely long sequences, but their impact on model hallucinosity remains underexplored. |
| Approach: | They propose a decoding-phase strategy that selectively removes generated KV pairs from retrieval heads responsible for retrieving critical information from source context. |
| Outcome: | The proposed method reduces hallucination across multiple models and datasets while preserving computational efficiency. |
Cross-lingual Terminology Extraction for Translation Quality Estimation (L18-1)
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| Challenge: | Using common statistical measures for termhood and unithood, we identify terms from monolingual texts and investigate the contribution of terminology to translation quality. |
| Approach: | They propose to use common statistical measures for termhood and unithood as features to train classifiers for identifying terms in cross-domain and cross-language settings. |
| Outcome: | The proposed method has shown some reliability in automatically identifying terms in human translations, but drawbacks in handling low frequency terms and term variations shall be dealt with in the future. |
Code-Switching for Enhancing NMT with Pre-Specified Translation (N19-1)
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| Challenge: | Existing methods to constrain NMT use placeholder tags for lexicon words and hard constraints during decoding. |
| Approach: | They propose to use placeholder tags to replace lexicon words with target translations . they use a data augmentation method to make code-switched training data . |
| Outcome: | The proposed method improves translation quality without hurting unconstrained words. |
DocFusion: A Unified Framework for Document Parsing Tasks (2025.findings-acl)
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Mingxu Chai, Ziyu Shen, Chong Zhang, Yue Zhang, Xiao Wang, Shihan Dou, Jihua Kang, Jiazheng Zhang, Qi Zhang
| Challenge: | Existing methods for document parsing often employ multiple models, limiting performance . Existing models often employ discrete tokens, whereas recognition relies on continuous coordinates . |
| Approach: | They propose a Gaussian-Kernel Cross-Entropy Loss (GK-CEL) that unifies detection and recognition by enabling generative frameworks to handle both tasks simultaneously. |
| Outcome: | The proposed model performs competitively across four core document parsing tasks. |
Chinese NER Using Lattice LSTM (P18-1)
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| Challenge: | Chinese named entity recognition (NER) is a fundamental task in information extraction. |
| Approach: | They propose a lattice-structured LSTM model for Chinese named entity recognition (NER) model leverages word and word sequence information to encode a sequence of input characters and all potential words that match a lexicon. |
| Outcome: | The proposed model outperforms word-based and character-based models on Chinese NER datasets. |
RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation (2023.emnlp-main)
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Fengji Zhang, Bei Chen, Yue Zhang, Jacky Keung, Jin Liu, Daoguang Zan, Yi Mao, Jian-Guang Lou, Weizhu Chen
| Challenge: | RepoCoder is a repository-level code completion framework that utilizes the useful information scattered in files. |
| Approach: | They propose a repository-level code completion framework called RepoCoder . it integrates a similarity-based retriever and a pre-trained code language model . they propose 'repoBench' benchmark to validate the framework's effectiveness . |
| Outcome: | The proposed framework outperforms the vanilla retrieval-augmented code completion approach in the real-world. |
Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning (2024.acl-long)
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Yue Yu, Jiaming Shen, Tianqi Liu, Zhen Qin, Jing Nathan Yan, Jialu Liu, Chao Zhang, Michael Bendersky
| Challenge: | Recent advances in natural language processing (NLP) have witnessed the remarkable capabilities of Large Language Models (LLMs). |
| Approach: | They propose an Explanation-Aware Soft Ensemble framework to empower in-context learning with Large language models. |
| Outcome: | The proposed framework can be used to enhance in-context learning on seven natural language understanding tasks and four varying-size LLMs. |
AdvancedIF: Rubric-Based Benchmarking and Reinforcement Learning for Advancing LLM Instruction Following (2026.acl-long)
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Yun He, Wenzhe Li, Hejia Zhang, Songlin Li, Karishma Mandyam, Sopan Khosla, Yuanhao Xiong, Nanshu Wang, Xiaoliang Peng, Beibin Li, Shengjie Bi, Shishir G Patil, Qi Qi, Shengyu Feng, Julian Katz-Samuels, Richard Yuanzhe Pang, Sujan Kumar Gonugondla, Hunter Lang, Yue Yu, Yundi Qian, Maryam Fazel-Zarandi, Licheng Yu, Amine Benhalloum, Hany Hassan Awadalla, Manaal Faruqui
| Challenge: | Recent advances in large language models (LLMs) have shown impressive performance on a range of tasks, yet advanced instruction following (IF) remains a significant challenge. |
| Approach: | They propose a benchmark that features over 1,600 prompts and expert-curated rubrics that assess LLMs’ ability to follow complex, multi-turn, and system-level instructions. |
| Outcome: | The proposed framework improves instruction-following abilities of large language models, achieving a 6.7% gain on AdvancedIF and strong results on public benchmarks. |
UNIVID: Unified Vision-Language Model for Video Moderation (2026.acl-industry)
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Kejuan Yang, Yizhuo Zhang, Mingyuan Du, Yue Zhang, Dixin Zheng, Kaili Zhao, Yang Xiao, Hanzhong Liang, Kenan Xiao
| Challenge: | Existing video moderation systems rely on fragmented black-box classification models that are difficult to maintain and lack transparency. |
| Approach: | They propose a Unified Vision-Language model for Video Moderation that generates policy-aware captions that serve as an interpretable intermediate representation. |
| Outcome: | The proposed model reduces violation leakage and overkill rate by 42.7% while reducing maintenance costs. |
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. |
Making the Best Use of Review Summary for Sentiment Analysis (2020.coling-main)
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| Challenge: | Existing methods for sentiment analysis of user reviews are limited to a few examples. |
| Approach: | They propose a hierarchically-refined attention model that exploits the sentimental distribution of a review and its corresponding summary. |
| Outcome: | The proposed model can make better use of user-written summaries for review sentiment analysis and is more effective compared to existing methods when the user summary is replaced with summary generated by an automatic summarization system. |
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. |
A Graph Enhanced BERT Model for Event Prediction (2022.findings-acl)
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| Challenge: | Existing methods to predict subsequent events use sparsity of event graph to improve performance. |
| Approach: | They propose to automatically build event graph using a BERT model by adding a structured variable to the model to learn to predict event connections. |
| Outcome: | The proposed model outperforms state-of-the-art models on two event prediction tasks. |
Unified Hallucination Detection for Multimodal Large Language Models (2024.acl-long)
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Xiang Chen, Chenxi Wang, Yida Xue, Ningyu Zhang, Xiaoyan Yang, Qiang Li, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen
| Challenge: | despite significant strides in multimodal tasks, MLLMs are plagued by the critical issue of hallucination. |
| Approach: | They propose a meta-evaluation benchmark to facilitate evaluation of advancements in hallucination detection methods. |
| Outcome: | The proposed framework validates hallucinations robustly and provides strategic insights . MHaluBench is a meta-evaluation benchmark designed to facilitate evaluation . |
Natural Language Processing Meets Quantum Physics: A Survey and Categorization (2021.emnlp-main)
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| Challenge: | Recent research has focused on quantum-inspired algorithms for NLP and quantum-based algorithms for cognition. |
| Approach: | They propose to categorize quantum-inspired algorithms according to quantum theory, linguistic targets that are modeled, and the downstream application. |
| Outcome: | The proposed methods are categorized according to the use of quantum theory, the linguistic targets that are modeled, and the downstream application. |
Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction (2022.coling-1)
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Yidong Wang, Hao Wu, Ao Liu, Wenxin Hou, Zhen Wu, Jindong Wang, Takahiro Shinozaki, Manabu Okumura, Yue Zhang
| Challenge: | Existing methods to extract opinion words from sentences are limited due to the expensive annotation process. |
| Approach: | They propose to exploit massive unlabeled data to reduce distribution shift risk . they propose to use two filters specifically for TOWE to filter noisy data . results indicate superiority of MGCR over current state-of-the-art methods . |
| Outcome: | The proposed method reduces the risk of distribution shifts by increasing the exposure of the model to varying distribution shift. |
AiraXiv: An AI-Driven Open-Access Platform for Human and AI Scientists (2026.acl-demo)
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| Challenge: | Recent advances in artificial intelligence (AI) have accelerated the growth of both human-authored and AI-generated research outputs. |
| Approach: | They propose an AI-driven open-access platform built on open preprints, AI-augmented analysis and review, and reader feedback. |
| Outcome: | The proposed platform supports human scientists through an interactive UI and AI scientists through Model Context Protocol (MCP)-based interactions. |
Target-Side Augmentation for Document-Level Machine Translation (2023.acl-long)
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| Challenge: | Document-level machine translation faces the challenge of data sparsity due to its long input length and a small amount of training data. |
| Approach: | They propose a document-level machine translation model that generates many potential translations for each source document and smoothes the distribution. |
| Outcome: | The proposed method outperforms the previous best system by 2.30 s-BLEU on News and achieves new state-of-the-art on News . |
ProMed: Shapley Information Gain Guided Reinforcement Learning for Proactive Medical LLMs (2026.acl-long)
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Hongxin Ding, Baixiang Huang, Yue Fang, Weibin Liao, Xinke Jiang, Jinyang Zhang, Yinghao Zhu, Zheng Li, Liantao Ma, Junfeng Zhao, Yasha Wang
| Challenge: | Existing medical Large Language Models (LLMs) follow a reactive paradigm, risking diagnostic errors by answering before seeking sufficient details. |
| Approach: | They propose a reinforcement learning framework that transitions LLMs toward a proactive paradigm, enabling them to ask clinically valuable questions before decision-making. |
| Outcome: | Experiments on partial-information medical benchmarks show that ProMed outperforms state-of-the-art methods by 6.29% on average and delivers a 54.45% gain over the reactive paradigm. |
Improving Cross-Domain Chinese Word Segmentation with Word Embeddings (N19-1)
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| Challenge: | Existing approaches to Chinese word segmentation (CWS) are character-based and word-based . character-driven approaches use conditional random field models to label sequences, with complex hand-crafted discrete features. |
| Approach: | They propose a semi-supervised word-based approach to improve cross-domain Chinese word segmentation given a baseline segmenter. |
| Outcome: | The proposed model outperforms state-of-the-art approaches on five datasets covering domains in novels, medicine, and patent. |
VoiceBench: Benchmarking LLM-Based Voice Assistants (2026.tacl-1)
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| Challenge: | Recent advances in large language models (LLMs) have enabled real-time speech interactions through LLMs. |
| Approach: | They propose a benchmark specifically designed to assess LLM-based voice assistants. |
| Outcome: | The proposed benchmark measures the performance of LLM-based voice assistants across eight tasks. |
DeepInsert: Early Layer Bypass for Efficient and Performant Multimodal Understanding (2026.eacl-long)
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Moulik Choraria, Xinbo Wu, Akhil Bhimaraju, Nitesh Sekhar, Yue Wu, Xu Zhang, Prateek Singhal, Lav R. Varshney
| Challenge: | Recent work shows that hyperscaling of data and parameter count in LLMs is yielding diminishing improvement when weighed against training costs. |
| Approach: | They propose to insert multimodal tokens directly into the middle of the model to bypass the early layers. |
| Outcome: | The proposed method reduces training and inference costs while preserving performance. |
SafeScientist: Enhancing AI Scientist Safety for Risk-Aware Scientific Discovery (2025.emnlp-main)
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Kunlun Zhu, Jiaxun Zhang, Ziheng Qi, Nuoxing Shang, Zijia Liu, Peixuan Han, Yue Su, Haofei Yu, Jiaxuan You
| Challenge: | Recent advances in large language model (LLM) agents have significantly accelerated scientific discovery automation, yet raised critical ethical and safety concerns. |
| Approach: | They propose a framework to enhance safety and ethical responsibility in AI-driven scientific exploration. |
| Outcome: | The proposed framework significantly improves safety performance by 35% compared to traditional frameworks. |
From spoken dialogue to formal summary: An utterance rewriting for dialogue summarization (2022.naacl-main)
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| Challenge: | Existing models focus more on the structure of summary, not on the personal and logical inconsistency problem. |
| Approach: | They propose a model to solve the problem of personal and logical inconsistency . they use an utterance rewriter to complete the ellipsis content of dialogue content . |
| Outcome: | The proposed model outperforms baseline models on both SAMSum and DialSum datasets. |
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)
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Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang
| Challenge: | Existing approaches to textual robustness evaluation focus on slightly modifying the input data, which maintains the original meaning and results in a different prediction. |
| Approach: | They propose a multilingual robustness evaluation toolkit for NLP that integrates universal text transformations, task-specific transformations and adversarial attack. |
| Outcome: | The toolkit includes universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses. |
Learning Interpretable Relationships between Entities, Relations and Concepts via Bayesian Structure Learning on Open Domain Facts (2020.acl-main)
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| Challenge: | Concept graphs are created as universal taxonomies for text understanding in the open domain knowledge. |
| Approach: | They propose to learn interpretable relationships from open-domain facts to enrich concept graphs. |
| Outcome: | The proposed method improves the identification of concepts for entities based on relations between entities on public English and Chinese datasets. |
Prompt-Driven Neural Machine Translation (2022.findings-acl)
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| Challenge: | Neural machine translation models still face various challenges including fragility and lack of style flexibility. |
| Approach: | They propose to incorporate prompts into neural machine translation to improve translation control and style flexibility. |
| Outcome: | Empirical results show that the proposed method improves translation control and quality and improves human-in-the-loop translation. |
KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents (2025.findings-naacl)
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Yuqi Zhu, Shuofei Qiao, Yixin Ou, Shumin Deng, Shiwei Lyu, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen, Ningyu Zhang
| Challenge: | Large Language Models (LLMs) fail to effectively guide the planning trajectories during task solving and result in planning hallucinations. |
| Approach: | They propose a novel approach to enhance the planning capabilities of large language models by incorporating explicit action knowledge. |
| Outcome: | The proposed approach can achieve comparable or superior performance to existing baselines on HotpotQA and ALFWorld. |
KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models (2024.acl-long)
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Zhuohao Yu, Chang Gao, Wenjin Yao, Yidong Wang, Wei Ye, Jindong Wang, Xing Xie, Yue Zhang, Shikun Zhang
| Challenge: | Existing methods to detect contaminated texts focus on quantifying contamination status instead of accurately gauging model performance. |
| Approach: | They propose a Knowledge-grounded Interactive Evaluation framework which incorporates an LLM-powered “interactor” role for the first time to accomplish a dynamic contamination-resilient evaluation. |
| Outcome: | The proposed framework is based on a question in a standard LLM benchmark and can be used to evaluate models in real-world conversations. |
Explicit Object Relation Alignment for Vision and Language Navigation (2022.acl-srw)
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| Challenge: | Existing work on vision and language navigation grounding the landmarks and spatial relations in textual instructions into visual modality is important. |
| Approach: | They propose a neural agent to explicitly align the spatial information in both instruction and visual environment, including landmarks and spatial relationships between the agent and landmarks. |
| Outcome: | The proposed method surpasses the baseline on the R2R dataset and shows that it can explain spatial reasoning and spatial relationships. |
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. |
NavHint: Vision and Language Navigation Agent with a Hint Generator (2024.findings-eacl)
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| Challenge: | Existing work on vision and language navigation relies on navigation-related losses to establish the connection between vision and modalities, neglecting aspects of helping the navigation agent build a deep understanding of the visual environment. |
| Approach: | They propose to provide indirect supervision to the navigation agent through a hint generator that generates visual descriptions during navigation. |
| Outcome: | The proposed method improves the navigation performance and interpretability of the R2R and R4R datasets. |
Subword Encoding in Lattice LSTM for Chinese Word Segmentation (N19-1)
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| Challenge: | Using subwords, Chinese word segmentation models use character combination information to disambiguate characters. |
| Approach: | They propose a subword-based neural word segmentor that integrates character embeddings into a Lattice LSTM network over a character sequence. |
| Outcome: | The proposed model can utilize abundant character combination information, which is effective to disambiguate characters. |
FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models (2024.emnlp-demo)
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Zhuohao Yu, Chang Gao, Wenjin Yao, Yidong Wang, Zhengran Zeng, Wei Ye, Jindong Wang, Yue Zhang, Shikun Zhang
| Challenge: | Large language models (LLMs) have revolutionized natural language processing with impressive performance across various tasks. |
| Approach: | They propose a framework for automated evaluations of large language models . they open-source their code at https://github.com/WisdomShell/FreeEval . |
| Outcome: | The framework is open-source and can be used to develop and validate new evaluation methods. |
ELLA: Efficient Lifelong Learning for Adapters in Large Language Models (2026.eacl-long)
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| Challenge: | Existing approaches to training Large Language Models (LLMs) suffer from catastrophic forgetting when adapted sequentially to new tasks in a continual learning (CL) setting. Existing methods are impractical and could potentially violate privacy. |
| Approach: | They propose a training framework built on the principle of selective subspace de-correlation that characterizes the structure of past updates and penalizes alignments along their high-energy, task-specific directions. |
| Outcome: | The proposed training framework achieves state-of-the-art CL performance on three popular benchmarks spanning both classification and generative tasks with relative accuracy gains of up to 9.6% and a 35 smaller memory footprint. |
OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement (2024.findings-acl)
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| Challenge: | OpenCodeInterpreter-33B provides a high level of performance for code generation, executing, and iterative refinement. |
| Approach: | They propose a family of open-source code systems for generating, executing, and iteratively refining code. |
| Outcome: | The OpenCodeInterpreter-33B performs well on humanEval, MBPP, and EvalPlus benchmarks. |
N-ary Relation Extraction using Graph-State LSTM (D18-1)
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| Challenge: | Existing methods for cross-sentence relation extraction split the input graph into two DAGs, but important information can be lost in the splitting procedure. |
| Approach: | They propose a graph-state LSTM model which uses a parallel state to model each word, recurrently enriching state values via message passing. |
| Outcome: | The proposed model keeps the original graph structure, and speeds up computation by allowing more parallelization. |
Measuring Consistency in Text-based Financial Forecasting Models (2023.acl-long)
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| Challenge: | Recent advances in natural language processing (NLP) have allowed financial forecasting to gain significant accuracy and reliability. |
| Approach: | They propose a tool that assesses logical consistency in financial text and compares it with other models to assess their performance. |
| Outcome: | The proposed evaluation tool assesses logical consistency in financial text. |
Semformer: Transformer Language Models with Semantic Planning (2024.emnlp-main)
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| Challenge: | Neural language models (LLMs) employ teacher forcing to predict tokens based on preceding ground truth tokens. |
| Approach: | They propose a method for training a Transformer language model that explicitly models the semantic planning of response. |
| Outcome: | The proposed method exhibits near-perfect performance and mitigates shortcut learning. |
DynaCode: A Dynamic Complexity-Aware Code Benchmark for Evaluating Large Language Models in Code Generation (2025.findings-acl)
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| Challenge: | Existing code benchmarks for large language models remain static, resulting in data contamination and unreliable evaluation results. |
| Approach: | They propose a dynamic, complexity-aware benchmark that overcomes the limitations of static datasets and provides a memorization-advantaged benchmark. |
| Outcome: | DynaCode generates 189 million unique nested code problems across 4 units of code complexity and 16 types of call graphs. |
VLN-Trans: Translator for the Vision and Language Navigation Agent (2023.acl-long)
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| Challenge: | We observe two kinds of instructions that make the grounding in the vision-and-language navigation task quite challenging. |
| Approach: | They propose to use a translator module to convert instructions into easy-to-follow sub-instruction representations at each step. |
| Outcome: | The proposed model is based on a Room2Room (R2R), Room4room (R4R), and Room2room Last (R1R-Last) datasets and achieves state-of-the-art results on multiple benchmarks. |
Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation (2021.emnlp-main)
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| Challenge: | Recent work on multilingual AMR-to-text generation has focused on data augmentation strategies that utilize generated silver AMRs, but this assumes a high quality of generated AMR. |
| Approach: | They propose to combine gold AMR with silver AMRs to generate multilingual AMR annotations. |
| Outcome: | The proposed models outperform the current state of the art for German, Italian, Spanish, and Chinese by a large margin. |
Paraphrase and Solve: Exploring and Exploiting the Impact of Surface Form on Mathematical Reasoning in Large Language Models (2024.naacl-long)
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| Challenge: | Despite the impressive performance of large-scale language models, their ability to reason through complex problems remains a bottleneck. |
| Approach: | They propose a method which diversifies reasoning paths from specific surface forms of the problem to improve mathematical reasoning performance. |
| Outcome: | The proposed approach improves mathematical reasoning performance over vanilla self-consistency, especially for problems initially deemed unsolvable. |
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. |
Cross-Domain NER using Cross-Domain Language Modeling (P19-1)
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| Challenge: | Existing methods for named entity recognition (NER) use labeled data for both source and target domains. |
| Approach: | They propose to use language modeling as a bridge between NER domains to perform cross-domain and cross-task knowledge transfer. |
| Outcome: | The proposed method extracts domain differences from cross-domain LM contrast, allowing unsupervised domain adaptation while giving state-of-the-art results among supervised domain adapters. |
Syntax-Enhanced Self-Attention-Based Semantic Role Labeling (D19-1)
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| Challenge: | Abstract: Syntax is the bridge to semantics, but recent studies have discussed the necessity of syntax in the context of SRL. |
| Approach: | They propose a syntax-enhanced self-attention model that incorporates syntactic knowledge into the SRL task effectively. |
| Outcome: | The proposed model achieves state-of-the-art for the Chinese SRL task on the CoNLL-2009 dataset. |
Mobile-R1: Towards Interactive Capability for VLM-Based Mobile Agent via Systematic Training (2026.acl-long)
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Jihao Gu, Qihang Ai, Yingyao Wang, Pi Bu, Jingxuan Xing, Yue Cao, Zekun Zhu, Wei Jiang, Ziming Wang, Yingxiu Zhao, Ming-Liang Zhang, Jun Song, Yuning Jiang, Bo Zheng
| Challenge: | Existing approaches to training agents for visual-language models trap them in local optima, hindering exploration and error correction with the environment. |
| Approach: | They propose a hierarchical training recipe that bridges atomic action execution and strategic task completion. |
| Outcome: | The proposed training recipe bridges atomic action execution and strategic task completion. |
Nash CoT: Multi-Path Inference with Preference Equilibrium (2024.emnlp-main)
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| Challenge: | Multi-path inference is an improvement on multi-path reasoning, but there is no optimal setting for the number of inference paths. |
| Approach: | They propose to use question-related role templates to guide LLMs into relevant roles to reduce the dependence on the number of inference paths. |
| Outcome: | The proposed system can achieve comparable or better results than self-consistency with the same number of paths. |