UNIFIED FRAMEWORK

Enhancing Entity Boundary Detection for Better Chinese Named Entity Recognition (2021.acl-short)

Challenge:	Pre-Trained Models (PTMs) have reshaped the development of natural language processing (NLP) but it is not easy to obtain high-performing PTMs without a large amount of labeled training data and deploy them online with fast inference speed.
Approach:	They propose to make it easy to build NLP applications with knowledge-enhanced pre-training and knowledge distillation.
Outcome:	EasyNLP supports a comprehensive suite of NLP algorithms and features knowledge-enhanced pre-training, knowledge distillation and few-shot learning functionalities.

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Chun Chen, Fang Kong

Generating Text from Language Models (2023.acl-tutorials)

Challenge:	Existing approaches to Chinese Named Entity Recognition (NER) lack explicit word boundary and tenses information.
Approach:	They propose a boundary enhanced approach for Chinese Named Entity Recognition . they add an additional Graph Attention Network(GAT) layer to capture internal dependency of phrases .
Outcome:	The proposed approach improves Chinese Named Entity Recognition (NER) on OntoNotes and Weibo corpora.

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Afra Amini, Ryan Cotterell, John Hewitt, Luca Malagutti, Clara Meister, Tiago Pimentel

dLLM: Simple Diffusion Language Modeling (2026.acl-demo)

Challenge:	a growing percentage of natural language processing tasks focus on the generation of text from probabilistic language models.
Approach:	They will provide a centralized discussion of critical considerations when choosing how to generate from a language model.
Outcome:	This tutorial will provide a centralized discussion of critical considerations when choosing how to generate from a language model.

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Zhanhui Zhou, Lingjie Chen, Hanghang Tong, Dawn Song

Fast-MIA: Efficient and Scalable Membership Inference for LLMs (2026.acl-demo)

Challenge:	diffusion language models (DLMs) are evolving rapidly but many lack transparent implementations or are scattered across codebases.
Approach:	They propose an open-source framework that unifies diffusion language modeling components while remaining flexible enough to support new methods and architectures.
Outcome:	dLLM unifies the core components of diffusion language modeling and makes them easy to customize for new designs.

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Hiromu Takahashi, Shotaro Ishihara

DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population (2022.emnlp-demos)

Challenge:	a library for evaluating membership inference attacks against large language models (LLMs) has emerged as a crucial technique for auditing privacy risks and copyright infringement in LLMs.
Approach:	They propose a Python library for efficiently evaluating membership inference attacks against large language models (LLMs) they use a high-throughput batch inference via vLLM and a cross-method caching architecture that computes intermediate results once and shares them across methods.
Outcome:	The proposed library performs a 5 speedup in inference and a cross-method caching architecture that computes intermediate results once and shares them across methods.

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Ningyu Zhang, Xin Xu, Liankuan Tao, Haiyang Yu, Hongbin Ye, Shuofei Qiao, Xin Xie, Xiang Chen, Zhoubo Li, Lei Li

GLiNER2: Schema-Driven Multi-Task Learning for Structured Information Extraction (2025.emnlp-demos)

Challenge:	Existing knowledge extraction tools are not complete due to emerging entities and relations in real-world applications.
Approach:	They propose an open-source knowledge extraction toolkit DeepKE that supports low-resource, document-level and multimodal scenarios in the knowledge base population.
Outcome:	The proposed toolkit supports low-resource, document-level and multimodal scenarios in the knowledge base population.

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Urchade Zaratiana, Gil Pasternak, Oliver Boyd, George Hurn-Maloney, Ash Lewis

Towards a Human-Computer Collaborative Scientific Paper Lifecycle: A Pilot Study and Hands-On Tutorial (2024.lrec-tutorials)

Challenge:	Existing solutions for information extraction (IE) require specialized models for different tasks or require expensive large language models.
Approach:	They propose a framework that enhances the original GLiNER architecture to support named entity recognition, text classification, and hierarchical structured data extraction within a single efficient model.
Outcome:	The proposed framework improves performance across diverse IE tasks and accessibility compared to LLM-based alternatives.

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Qingyun Wang, Carl Edwards, Heng Ji, Tom Hope

Unified Examination of Entity Linking in Absence of Candidate Sets (2024.naacl-short)

Challenge:	a tutorial aims to provide an overview of the scientific paper lifecycle . large language models (LLMs) have increasingly played an important role in academic writing .
Approach:	They propose to provide an overview of the scientific paper lifecycle using large language models.
Outcome:	The tutorial will provide an overview of the scientific paper lifecycle, including scientific literature understanding, experiment development, manuscript draft writing, and finally draft evaluation.

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Nicolas Ong, Hassan Shavarani, Anoop Sarkar

ChatEval: A Tool for Chatbot Evaluation (N19-4)

Challenge:	Entity linking systems depend on candidate sets for their performance, but a comprehensive comparative analysis of these systems is lacking.
Approach:	They propose a black-box benchmark and a method to evaluate all state-of-the-art entity linking methods.
Outcome:	The proposed approach reduces the inference time and memory footprint of some models.

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João Sedoc, Daphne Ippolito, Arun Kirubarajan, Jai Thirani, Lyle Ungar, Chris Callison-Burch

A Browser-based Open Source Assistant for Multimodal Content Verification (2026.eacl-demo)

Challenge:	open-domain dialog systems are difficult to evaluate due to lack of standardization and standardization in evaluation procedures.
Approach:	They propose a framework for human evaluation of chatbots that augments existing tools . researchers can submit their trained models to the ChatEval web interface . reproducibility and model assessment for opendomain dialog systems is challenging .
Outcome:	The proposed framework provides a web-based hub for researchers to compare their models with baselines and prior work.

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Rosanna Milner, Michael Foster, Twin Karmakharm, Olesya Razuvayevskaya, Valentin Porcellini, Denis Teyssou, Ian Roberts, Kalina Bontcheva

Towards Unification of Discourse Annotation Frameworks (2022.acl-srw)

Challenge:	Disinformation and advanced generative AI content pose a significant challenge for journalists and fact-checkers who must rapidly verify digital media.
Approach:	They propose to integrate a browser-based tool that automatically extracts content from a suite of backend NLP classifiers and presents actionable credibility signals and AI-generation likelihood in an easy-to-digest format.
Outcome:	The Verification Assistant is a browser-based tool that extracts content and routes it to a suite of backend NLP classifiers, presenting actionable credibility signals, AI-generation likelihood, and other verification advice in an easy-to-digest format.

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Yingxue Fu

Challenge:	Discourse information is difficult to represent and annotate, and corpora annotated under different frameworks vary considerably.
Approach:	They propose to use automatic means to unify discourse structures and relations . they will also explore the application of the unified framework in multi-task learning and graphical models .
Outcome:	The proposed method can be used in multi-task learning and graphical models.

FAKTA: An Automatic End-to-End Fact Checking System (N19-4)

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Moin Nadeem, Wei Fang, Brian Xu, Mitra Mohtarami, James Glass

Ethical NLP

Challenge:	Existing studies have investigated individual components of fact checking process but none offer such a capability.
Approach:	They propose a framework that integrates various components of a fact-checking process.
Outcome:	The proposed framework integrates various components of a fact-checking process to predict the factuality of claims and provide evidence at the document and sentence level to explain its predictions.

PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models (2026.eacl-demo)

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Robert Belanec, Ivan Srba, Maria Bielikova

A Unified RvNN Framework for End-to-End Chinese Discourse Parsing (C18-2)

Challenge:	Parameter-Efficient Fine-Tuning (PEFT) methods address the increasing size of Large Language Models (LLMs).
Approach:	They propose a framework for efficient fine-tuning Large Language Models (LLMs) they aim to train only a small percentage of the full model's parameters .
Outcome:	Xu et al., 2023; Ding e t al, 2024; Lialin e al. 2023) show that using PEFT methods can improve performance.

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Lin Chuan-An, Hen-Hsen Huang, Zi-Yuan Chen, Hsin-Hsi Chen

AutoAlign: Get Your LLM Aligned with Minimal Annotations (2025.acl-demo)

Challenge:	Existing work on Chinese discourse parser relies on external packages to extract linguistic features from free text.
Approach:	They propose an end-to-end Chinese discourse parser based on recursive neural network to jointly model the subtasks including elementary discourse unit segmentation, tree structure construction, center labeling, and sense labeling.
Outcome:	The proposed framework achieves state-of-the-art in the Chinese Discourse Treebank dataset.

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Xinyu Lu, Dong Xu, Chunkang Zhang, Xinyan Guan, Junxiang Wang, Qingyu Zhang, Pengbo Wang, Yingzhi Mao, Hao Xiang, Xueru Wen, Zichao Li, Yaojie Lu, Hongyu Lin, Le Sun, Xianpei Han

Named Entity Recognition in Multi-level Contexts (2020.aacl-main)

Challenge:	Automated Alignment (ALM) is a set of algorithms designed to align Large Language Models (LLMs) with human intentions and values while minimizing manual intervention.
Approach:	They propose an open-source toolkit that integrates mainstream automated algorithms through a consistent interface and an accessible workflow supporting one-click execution for prompt synthesis and automatic alignment signal construction.
Outcome:	The proposed framework enables easy reproduction of existing results through extensive benchmarks and facilitates the development of novel approaches via modular components.

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Yubo Chen, Chuhan Wu, Tao Qi, Zhigang Yuan, Yongfeng Huang

OpenFactCheck: A Unified Framework for Factuality Evaluation of LLMs (2024.emnlp-demo)

Challenge:	Existing methods for named entity recognition are unsatisfactory for recognizing entities in limited or ambiguous sentence-level contexts.
Approach:	They propose a framework to incorporate multi-level contexts for named entity recognition using TagLM as a baseline model and an auxiliary task to mine word-level contextual information.
Outcome:	The proposed framework is based on a set of sentence-level contexts and a document-level task to mine word-level contextual information.

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Hasan Iqbal, Yuxia Wang, Minghan Wang, Georgi Georgiev, Jiahui Geng, Iryna Gurevych, Preslav Nakov

OpenKE: An Open Toolkit for Knowledge Embedding (D18-2)

Challenge:	Large language models (LLMs) often produce content that deviates from real-world facts.
Approach:	They developed a unified framework to assess the factuality of large language models . open-sourced framework is publicly available as a Python library and web service .
Outcome:	OpenFactCheck is open-sourced and publicly released as a Python library and web service.

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Xu Han, Shulin Cao, Xin Lv, Yankai Lin, Zhiyuan Liu, Maosong Sun, Juanzi Li

Simple Question Answering with Subgraph Ranking and Joint-Scoring (N19-1)

Challenge:	Existing knowledge embedding tools are available for embeddable knowledge graphs.
Approach:	They propose a unified framework and various fundamental models to embed knowledge graphs into a continuous low-dimensional space.
Outcome:	The toolkit and pre-trained embeddings are available on http://openke.thunlp.org/.

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Wenbo Zhao, Tagyoung Chung, Anuj Goyal, Angeliki Metallinou

OpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction (D19-3)

Challenge:	Knowledge graph based simple question answering is a major area of research in question answering.
Approach:	They propose a framework to describe and analyze existing knowledge graph based simple question answering approaches.
Outcome:	The proposed model achieves a state-of-the-art (85.44% accuracy) on the SimpleQuestions dataset.

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Xu Han, Tianyu Gao, Yuan Yao, Deming Ye, Zhiyuan Liu, Maosong Sun

AugCSE: Contrastive Sentence Embedding with Diverse Augmentations (2022.aacl-main)

Challenge:	OpenNRE provides a framework to implement neural relation extraction (RE) . the toolkit provides various functional modules based on TensorFlow and PyTorch .
Approach:	OpenNRE is an open-source framework to implement neural relation extraction models. they also release an online system to meet real-time extraction without any training and deployment.
Outcome:	OpenNRE provides a framework to implement neural models for relation extraction (RE) the toolkit also includes an online system to meet real-time extraction without training and deployment .

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Zilu Tang, Muhammed Yusuf Kocyigit, Derry Tanti Wijaya

A Systematic Study of Cross-Layer KV Sharing for Efficient LLM Inference (2025.naacl-short)

Challenge:	Similar work has shown that a single augmentation can be used to learn a robust generalpurpose representation with contrastive learning.
Approach:	They propose a unified framework to utilize diverse sets of data augmentations to achieve a better, general-purpose sentence embedding model.
Outcome:	The proposed framework achieves state-of-the-art results on downstream transfer tasks and performs competitively on semantic textual similarity tasks, using only unsupervised data.

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You Wu, Haoyi Wu, Kewei Tu

RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models (2022.aacl-main)

Challenge:	Recent studies have shown that sharing key-value (KV) cache across layers is effective in efficient inference of large language models.
Approach:	They propose a unified framework that covers several recent methods and their novel variants to investigate cross-layer KV sharing.
Outcome:	The proposed framework achieves higher throughput and better performance when reducing the size of the key-value cache by 2 while maintaining competitive performance.

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Lingzhi Wang, Huang Hu, Lei Sha, Can Xu, Daxin Jiang, Kam-Fai Wong

LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models (2024.acl-demos)

Challenge:	Existing generative methods to recommend items are shallowly integrated into the model training and have poor chit-chat ability.
Approach:	They propose a framework that integrates recommendation into the dialog generation by introducing a vocabulary pointer.
Outcome:	The proposed framework outperforms the state-of-the-art models on a benchmark dataset.

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Yaowei Zheng, Richong Zhang, Junhao Zhang, YeYanhan YeYanhan, Zheyan Luo

XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser (2025.coling-main)

Challenge:	Efficient fine-tuning of large language models requires non-trivial efforts to implement these methods on different models.
Approach:	They propose a framework that democratizes the fine-tuning of large language models by integrating a suite of efficient training methods into one framework.
Outcome:	The proposed framework is able to scale to 100+ LLMs without coding and receives over 25,000 stars and 3,000 forks.

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Xianfu Cheng, Hang Zhang, Jian Yang, Xiang Li, Weixiao Zhou, Fei Liu, Kui Wu, Xiangyuan Guan, Tao Sun, Xianjie Wu, Tongliang Li, Zhoujun Li

entity-linkings: A Unified Library for Entity Linking (2026.eacl-demo)

Challenge:	Document AI parsing semi-structured image form is a key information extraction task.
Approach:	They propose a multimodal and multilingual semi-structured FORM PARSER which integrates SER and relation extraction into a unified framework.
Outcome:	The proposed framework achieves up to 1.79% improvement on RE tasks in multilingual and zero-shot settings.

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Yuya Sawada, Tsuyoshi Fujita, Yusuke Sakai, Taro Watanabe

A Unified Encoder-Decoder Framework with Entity Memory (2022.emnlp-main)

Challenge:	Entity linking (EL) is the task of mapping named entities in text to canonical entries in a knowledge base.
Approach:	They propose a unified library for using and developing entity linking systems . a strong emphasis is placed on usability, making it highly extensible .
Outcome:	a new library aims to disambiguate named entities in text by mapping them to canonical entries in a knowledge base.

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Zhihan Zhang, Wenhao Yu, Chenguang Zhu, Meng Jiang

PaperMage: A Unified Toolkit for Processing, Representing, and Manipulating Visually-Rich Scientific Documents (2023.emnlp-demo)

Challenge:	Existing approaches to index, retrieve, and read documents as evidence suffer from large computational overheads.
Approach:	They propose an encoder-decoder framework with an entity memory that stores entity knowledge as latent representations and pre-trained on Wikipedia along with encoder parameters.
Outcome:	The proposed framework outperforms memory-based and non-memory encoder-decoder models on various entity-intensive question answering and generation tasks.

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Kyle Lo, Zejiang Shen, Benjamin Newman, Joseph Chang, Russell Authur, Erin Bransom, Stefan Candra, Yoganand Chandrasekhar, Regan Huff, Bailey Kuehl, Amanpreet Singh, Chris Wilhelm, Angele Zamarron, Marti A. Hearst, Daniel Weld, Doug Downey, Luca Soldaini

EvoAgentX: An Automated Framework for Evolving Agentic Workflows (2025.emnlp-demos)

Challenge:	Existing tools for working with scientific documents are limited and documents are often in difficult-to-use PDF formats.
Approach:	They propose an open-source Python toolkit for analyzing and processing visually-rich scientific documents.
Outcome:	PaperMage provides turn-key recipes for common scientific document processing use-cases.

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Yingxu Wang, Siwei Liu, Jinyuan Fang, Zaiqiao Meng

Numerical Reasoning

Challenge:	Existing MAS frameworks often require manual workflow configuration and lack native support for dynamic evolution and performance optimization.
Approach:	They propose an open-source platform that automates generation, execution, and evolutionary optimization of multi-agent workflows.
Outcome:	The proposed platform automates generation, execution, and evolutionary optimization of multi-agent workflows.

CiteLab: Developing and Diagnosing LLM Citation Generation Workflows via the Human-LLM Interaction (2025.acl-demo)

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Jiajun Shen, Tong Zhou, Yubo Chen, Kang Liu, Jun Zhao

Challenge:	Existing frameworks for enabling Large Language Models to generate citations are lacking . however, they can still produce hallucinated responses that are non-factual or irrelevant to the input.
Approach:	They propose an open-source and modular framework for enabling LLMs to generate citations in Question-Answering tasks.
Outcome:	The proposed framework is extensible and paired with a visual interface, Citefix, facilitating case study and modification of existing citation generation methods.

UniFine: A Unified and Fine-grained Approach for Zero-shot Vision-Language Understanding (2023.findings-acl)

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Rui Sun, Zhecan Wang, Haoxuan You, Noel Codella, Kai-Wei Chang, Shih-Fu Chang

Gender Bias in Machine Translation (2021.tacl-1)

Challenge:	supervised methods for vision-language tasks have been well-studied, but they lack the fine-grained information needed for semantics understanding.
Approach:	They propose a framework to take advantage of fine-grained information for zero-shot vision-language learning, covering multiple tasks such as VQA, SNLI-VE, and VCR.
Outcome:	The proposed framework outperforms previous zero-shot methods on VQA and achieves substantial improvement on SNLI-VE and VCR.

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Beatrice Savoldi, Marco Gaido, Luisa Bentivogli, Matteo Negri, Marco Turchi

UltraEval-Audio: A Unified Framework for Comprehensive Evaluation of Audio Foundation Models (2026.acl-demo)

Challenge:	Interest in understanding, assessing, and mitigating gender bias in machine translation (MT) still lacks cohesion.
Approach:	They propose to review current conceptualizations of gender bias in machine translation (MT) they summarize previous studies and propose ways to mitigate bias.
Outcome:	This paper summarizes the current conceptualizations and proposes strategies to mitigate biases in machine translation (MT) .

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Qundong Shi, Jie Zhou, Biyuan Lin, Junbo Cui, Guoyang Zeng, Yixuan Zhou, Ziyang Wang, Xin Liu, Zhen Luo, Yudong Wang, Zhiyuan Liu

FL-MSCL: A Unified Figurative Language Detection Model Driven by Multi-Type Signals and Contrastive Learning (2026.acl-short)

Challenge:	Existing evaluation frameworks for audio foundation models are heavily reliant on English, making it difficult to objectively assess models’ performance on Chinese.
Approach:	They propose a unified framework that supports 10 languages, 14 task categories, 24 models, and 36 benchmarks with one-command evaluation and real-time leaderboards.
Outcome:	The proposed framework supports 10 languages, 14 task categories, 24 models, and 36 benchmarks with one-command evaluation and real-time leaderboards.

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Lu Shijia, Fumiyo Fukumoto, Huang Xiaoxi, Yoshimi Suzuki

RAMQA: A Unified Framework for Retrieval-Augmented Multi-Modal Question Answering (2025.findings-naacl)

Challenge:	Figurative language recognition challenges distinguishing between fine-grained rhetorical categories . existing approaches are framed as single-category binary classifiers .
Approach:	They propose a framework that integrates prompt-based knowledge injection with supervised contrastive learning to enforce explicit class distinctions.
Outcome:	The proposed framework achieves competitive performance on a four-way sentence-level classification task.

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Yang Bai, Christan Grant, Daisy Zhe Wang

Joint Learning for Emotion Classification and Emotion Cause Detection (D18-1)

Challenge:	Existing ranking methods rely on small encoder-based ranking models, which are incompatible with modern decoder--based generative large language models (LLMs) Existing methods based on small LLaVA rankers are incompatible with advanced LLMs.
Approach:	They propose a framework that combines learning-to-rank methods with generative permutation-enhanced ranking techniques.
Outcome:	The proposed framework improves on two benchmarks, WebQA and MultiModalQA, showing significant improvements over baselines.

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Ying Chen, Wenjun Hou, Xiyao Cheng, Shoushan Li

A Unified Framework and Dataset for Assessing Societal Bias in Vision-Language Models (2024.findings-emnlp)

Challenge:	Using a unified framework, we propose a joint approach for emotion classification and emotion cause detection.
Approach:	They propose a neural network-based joint approach for emotion classification and emotion cause detection which captures mutual benefits across the two sub-tasks.
Outcome:	The proposed approach can capture mutual benefits across two sub-tasks on Chinese microblogs.

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Ashutosh Sathe, Prachi Jain, Sunayana Sitaram

Frowning Frodo, Wincing Leia, and a Seriously Great Friendship: Learning to Classify Emotional Relationships of Fictional Characters (N19-1)

Challenge:	Existing studies have highlighted the existence of social biases within large vision and language models.
Approach:	They propose a framework for systematically evaluating gender, race, and age biases in vision-language models with respect to professions.
Outcome:	The proposed framework covers all supported inference modes of the recent vision-language models, including image-to-text, text-to image, and image- to-image.

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Evgeny Kim, Roman Klinger

Ready to Translate, Not to Represent? Bias and Performance Gaps in Multilingual LLMs Across Language Families and Domains (2026.findings-acl)

Challenge:	Existing literature analysis does not focus on roles of characters or on relationships between them.
Approach:	They propose to combine emotion and character identification into a unified framework for character network extraction from fictional texts.
Outcome:	The proposed task is based on fan-fiction short stories and is able to predict emotion relations in the extracted network graph.

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Md. Faiyaz Abdullah Sayeedi, Subhey Sadi Rahman, Md. Mahbub Alam, Md. Adnanul Islam, Jannatul Ferdous Deepti, Tasnim Mohiuddin, Md Mofijul Islam, Swakkhar Shatabda

ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning (2026.acl-demo)

Challenge:	Large Language Models (LLMs) have redefined Machine Translation, enabling context-aware and fluent translations across hundreds of languages and textual domains.
Approach:	They propose a framework and dataset to evaluate the translation quality and fairness of open-source LLMs.
Outcome:	The proposed framework and dataset evaluates translation quality and fairness of open-source LLMs.

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Vladislav Smirnov, Quang-Chieu Nguyen, Sergey Senichev, Minh Ngoc Ta, Ekaterina Fadeeva, Artem Vazhentsev, Daria Galimzianova, Nikolai Rozanov, Viktor Mazanov, Jingwei Ni, Tianyi Wu, Igor Kiselev, Mrinmaya Sachan, Iryna Gurevych, Preslav Nakov, Timothy Baldwin, Artem Shelmanov

CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmented Generation (2025.findings-naacl)

Challenge:	Existing TTC scaling strategies and reasoning scorers are fragmented and evaluated under inconsistent protocols.
Approach:	They propose a framework for seamless test-time compute scaling of large language model reasoning . they use a modular Python library to implement state-of-the-art scaling strategy and scorer families .
Outcome:	The proposed framework evaluates performance and computational efficiency on mathematical and coding tasks.

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Yiruo Cheng, Kelong Mao, Ziliang Zhao, Guanting Dong, Hongjin Qian, Yongkang Wu, Tetsuya Sakai, Ji-Rong Wen, Zhicheng Dou

U-CORE: A Unified Deep Cluster-wise Contrastive Framework for Open Relation Extraction (2023.tacl-1)

Challenge:	Existing research focuses on single-turn RAG, leaving a gap in addressing multi-turn conversations . a new benchmark is designed to assess RAG systems in realistic multi-turned conversations based on Wikipedia .
Approach:	They propose a large-scale benchmark to assess RAG systems in multi-turn contexts . CORAL includes diverse information-seeking conversations automatically derived from Wikipedia . authors propose unified framework to standardize various conversational RAG methods .
Outcome:	The proposed framework supports three core tasks of conversational RAG: passage retrieval, response generation, and citation labeling.

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Jie Zhou, Shenpo Dong, Yunxin Huang, Meihan Wu, Haili Li, Jingnan Wang, Hongkui Tu, Xiaodong Wang

Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents (2025.emnlp-demos)

Challenge:	Existing methods for Relation Extraction (RE) are limited due to the overlap between predefined and undefined relations.
Approach:	They propose a unified framework for both Zero-shot and Unsupervised Relation Extraction tasks by leveraging techniques from Contrastive Learning and Clustering.
Outcome:	The proposed framework improves on three well-known datasets showing an average improvement of 7.35% ARI on Zero-shot ORE tasks and 15.24% ARI for Unsupervised ORE.

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Ziyang Miao, Qiyu Sun, Jingyuan Wang, Yuchen Gong, Yaowei Zheng, Shiqi Li, Richong Zhang

LLMs as Bridges: Reformulating Grounded Multimodal Named Entity Recognition (2024.findings-acl)

Challenge:	Existing data synthesis tools struggle to extract reliable fine-tuning data from heterogeneous documents.
Approach:	They propose a framework for synthesizing fine-tuning data from unstructured documents via an intuitive graphical user interface.
Outcome:	The proposed framework can extract reliable data from unstructured documents via an intuitive graphical user interface (GUI) it leverages persona-driven prompting approach to generate diverse question-answer pairs using public-available LLMs.

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Jinyuan Li, Han Li, Di Sun, Jiahao Wang, Wenkun Zhang, Zan Wang, Gang Pan

Visual Data in NLP

Challenge:	Existing methods for Grounded Multimodal Named Entity Recognition (GMNER) lack a strong correlation between image-text pairs and is ungroundable.
Approach:	They propose a framework that reformulates GMNER into a joint MNER-VE-VG task by leveraging large language models as a connecting bridge.
Outcome:	The proposed framework outperforms state-of-the-art methods on the existing GMNER dataset and achieves absolute leads of 10.65%, 6.21%, and 8.83% in all three subtasks.

Training Language Models to Critique With Multi-agent Feedback (2025.findings-emnlp)

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Tian Lan, Wenwei Zhang, Chengqi Lyu, Shuaibin Li, Chen Xu, Heyan Huang, Dahua Lin, Xian-Ling Mao, Kai Chen

CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training (2022.findings-naacl)

Challenge:	utilizing human annotations can enhance critique ability, but model-generated critiques suffer from inherent flaws due to complexity of critique . a new framework that leverages multi-agent feedback improves critique ability .
Approach:	They propose a framework that leverages multi-agent feedback to improve critique ability . they propose to use supervised fine-tuning and reinforcement learning to improve this capability .
Outcome:	The proposed framework improves critique ability in both supervised fine-tuning and reinforcement learning stages.

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Xin Wang, Yasheng Wang, Yao Wan, Jiawei Wang, Pingyi Zhou, Li Li, Hao Wu, Jin Liu

Bias in the Ear of the Listener: Assessing Sensitivity in Audio Language Models Across Linguistic, Demographic, and Positional Variations (2026.findings-eacl)

Challenge:	Recent studies have focused on code representation learning, which aims to represent the semantics of source code into distributed vectors.
Approach:	They propose to integrate different views with the natural-language description of source code into a unified framework with Multi-View contrastive Pre-training.
Outcome:	The proposed model outperforms state-of-the-art models on three downstream tasks over five datasets.

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Sheng-Lun Wei, Yu-Ling Liao, Yen-Hua Chang, Hen-Hsen Huang, Hsin-Hsi Chen

On the Efficacy of Sampling Adapters (2023.acl-long)

Challenge:	Recent advances extend language understanding beyond text to speech, enabling unified reasoning across modalities.
Approach:	They construct and release a speech-augmented benchmark based on Global MMLU Lite and a data set spanning English, Chinese, and Korean.
Outcome:	The proposed model is robust to demographic factors but sensitive to language and option order, suggesting that speech can amplify structural biases.

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Clara Meister, Tiago Pimentel, Luca Malagutti, Ethan Wilcox, Ryan Cotterell

Green & Sustainable NLP

Challenge:	Using sampling adapters can improve the quality of the generated text.
Approach:	They propose a framework for understanding sampling adapters and propose 'sampling adapters' they argue that the shift enforced by them can be viewed as a trade-off between precision and recall .
Outcome:	The proposed framework can be used to improve the quality of language models by modifying their distributions to improve their precision and recall.

InstaJudge: Aligning Judgment Bias of LLM-as-Judge with Humans in Industry Applications (2025.emnlp-industry)

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Myeongjun Erik Jang, Fran Silavong

Extracting Trigger-sharing Events via an Event Matrix (2022.findings-emnlp)

Challenge:	Automated evaluation using LLM-as-Judge is a viable alternative to human evaluation, but misalignment of judgment biases between humans and LLMs hinders its use in real-world applications.
Approach:	They propose an LLM-as-Judge library that improves alignments of judgment biases through automatic prompt optimization.
Outcome:	The proposed library outperforms existing LLM-as-Judge libraries by a large margin while being more cost efficient.

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Jun Xu, Weidi Xu, Mengshu Sun, Taifeng Wang, Wei Chu

COFAR: Commonsense and Factual Reasoning in Image Search (2022.aacl-main)

Challenge:	Existing methods to extract multiple events with triggers and arguments are invalid as there may be multiple events.
Approach:	They propose a framework for event extraction which models the relations between arguments by an event matrix.
Outcome:	The proposed framework beats all the advanced competitors on 3 widely-used datasets.

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Prajwal Gatti, Abhirama Subramanyam Penamakuri, Revant Teotia, Anand Mishra, Shubhashis Sengupta, Roshni Ramnani

Unifying Streaming and Non-streaming Zipformer-based ASR (2025.acl-industry)

Challenge:	Existing approaches to retrieve relevant images for natural language searches are limited by visual recognition and lack of commonsense reasoning.
Approach:	They propose a framework that leverages visual content and natural language queries to enable commonsense reasoning and factual reasoning in the image search.
Outcome:	The proposed framework enables commonsense and factual reasoning in image search on a COFAR dataset.

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Bidisha Sharma, Karthik Pandia D S, Shankar Venkatesan, Jeena J Prakash, Shashi Kumar, Malolan Chetlur, Andreas Stolcke

A Semi-supervised Scalable Unified Framework for E-commerce Query Classification (2025.acl-industry)

Challenge:	Existing frameworks for streaming and non-streaming ASR models have been used to reduce development, training and deployment costs.
Approach:	They propose to use dynamic right-context through chunked attention masking to train zipformer-based ASR models.
Outcome:	The proposed framework reduces word error by relative 7.9% with a small degradation in user-perceived latency.

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Chunyuan Yuan, Chong Zhang, Zhen Fang, Ming Pang, Xue Jiang, Changping Peng, Zhangang Lin, Ching Law

UniFashion: A Unified Vision-Language Model for Multimodal Fashion Retrieval and Generation (2024.emnlp-main)

Challenge:	Existing query classification methods rely on posterior click behavior to construct training samples, resulting in insufficient prior information for modeling.
Approach:	They propose a semi-supervised scaleable unified framework that integrates enhanced modules to unify query classification tasks.
Outcome:	The proposed framework outperforms the state-of-the-art models in offline and online A/B experiments.

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Xiangyu Zhao, Yuehan Zhang, Wenlong Zhang, Xiao-Ming Wu

ConTempo: A Unified Temporally Contrastive Framework for Temporal Relation Extraction (2024.findings-acl)

Challenge:	e-commerce tasks such as multimodal retrieval and multimodal generation are largely ignored due to the diversity of the multimodal fashion domain.
Approach:	They propose a framework that integrates image generation with retrieval and text generation tasks.
Outcome:	The proposed framework outperforms state-of-the-art models across fashion tasks.

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Jingcheng Niu, Saifei Liao, Victoria Ng, Simon De Montigny, Gerald Penn

HierDiffuse: Progressive Diffusion for Robust Interest Fusion in CTR Prediction (2025.emnlp-industry)

Challenge:	Temporal relation extraction (TRE) is a task of classifying temporal relations between events conveyed in narratives.
Approach:	They propose a Temporally Contrastive learning model that increases the model’s awareness of the meaning of temporal relations by leveraging their symmetric or antisymmetric properties.
Outcome:	The proposed model improves the model's representation of meaning of temporal relations and its ability to integrate with the underlying temporal calculus.

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Ziheng Ni, Congcong Liu, Yuying Chen, Zhiwei Fang, Changping Peng, Zhangang Lin, Ching Law, Jingping Shao

KERS: A Knowledge-Enhanced Framework for Recommendation Dialog Systems with Multiple Subgoals (2021.findings-emnlp)

Challenge:	Existing approaches fuse long-term behavioral profiles and short-term interactions, suffering from representational misalignment and noise in transient signals.
Approach:	They propose a framework that redefines interest fusion as a hierarchical denoising process through diffusion models.
Outcome:	The proposed framework redefines interest fusion as a hierarchical denoising process through diffusion models.

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Jun Zhang, Yan Yang, Chencai Chen, Liang He, Zhou Yu

I-SEE: An Instruction-tuned, SOP-Enhanced Quality Evaluator for Product Content (2025.emnlp-industry)

Challenge:	Existing frameworks for multi-subgoal dialogs require a system to build a social bond with users to gain trust and develop affinity.
Approach:	They propose a framework for common knowledge-based multi-subgoal dialogs that divides up conversations with multiple subgoals and propose mechanisms to filter noisy knowledge and to include cleaned knowledge in the dialog response generation process.
Outcome:	The proposed framework obtains state-of-the-art results on a DuRecDial dataset in both automatic and human evaluation.

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Aniket Joshi, Cyrus Andre DSouza, Sejal Jain, Jitenkumar Babubhai Rana, Promod Yenigalla

A Systematic Study of Leveraging Subword Information for Learning Word Representations (N19-1)

Challenge:	Existing approaches to content evaluation treat information uniformly without prioritizing based on customer relevance.
Approach:	They propose a framework that combines domain expertise with a single instruction to improve content.
Outcome:	a new framework outperforms existing models in detecting inconsistencies across 20 product categories and 150 product specific features.

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Yi Zhu, Ivan Vulić, Anna Korhonen

Representation Learning

Challenge:	Existing word representation models for morphologically rich languages use subword-level information, but their systematic comparative analysis across typologically diverse languages and tasks is still missing.
Approach:	They propose a framework for learning subword-informed word representations that allows for easy experimentation with different segmentation and composition components.
Outcome:	The proposed framework allows for easy experimentation with different segmentation and composition components, as well as advanced techniques based on position embeddings and self-attention.

Triad: A Framework Leveraging a Multi-Role LLM-based Agent to Solve Knowledge Base Question Answering (2024.emnlp-main)

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Chang Zong, Yuchen Yan, Weiming Lu, Jian Shao, Yongfeng Huang, Heng Chang, Yueting Zhuang

AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehension (2024.acl-long)

Challenge:	Recent advances with LLMs have shown promising results across various tasks, but their use in answering questions from knowledge bases remains largely unexplored.
Approach:	They propose a framework that utilizes an LLM-based agent with multiple roles for KBQA tasks.
Outcome:	The proposed framework outperforms state-of-the-art systems on the LC-QuAD and YAGO-QA benchmarks yielding F1 scores of 11.8% and 20.7%, respectively.

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Qian Yang, Jin Xu, Wenrui Liu, Yunfei Chu, Ziyue Jiang, Xiaohuan Zhou, Yichong Leng, Yuanjun Lv, Zhou Zhao, Chang Zhou, Jingren Zhou

The Art of Prompting: Event Detection based on Type Specific Prompts (2023.acl-short)

Challenge:	Existing benchmarks for audio-centric interaction have impeded advancements in this field . AIR-Bench evaluates LALMs' ability to understand audio signals and interact with humans .
Approach:	They propose a benchmark to evaluate the ability of large audio-language models to understand audio signals . they use 19 tasks with approximately 19k single-choice questions to examine single-task ability .
Outcome:	The proposed framework evaluates the ability of large audio-language models to understand audio signals and interact with humans in the textual format.

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Sijia Wang, Mo Yu, Lifu Huang

Speaking Beyond Language: A Large-Scale Multimodal Dataset for Learning Nonverbal Cues from Video-Grounded Dialogues (2025.acl-long)

Challenge:	Experimental results show that a well-defined and comprehensive description of event types can significantly improve event detection performance when the annotations are limited.
Approach:	They propose a unified framework to integrate event type specific prompts for supervised, few-shot and zero-shot event detection.
Outcome:	The proposed framework shows up to 22.2% gain over the prior state-of-the-art frameworks.

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Youngmin Kim, Jiwan Chung, Jisoo Kim, Sunghyun Lee, Sangkyu Lee, Junhyeok Kim, Cheoljong Yang, Youngjae Yu

MolTC: Towards Molecular Relational Modeling In Language Models (2024.findings-acl)

Challenge:	Existing large language models fail to incorporate nonverbal elements into conversational experiences.
Approach:	They propose a multimodal language model that generates nonverbal cues alongside text . their dataset is annotated with time-aligned text, facial expressions, and body language .
Outcome:	The proposed model generates nonverbal languages and text, corresponding to conversational input.

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Junfeng Fang, Shuai Zhang, Chang Wu, Zhengyi Yang, Zhiyuan Liu, Sihang Li, Kun Wang, Wenjie Du, Xiang Wang

LoRA Meets Dropout under a Unified Framework (2024.findings-acl)

Challenge:	Molecular Relational Learning (MRL) is a promising way to understand interactions between molecular pairs.
Approach:	They propose a novel LLM-based multi-modal framework for molecular interaction modeling following Chain-of-Thought (CoT) theory which integrates graphical information of two molecules in pair.
Outcome:	The proposed framework integrates graphical information of two molecules in pair.

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Sheng Wang, Liheng Chen, Jiyue Jiang, Boyang Xue, Lingpeng Kong, Chuan Wu

Green & Sustainable NLP

Challenge:	Parameter-efficientfinetuning (PEFT) has gained popularity as a lightweight approach for model customization.
Approach:	They propose a parameter-efficient dropout method that is overfitting-prone and parameter-freezed.
Outcome:	The proposed method is superior to existing methods and compares with transformer-specific methods.

OneRec-Think: In-Text Reasoning for Generative Recommendation (2026.acl-long)

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Zhanyu Liu, Shiyao Wang, Xingmei Wang, Rongzhou Zhang, Jiaxin Deng, Honghui Bao, Jinghao Zhang, Wuchao Li, PengFei Zheng, Xiangyu Wu, Yifei Hu, Qigen Hu, Xinchen Luo, Lejian Ren, Zhang Zixing, Qianqian Wang, Kuo Cai, Yunfan Wu, Hongtao Cheng, Zexuan Cheng, Lu Ren, Huanjie Wang, Yi Su, Ruiming Tang, Kun Gai, Guorui Zhou

TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge (2022.findings-acl)

Challenge:	Existing generative models lack the capacity for explicit and controllable reasoning, a key advantage of LLMs.
Approach:	They propose a framework that integrates dialogue, reasoning, and personalized recommendation.
Outcome:	Experiments across public benchmarks show state-of-the-art performance.

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Chao-Hong Tan, Jia-Chen Gu, Chongyang Tao, Zhen-Hua Ling, Can Xu, Huang Hu, Xiubo Geng, Daxin Jiang

A Unified Framework for N-ary Property Information Extraction in Materials Science (2025.findings-emnlp)

Challenge:	Generating natural and informative texts has been a long-standing problem in NLP.
Approach:	They propose to augment TExt Generation via Task-specific and Open-world Knowledge in a unified framework.
Outcome:	The proposed model can learn what and how to generate on two text generation tasks.

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Van-Thuy Phi, Yuji Matsumoto

Learning Reasoning Patterns for Relational Triple Extraction with Mutual Generation of Text and Graph (2022.findings-acl)

Challenge:	a framework for extracting n-ary property information from materials science literature is proposed . the framework addresses the critical challenge of capturing complex relationships that span multiple sentences.
Approach:	They propose a framework for extracting n-ary property information from materials science literature . they propose three complementary approaches to capture complex relationships that span multiple sentences .
Outcome:	The proposed framework outperforms existing methods in n-ary property extraction tasks.

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Yubo Chen, Yunqi Zhang, Yongfeng Huang

Are Any-to-Any Models More Consistent Across Modality Transfers Than Specialists? (2025.acl-long)

Challenge:	Existing methods focused on learning text patterns from explicit mentions but failed to extract the implicitly implied triples.
Approach:	They propose to construct a relational graph from a sentence and apply multi-layer graph convolutions to capture the type inference logic of the paths.
Outcome:	The proposed framework can find multi-hop reasoning paths and capture type inference logic with the sentence's supplementary relational expressions.

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Jiwan Chung, Janghan Yoon, Junhyeong Park, Sangeyl Lee, Joowon Yang, Sooyeon Park, Youngjae Yu

Think Outside the Policy: In-Context Steered Policy Optimization (2026.findings-acl)

Challenge:	equivariance evaluations uncover weak but observable consistency through structured analyses of the intermediate latent space enabled by multiple editing operations.
Approach:	They use a dataset of 1,000 images paired with captions, editing instructions, and Q&A pairs to evaluate cross-modal transfers rigorously.
Outcome:	The proposed models do not consistently demonstrate greater cross-modal consistency than specialized models in pointwise evaluations such as cyclic consistency.

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Hsiu-Yuan Huang, Chenming Tang, Weijie Liu, Clive Bai, Saiyong Yang, Yunfang Wu

CLaMP 3: Universal Music Information Retrieval Across Unaligned Modalities and Unseen Languages (2025.findings-acl)

Challenge:	Existing Reinforcement Learning from Verifiable Rewards (RLVR) methods exhibit limited exploration due to reliance on on-policy rollouts which are limited to the current policy’s distribution, resulting in narrow trajectory diversity.
Approach:	They propose a framework that leverages the in-context learning capability of Large Reasoning Models to provide expert guidance using existing datasets.
Outcome:	The proposed framework improves RLVR performance and training stability on mathematical reasoning benchmarks.

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Shangda Wu, Guo Zhancheng, Ruibin Yuan, Junyan Jiang, SeungHeon Doh, Gus Xia, Juhan Nam, Xiaobing Li, Feng Yu, Maosong Sun

NLP-ADBench: NLP Anomaly Detection Benchmark (2025.findings-emnlp)

Challenge:	Music information retrieval (MIR) is a field that aims at developing computational tools for processing, organizing, and accessing music data.
Approach:	They propose a framework that aligns music modalities with multilingual text in a shared representation space.
Outcome:	Experiments show CLaMP 3 performs state-of-the-art on multiple MIR tasks . it surpasses baselines and shows excellent generalization in multimodal and multilingual contexts .

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Yuangang Li, Jiaqi Li, Zhuo Xiao, Tiankai Yang, Yi Nian, Xiyang Hu, Yue Zhao

The Devil is in the Details: On Models and Training Regimes for Few-Shot Intent Classification (2023.eacl-main)

Challenge:	Anomaly detection (AD) is an important machine learning task, but its effectiveness in detecting harmful content, phishing attempts, and spam reviews is limited.
Approach:	They introduce NLP-ADBench, the most comprehensive NLP anomaly detection benchmark to date . it includes eight curated datasets and 19 state-of-the-art algorithms .
Outcome:	The NLP-ADBench benchmark includes 19 state-of-the-art methods and 8 curated datasets . no single model dominates across all datasets, indicating need for automated model selection .

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Mohsen Mesgar, Thy Thy Tran, Goran Glavaš, Iryna Gurevych

Sibylvariant Transformations for Robust Text Classification (2022.findings-acl)

Challenge:	Recent methods for task-oriented dialog (ToD) intent classification use pretrained language models . but lack of informative ablations prevents identification of factors that drive performance .
Approach:	They propose a framework to evaluate components of Few-Shot Intent Classification . they propose to combine cross-encoder architecture and episodic meta-learning .
Outcome:	The proposed framework evaluates cross-encoder architecture and episodic meta-learning . it also shows that splitting episodes into support and query sets outperforms non-episodic counterparts.

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Fabrice Harel-Canada, Muhammad Ali Gulzar, Nanyun Peng, Miryung Kim

ArcaneQA: Dynamic Program Induction and Contextualized Encoding for Knowledge Base Question Answering (2022.coling-1)

Challenge:	Existing text transformation techniques are limited in their ability to expand input space . many techniques can artificially expand labeled training sets or test suites, but are class-preserving .
Approach:	They propose a concept of sibylvariance to describe transforms that relax the label-preserving constraint and knowably vary the expected class.
Outcome:	The proposed transforms can expand input space, but they are limited in their ability to expand . the proposed transform can knowably vary the expected class and lead to more diverse distributions .

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Yu Gu, Yu Su

TopicBERT for Energy Efficient Document Classification (2020.findings-emnlp)

Challenge:	Existing ranking-based KBQA models struggle with flexibility in predicting complicated queries and have impractical running time.
Approach:	They propose a new generation-based question answering on knowledge bases model that addresses both large search space and ambiguities in schema linking.
Outcome:	The proposed model overcomes two intertwined challenges on popular KBQA datasets and is highly competitive and efficient.

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Yatin Chaudhary, Pankaj Gupta, Khushbu Saxena, Vivek Kulkarni, Thomas Runkler, Hinrich Schütze

Multi-component Causal Tracing in Large Language Models (2026.acl-long)

Challenge:	Prior work has noted that BERT’s computational cost grows quadratically with sequence length thus leading to longer training times, higher GPU memory constraints and carbon emissions.
Approach:	They propose to combine topic and language models to optimize the computational cost of fine-tuning for document classification by complementary learning.
Outcome:	The proposed model achieves a 1.4x speedup with 40% reduction in CO2 emission while retaining 99.9% performance over 5 datasets.

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Zirui Yan, Dennis Wei, Dmitriy A Katz, Prasanna Sattigeri, Ali Tajer

An Attribute Enhanced Domain Adaptive Model for Cold-Start Spam Review Detection (C18-1)

Challenge:	Large language models (LLMs) are prone to various forms of safety risks, such as learning and propagating societal biases and even creating harmful or deceptive content through jailbreak attacks.
Approach:	They propose a framework for causally tracing multiple components simultaneously that systematically identifies the subsets of components most critical to a desired performance metric.
Outcome:	The proposed method outperforms existing methods in identifying components critical to a desired performance metric.

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Zhenni You, Tieyun Qian, Bing Liu

Challenge:	Existing approaches to spam detection focus on extracting linguistic or behavior features to distinguish the spam and legitimate reviews.
Approach:	They propose a deep learning architecture for incorporating entities and their attributes into a unified framework.
Outcome:	The proposed framework outperforms the state-of-the-art methods on two Yelp datasets.

Unifying Human and Statistical Evaluation for Natural Language Generation (N19-1)

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Tatsunori B. Hashimoto, Hugh Zhang, Percy Liang

Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts (2023.emnlp-main)

Challenge:	Human evaluation captures quality but fails to capture diversity . statistical evaluation fails to catch models that plagiarize from training set .
Approach:	They propose a framework which evaluates both diversity and quality based on the optimal error rate of predicting whether a sentence is human-generated.
Outcome:	The proposed framework evaluates diversity and quality on summarization and chit-chat dialogue.

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Tengxiao Liu, Qipeng Guo, Yuqing Yang, Xiangkun Hu, Yue Zhang, Xipeng Qiu, Zheng Zhang

Strategize Before Teaching: A Conversational Tutoring System with Pedagogy Self-Distillation (2023.findings-eacl)

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.

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Lingzhi Wang, Mrinmaya Sachan, Xingshan Zeng, Kam-Fai Wong

A Unified Generative Framework for Aspect-based Sentiment Analysis (2021.acl-long)

Challenge:	Conversational tutoring systems (CTSs) aim to help students master educational material with natural language interaction in the form of a dialog.
Approach:	They propose to jointly predict teaching strategies and generate tutor responses accordingly to help students master educational material through dialog.
Outcome:	The proposed framework is based on three dialog tutoring datasets and is more realistic than previous models that generate responses given the strategies as input.

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Hang Yan, Junqi Dai, Tuo Ji, Xipeng Qiu, Zheng Zhang

Sentiment Analysis

Challenge:	Existing complicated ABSA models focus on subtasks, which leads to complicated solutions . et al., j. c. d. r., and j dr. s. v. present a unified approach to solve seven subtask tasks in one framework.
Approach:	They redefine every subtask target as a sequence mixed by pointer indexes and sentiment class indexe . they exploit the pre-training sequence-to-sequence model BART to solve all ABSA subtasks in an end-to end framework.
Outcome:	The proposed framework achieves substantial performance gain and provides a real unified solution for the whole ABSA subtasks.

Unified Semantic Typing with Meaningful Label Inference (2022.naacl-main)

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James Y. Huang, Bangzheng Li, Jiashu Xu, Muhao Chen

Retrieval as Generation: A Unified Framework with Self-Triggered Information Planning (2026.acl-long)

Challenge:	Semantic typing aims at classifying tokens into semantic categories such as relations, entity types, and event types.
Approach:	They propose a unified framework for semantic typing that captures label semantics by projecting both inputs and labels into a joint semantic embedding space.
Outcome:	The proposed framework achieves strong performance across three semantic typing tasks.

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Bo Li, Mingda Wang, Gexiang Fang, Shikun Zhang, Wei Ye

Correcting Chinese Spelling Errors with Phonetic Pre-training (2021.findings-acl)

Challenge:	Existing models that ground retrieval on external evidence are limited in their ability to implement retrieval-augmented generation.
Approach:	They propose a retrieval-augmented generation model that embeds retrieval control directly into generation.
Outcome:	The proposed model surpasses strong RAG baselines and uses substantially fewer parameters.

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Ruiqing Zhang, Chao Pang, Chuanqiang Zhang, Shuohuan Wang, Zhongjun He, Yu Sun, Hua Wu, Haifeng Wang

Syntactic Text Processing

Challenge:	Existing methods for Chinese spelling correction only use pre-trained language model or incorporate phonological information as external knowledge.
Approach:	They propose a phonetic Chinese spelling correction model that integrates phonetic features into language model by leveraging pre-training and fine-tuning methods.
Outcome:	The proposed model outperforms existing methods on SIGHAN datasets and improves on other datasets.

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

Recurrent One-Hop Predictions for Reasoning over Knowledge Graphs (C18-1)

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.

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Wenpeng Yin, Yadollah Yaghoobzadeh, Hinrich Schütze

Asking Questions Like Educational Experts: Automatically Generating Question-Answer Pairs on Real-World Examination Data (2021.emnlp-main)

Challenge:	Large scale knowledge graphs (KGs) such as Freebase are generally incomplete.
Approach:	They propose a model that predicts entities at each step of mh-KB paths . the model is based on recurrent neural networks and vector representations of entities and relations .
Outcome:	The proposed models show state-of-the-art for two important multi-hop KG reasoning tasks.

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Fanyi Qu, Xin Jia, Yunfang Wu

Challenge:	Existing approaches to generate high quality question-answer pairs are limited . a new framework is proposed for the question-answer generation task on real-world examination data.
Approach:	They propose a multi-agent communication model to generate and optimize the question and keyphrases iteratively and then apply the generated question and keys to guide the generation of answers.
Outcome:	The proposed framework makes great breakthroughs in the question-answer pair generation task.

AVA: Attentive VLM Agent for Mastering StarCraft II (2026.findings-acl)

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Weiyu Ma, Yuqian Fu, Zecheng Zhang, Bernard Ghanem, Guohao Li

ARREST: Adversarial Resilient Regulation Enhancing Safety and Truth in Large Language Models (2026.eacl-long)

Challenge:	Existing StarCraft II benchmarks rely on abstract state representations that deviate from human perception . Existing systems rely only on abstract representations, creating an artificial gap between how humans process battlefield information and limiting ecological validity of learned behaviors.
Approach:	They introduce AVACraft, the first multimodal benchmark environment for complex decision-making in StarCraft II.
Outcome:	The AVACraft benchmark supports both traditional and modern multi-agent reinforcement learning paradigms.

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Sharanya Dasgupta, Arkaprabha Basu, Sujoy Nath, Swagatam Das

Bridging SFT and RL: Dynamic Policy Optimization for Robust Reasoning (2026.findings-acl)

Challenge:	Large Language Models lack human cognition to balance factuality and safety . a common issue is representational misalignment in their activation space .
Approach:	They propose a framework that regulates drifted features and engages both soft and hard refusals in addition to factual corrections.
Outcome:	The proposed framework regulates falsehood into truthfulness and unsafe output into safe output without fine-tuning the model's parameters.

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Taojie Zhu, Dongyang Xu, Ding Zou, Sen Zhao, Qiaobo Hao, Zhiguo Yang, Yonghong He

Incorporating Glosses into Neural Word Sense Disambiguation (P18-1)

Challenge:	Existing unified optimization strategies overlook the statistical conflict between these distinct gradient signals.
Approach:	They propose a framework to reduce bias-variance trade-offs in Large Language Models . they propose DYPO, which leverages intrinsic group dynamics to significantly reduce RL gradient variance .
Outcome:	The proposed framework outperforms traditional pipelines on reasoning benchmarks and out-of-distribution tasks.

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Fuli Luo, Tianyu Liu, Qiaolin Xia, Baobao Chang, Zhifang Sui

A Unified Framework for Pun Generation with Humor Principles (2022.findings-emnlp)

Challenge:	Existing neural networks for Word Sense Disambiguation rely on labeled data and lexical knowledge.
Approach:	They propose a gloss-augmented WSD neural network which integrates context and glosses of the target word into a unified framework.
Outcome:	The proposed model outperforms the state-of-the-art systems on several English all-words WSD datasets.

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Yufei Tian, Divyanshu Sheth, Nanyun Peng

Making Flexible Use of Subtasks: A Multiplex Interaction Network for Unified Aspect-based Sentiment Analysis (2021.findings-acl)

Challenge:	Existing models for generating homophonic and homographic puns lack the linguistic attributes of successful puns to resolve the split-up in existing work.
Approach:	They propose a framework to generate both homophonic and homographic puns to resolve the split-up in existing works by incorporating three linguistic attributes of puns into the language models: ambiguity, distinctiveness, and surprise.
Outcome:	The proposed model over strong baselines shows that it can generate both homophonic and homographic puns.

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Guoxin Yu, Xiang Ao, Ling Luo, Min Yang, Xiaofei Sun, Jiwei Li, Qing He

Model-Free Context-Aware Word Composition (C18-1)

Challenge:	Existing studies aim to integrate multiple sub-tasks into a unified ABSA model but suffer from major disadvantages .
Approach:	They propose a multi-task learning approach to make use of sub-tasks for a unified ABSA.
Outcome:	The proposed model can work well when some sub-tasks are absent, and the interactive relations among subtasks not adequate.

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Bo An, Xianpei Han, Le Sun

Towards a unified framework for bilingual terminology extraction of single-word and multi-word terms (C18-1)

Challenge:	Word composition models do not take ambiguity of words and context into consideration for learning representations, and thus suffer from the inaccurate representation of semantics.
Approach:	They propose a model-free context-aware word composition model which takes the latent semantic information as global context for learning representations.
Outcome:	The proposed model improves on existing composition models at different granularities and shows that it can be used to learn semantics.

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Jingshu Liu, Emmanuel Morin, Peña Saldarriaga

Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation (2025.naacl-long)

Challenge:	Existing methods for extracting bilingual terminology from comparable corpora are limited to a set of syntactic patterns.
Approach:	They propose a framework for aligning bilingual terms independently of term lengths . they introduce some enhancements to the context-based and neural network based approaches .
Outcome:	The proposed framework improves the performance of the context-based and neural network based approaches and can be adapted in specialized domains.

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Satyapriya Krishna, Kalpesh Krishna, Anhad Mohananey, Steven Schwarcz, Adam Stambler, Shyam Upadhyay, Manaal Faruqui

Single LLM, Multiple Roles: A Unified Retrieval-Augmented Generation Framework Using Role-Specific Token Optimization (2025.emnlp-main)

Challenge:	Recent advances in Large Language Models (LLMs) have significantly enhanced their capabilities across various cognitive tasks.
Approach:	They propose a high-quality evaluation dataset to test LLMs' ability to provide factual responses, assess retrieval capabilities, and evaluate the reasoning required to generate final answers.
Outcome:	The proposed framework improves performance in end-to-end RAG scenarios.

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Yutao Zhu, Jiajie Jin, Hongjin Qian, Zheng Liu, Zhicheng Dou, Ji-Rong Wen

Bridging Attribution and Open-Set Detection using Graph-Augmented Instance Learning in Synthetic Speech (2026.eacl-long)

Challenge:	Existing studies have optimized retrieval-augmented generation (RAG) across sub-tasks, but integrating these optimizations into a unified framework remains challenging.
Approach:	They propose a unified retrieval-augmented generation framework that optimizes role-specific tokens for multi-task processing.
Outcome:	The proposed framework achieves efficient multi-task processing through role-specific token optimization.

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Mohd Mujtaba Akhtar, null Girish, Farhan Sheth, Muskaan Singh

Support or Refute: Analyzing the Stance of Evidence to Detect Out-of-Context Mis- and Disinformation (2023.emnlp-main)

Challenge:	Synthetic speech detection is a critical part of safeguarding digital communication, enabling systems to identify and mitigate the risks posed by highly realistic, machine-generated voices.
Approach:	They propose a framework that combines SFMs with graph-based modeling and open-set generalization to capture meaningful relationships between utterances and recognize speech that doesn’t belong to any known generator.
Outcome:	The proposed framework improves performance across both tasks, with Mamba-based embeddings delivering particularly strong results.

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Xin Yuan, Jie Guo, Weidong Qiu, Zheng Huang, Shujun Li

A Unified Syntax-aware Framework for Semantic Role Labeling (D18-1)

Challenge:	Mis- and disinformation online are a major source of harms of different kinds . out-of-context information is where different pieces of information are falsely associated . past studies have attempted to defend against OOC mis- and deinformation through external evidence, but they disregard the role of different pieces with different stances.
Approach:	They propose a stance extraction network that can extract stances of different pieces of evidence in a single framework.
Outcome:	The proposed model outperforms the state-of-the-art models on a public large-scale dataset with a performance gain of 3.2% in accuracy.

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Zuchao Li, Shexia He, Jiaxun Cai, Zhuosheng Zhang, Hai Zhao, Gongshen Liu, Linlin Li, Luo Si

Structured Sentiment Analysis as Dependency Graph Parsing (2021.acl-long)

Challenge:	Syntactic information has been paid a great attention over the role of enhancing SRL . but the gap between syntax-aware and syntax-gnostic SRL is smaller . a new framework proposes syntax-based SRL for a wide range of NLP tasks .
Approach:	They propose to extend existing models to investigate more effective ways of incorporating syntax into sequential neural networks.
Outcome:	The proposed framework outperforms existing models on CoNLL-2009 benchmarks in English and Chinese.

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Jeremy Barnes, Robin Kurtz, Stephan Oepen, Lilja Øvrelid, Erik Velldal

Cross-Media Keyphrase Prediction: A Unified Framework with Multi-Modality Multi-Head Attention and Image Wordings (2020.emnlp-main)

Challenge:	Structured sentiment analysis attempts to extract full opinion tuples from a text, but has been subdivided into smaller and smaller sub-tasks, e.g., target extraction or targeted polarity classification.
Approach:	They propose a framework which jointly predicts all elements of an opinion tuple and their relations by using dependency graph parsing.
Outcome:	The proposed framework improves on five datasets in English, Norwegian, Basque, and Catalan and refining the sentiment graphs with syntactic dependency information further improves results.

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Yue Wang, Jing Li, Michael Lyu, Irwin King

ChatVLA: Unified Multimodal Understanding and Robot Control with Vision-Language-Action Model (2025.emnlp-main)

Challenge:	Existing studies focus on text modeling, ignoring the rich features embedded in the matching images.
Approach:	They propose a novel multi-modal multi-head attention model to capture cross-media interactions and image wordings to bridge the two modalities.
Outcome:	The proposed model outperforms the current state of the art based on text modeling and image matching .

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Zhongyi Zhou, Yichen Zhu, Minjie Zhu, Junjie Wen, Ning Liu, Zhiyuan Xu, Weibin Meng, Yaxin Peng, Chaomin Shen, Feifei Feng, Yi Xu

CSPB: Conversational Speech Processing Benchmark for Self-supervised Speech Models (2026.eacl-long)

Challenge:	Recent advances in vision-language-action models prioritize robotic action mastery . however, models trained on visual-text pairs struggle to interpret multimodal data .
Approach:	They propose a framework that integrates multimodal data after initial control mastery and a Mixture-of-Experts architecture to minimize task interference.
Outcome:	The proposed framework surpasses state-of-the-art vision-language-action (VLA) methods on multimodal understanding benchmarks and achieves six times higher performance on visual question-answering datasets.

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Zili Huang, Matthew Maciejewski, Leibny Paola Garcia Perera, Shinji Watanabe, Sanjeev Khudanpur

A Survey on Personalized Alignment—The Missing Piece for Large Language Models in Real-World Applications (2025.findings-acl)

Challenge:	Existing benchmarks focus on clean, single-speaker, single channel audio, failing to reflect the complexities of natural human interaction.
Approach:	They propose a benchmark to assess the robustness of self-supervised speech models in conversational settings.
Outcome:	The proposed benchmark assesses the robustness of self-supervised speech models in conversational scenarios.

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Jian Guan, Junfei Wu, Jia-Nan Li, Chuanqi Cheng, Wei Wu

RelU-Net: Syntax-aware Graph U-Net for Relational Triple Extraction (2022.emnlp-main)

Challenge:	Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their transition to real-world applications reveals a critical limitation: the inability to adapt to individual preferences while maintaining alignment with universal human values.
Approach:	They propose a framework that enables LLMs to adapt their behavior within ethical boundaries based on individual preferences.
Outcome:	The proposed framework analyzes implementation approaches and evaluates their effectiveness across various scenarios.

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Yunqi Zhang, Yubo Chen, Yongfeng Huang

Cross-lingual Cross-modal Pretraining for Multimodal Retrieval (2021.naacl-main)

Challenge:	Existing methods focused on capturing semantic information but failed to incorporate syntactic structures of the sentence, which is proved to contain rich relational information.
Approach:	They propose a framework to capture syntactic information for relational triple extraction by contracting dependency tree into a core relational topology and eliminating redundant information with graph pooling operations.
Outcome:	The proposed framework incorporates syntactic information for relational triple extraction.

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Hongliang Fei, Tan Yu, Ping Li

CodeWiki: Evaluating AI’s Ability to Generate Holistic Documentation for Large-Scale Codebases (2026.findings-acl)

Challenge:	Recent pretrained vision-language models have achieved impressive performance on cross-modal retrieval tasks in English.
Approach:	They propose a new approach to learn cross-lingual cross-modal representations for matching images and captions in multiple languages using an annotated corpus.
Outcome:	The proposed model achieves impressive performance on two multimodal multilingual image caption benchmarks: Multi30k with German captions and MSCOCO with Japanese captions.

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Anh Nguyen Hoang, Minh Le-Anh, Bach Le, Nghi D. Q. Bui

Challenge:	Existing automated methods struggle to capture rich semantic dependencies and architectural structure.
Approach:	They propose a framework for automated repository-level documentation across seven programming languages.
Outcome:	The proposed framework outperforms the closed-source DeepWiki benchmark by 68.79% and is open source to support future research.

Aligning to Constraints for Data-Efficient Language Model Customization (2025.findings-naacl)

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Fei Wang, Chao Shang, Shuai Wang, Sarthak Jain, Qiang Ning, Bonan Min, Vittorio Castelli, Yassine Benajiba, Dan Roth

FGDGNN: Fine-Grained Dynamic Graph Neural Network for Rumor Detection on Social Media (2025.findings-acl)

Challenge:	General-purpose language models (LMs) are aligned to diverse user intents, but fall short when it comes to specific applications.
Approach:	They propose a framework that uses constraints to automatically produce supervision signals for user alignment with constraints.
Outcome:	The proposed framework can produce supervision signals for user alignment with constraints.

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Mei Guo, Chen Chen, Chunyan Hou, Yike Wu, Xiaojie Yuan

Joint Learning-based Heterogeneous Graph Attention Network for Timeline Summarization (2022.naacl-main)

Challenge:	Existing methods for detecting rumors on social media focus on coarse-grained temporal information and ignore fine-grain temporal dynamics.
Approach:	They propose a fine-grained dynamic graph neural network model which incorporates fine-grain temporal information into a unified framework for rumor detection.
Outcome:	The proposed model improves on three public real-world datasets.

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Jingyi You, Dongyuan Li, Hidetaka Kamigaito, Kotaro Funakoshi, Manabu Okumura

Text2Tabular – Reconstructing Tabular Research Data from Scientific Publications (2026.acl-long)

Challenge:	Existing studies on timeline summarization ignore the information interaction between sentences and dates, and combine them as two separate tasks.
Approach:	They propose a joint learning-based heterogeneous graph attention network for timeline summarization (HeterTls) they combine date selection and event detection into a unified framework to improve extraction accuracy .
Outcome:	The proposed model outperforms state-of-the-art models on four datasets . it significantly outperformed the baseline models on ROUGE scores and date selection metrics .

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Jonas Gottal, Florian Matthes

QualBench: Benchmarking Chinese LLMs with Localized Professional Qualifications for Vertical Domain Evaluation (2025.emnlp-main)

Challenge:	Text2Tabular reconstructs research datasets from scientific literature using advanced natural language processing and statistical modeling.
Approach:	Text2Tabular reconstructs research datasets from scientific publications using natural language processing and statistical modeling.
Outcome:	Text2Tabular reconstructs scientific literature-based datasets using natural language processing and statistical modeling.

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Mengze Hong, Wailing Ng, Chen Jason Zhang, Di Jiang

Challenge:	Existing benchmarks often lack domain coverage and provide limited insights into the working context of Chinese LLMs.
Approach:	They propose a multi-domain Chinese QA benchmark dedicated to localized assessment of Chinese LLMs.
Outcome:	The Qwen2.5 model outperforms the more advanced GPT-4o model in the Chinese market . the dataset includes over 17,000 questions across six vertical domains .

Eliminating Out-of-Domain Recommendations in LLM-based Recommender Systems: A Unified View (2026.findings-acl)

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Hao Liao, Jiwei Zhang, Jianxun Lian, Wensheng Lu, Mingqi Wu, null Shuowangg, Yong Zhang, Yitian Huang, Mingyang Zhou, Rui Mao

Principled Self-Correction in Discrete Diffusion: A UCB-Guided Framework for Text Generation (2026.eacl-long)

Challenge:	Existing approaches to reduce OOD recommendations fall into three grounding paradigms: retrieval, constrained generation and discrete item tokenizer generation.
Approach:	They propose a framework that instantiates three grounding paradigms under a single architecture . embedding-based retrieval, constrained generation and discrete item-tokenizer methods are implemented .
Outcome:	The proposed framework eradicates OOD recommendations across all variants and achieves state-of-the-art accuracy compared to strong ID-based and LLM-based baselines.

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Masaki Asada, Makoto Miwa

Graph Enhanced Contrastive Learning for Radiology Findings Summarization (2022.acl-long)

Challenge:	Existing diffusion models are trained on corrupted ground-truth tokens, but at inference time they must denoise inputs corruptes from their own predictions.
Approach:	They propose a framework that denoises inputs corrupted from their own predictions at inference time.
Outcome:	The proposed framework achieves higher faithfulness and coherence over existing diffusion baselines.

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Jinpeng Hu, Zhuo Li, Zhihong Chen, Zhen Li, Xiang Wan, Tsung-Hui Chang

Improving Neural Machine Translation by Multi-Knowledge Integration with Prompting (2023.findings-emnlp)

Challenge:	Existing methods for automating impression generation have limited the relationship between extra knowledge and the original findings.
Approach:	They propose a framework for automating impression generation that exploits extra knowledge and original findings . they propose combining key words and their relations to extract critical information .
Outcome:	The proposed framework exploits extra knowledge and the original findings in an integrated way . the state-of-the-art results on two datasets confirm the effectiveness of the proposed method .

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Ke Wang, Jun Xie, Yuqi Zhang, Yu Zhao

Take the essence and discard the dross: A Rethinking on Data Selection for Fine-Tuning Large Language Models (2025.naacl-long)

Challenge:	Existing methods focus on how to integrate multiple types of knowledge into NMT models .
Approach:	They propose a framework that integrates multiple types of knowledge into NMT models . they use multiple types as prefix-prompts of input for the encoder and decoder .
Outcome:	The proposed framework outperforms baselines on English-Chinese and English-German translation.

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Ziche Liu, Rui Ke, Yajiao Liu, Feng Jiang, Haizhou Li

Faithful-First Reasoning, Planning, and Acting for Multimodal LLMs (2026.findings-acl)

Challenge:	Existing studies focus on data selection but lack a clear, unified framework . variability in experimental settings complicates systematic comparisons .
Approach:	They propose a three-stage scheme to standardize data selection for fine-tuning large language models . they propose unified comparison approach that incorporates ratio-based efficiency and ranking-based feasibility metrics to address inconsistencies across experiments.
Outcome:	The proposed scheme outperforms existing methods in a dozen key studies and identifies key challenges.

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Junxian Li, Xinyue Xu, Sai Ma, Di Zhang, Sichao Li

Relation-Aware Collaborative Learning for Unified Aspect-Based Sentiment Analysis (2020.acl-main)

Challenge:	Existing efforts to improve task accuracy or enrich COT generation are lacking in multimodal large language models.
Approach:	They propose a Faithful-First Reasoning, Planning, and Acting framework that evaluates faithfulness of intermediate reasoning and uses it to plan and execute faithfulness-aware actions during inference.
Outcome:	The proposed framework improves perceptual faithfulness by up to 24% over prompt-based and tool-augmented reasoning frameworks without degrading task accuracy.

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Zhuang Chen, Tieyun Qian

Sentiment Analysis

Challenge:	Existing studies focus on one of three subtasks for aspect-based sentiment analysis (ABSA) Existing work develops separate methods for each subtask, or takes OE as an auxiliary task of AE.
Approach:	They propose a relation-aware collaborative learning framework which allows subtasks to work coordinately via multi-task learning and relation propagation mechanisms.
Outcome:	Extensive experiments on three real-world datasets show that RACL outperforms state-of-the-art methods for ABSA.

Reevaluating Adversarial Examples in Natural Language (2020.findings-emnlp)

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John Morris, Eli Lifland, Jack Lanchantin, Yangfeng Ji, Yanjun Qi

VideoPro: Adaptive Program Reasoning for Long Video Understanding (2026.acl-long)

Challenge:	State-of-the-art adversarial examples lack a common definition of what constitutes success . human surveys show that to preserve semantics, we need to increase the minimum cosine similarities between the embeddings of swapped words and between the sentence encodings of original and perturbed sentences.
Approach:	They propose a unified definition of what constitutes a successful adversarial example . they propose four categories of constraints that are used to define adversarials .
Outcome:	The proposed framework is based on the outputs of two state-of-the-art synonym substitution attacks.

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Chenglin Li, Feng Han, Yikun Wang, Ruilin Li, Shuai Dong, Haowen Hou, Haitao Li, Qianglong Chen, Feng Tao, Jingqi Tong, Yin Zhang, Jiaqi Wang

TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graph (2021.emnlp-main)

Challenge:	Existing methods for understanding long videos are limited due to the sparsity of visual evidence relevant to a given query.
Approach:	They propose a framework that enables VideoLLMs to reason over long videos and refine their predictions through executable programs.
Outcome:	The proposed framework outperforms existing methods across long-video understanding benchmarks.

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Jiaxin Shi, Shulin Cao, Lei Hou, Juanzi Li, Hanwang Zhang

A Systematic Study of Compositional Syntactic Transformer Language Models (2025.acl-long)

Challenge:	Existing models infer the answer by predicting the sequential relation path or aggregating the hidden graph features.
Approach:	They propose a model which jumps between entities at multiple steps . they demonstrate that TransferNet surpasses state-of-the-art models by a large margin .
Outcome:	The proposed model surpasses state-of-the-art models on MetaQA and on other datasets.

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Yida Zhao, Hao Xve, Xiang Hu, Kewei Tu

Active Generalized Category Discovery with Diverse LLM Feedback (2026.eacl-long)

Challenge:	Syntactic language models (SLMs) incorporate syntactical biases into Transformers . authors identify key aspects of design choices in existing models and novel variants based on experimental results .
Approach:	They propose a framework that incorporates existing and new SLMs to enhance Transformers by incorporating syntactic biases.
Outcome:	The proposed framework improves on existing models and novel variants across language modeling, syntactic generalization, summarization, and inference efficiency.

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Henry Peng Zou, Siffi Singh, Yi Nian, Jianfeng He, Jason Cai, Saab Mansour, Hang Su

Generating Responses with a Specific Emotion in Dialog (P19-1)

Challenge:	Generalized Category Discovery (GCD) is a practical and challenging open-world task that aims to recognize both known and novel categories in unlabeled data using limited labeled data from known categories.
Approach:	They propose a framework for generalized category discovery that actively learns from diverse and collaborative feedback.
Outcome:	The proposed framework improves instance-level contrastive features, generates category descriptions, and aligns uncertain instances with LLM-selected category descriptions.

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Zhenqiao Song, Xiaoqing Zheng, Lu Liu, Mu Xu, Xuanjing Huang

ToXCL: A Unified Framework for Toxic Speech Detection and Explanation (2024.naacl-long)

Challenge:	EmoDS can express emotions in both ways, but it is difficult to scale to large datasets.
Approach:	They propose an emotional dialog system that can express emotions in both ways . they use strong emotional words and neutral words to increase the intensity of emotions .
Outcome:	The proposed system performs better than baselines in BLEU, diversity and quality of emotional expression.

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Nhat Hoang, Do Long, Duc Anh Do, Duc Anh Vu, Anh Tuan Luu

Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction (D18-1)

Challenge:	Existing models that focus on explicit toxic speech detection and explanation are prone to error propagation problems . et al., 2018) show that toxic speech models can be prone for generating errors .
Approach:	They propose a framework that can detect and explain toxic speech using a target group generator and an encoder-decoder model.
Outcome:	The proposed model outperforms baseline models and achieves state-of-the-art effectiveness . the proposed model generates a toxic explanation that matches the ground truth explanation .

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Yi Luan, Luheng He, Mari Ostendorf, Hannaneh Hajishirzi

Risk Minimization for Zero-shot Sequence Labeling (2021.acl-long)

Challenge:	Existing relation extraction systems are designed for within-sentence relations, but extracting information from scientific articles requires relations across sentences.
Approach:	They propose a multi-task setup for identifying entities, relations, and coreference clusters in scientific articles . they develop a unified framework called SciIE with shared span representations to solve this problem .
Outcome:	The proposed model outperforms existing models without domain-specific features in scientific information extraction.

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Zechuan Hu, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

Contract Discovery: Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive Baselines (2020.findings-emnlp)

Challenge:	Existing approaches to zero-shot sequence labeling are expensive and hard to obtain for lowresource languages/domains.
Approach:	They propose a framework for zero-shot sequence labeling with minimum risk training and a decomposable risk function that models the relations between predicted labels from the source models and the true labels.
Outcome:	The proposed framework outperforms state-of-the-art systems on 21 datasets.

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Łukasz Borchmann, Dawid Wisniewski, Andrzej Gretkowski, Izabela Kosmala, Dawid Jurkiewicz, Łukasz Szałkiewicz, Gabriela Pałka, Karol Kaczmarek, Agnieszka Kaliska, Filip Graliński

Curriculum Learning Meets Directed Acyclic Graph for Multimodal Emotion Recognition (2024.lrec-main)

Challenge:	Existing methods for detecting text fragments are not suitable for contract discovery, since it requires manual definition of a few examples, followed by conventional information.
Approach:	They propose a task where legal clauses are extracted from documents, given a few examples of similar clauses from other legal acts.
Outcome:	The proposed task differs substantially from conventional NLI and shared tasks on legal information extraction.

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Cam-Van Thi Nguyen, Cao-Bach Nguyen, Duc-Trong Le, Quang-Thuy Ha

Towards a Unified Framework for Reference Retrieval and Related Work Generation (2023.findings-emnlp)

Challenge:	Existing models for multimodal Emotion Recognition in conversation (ERC) use text as the main modality for emotion recognition.
Approach:	They propose a Directed Acyclic Graph (DAG) approach that integrates textual, acoustic, and visual features within a unified framework.
Outcome:	The proposed model outperforms baseline models on the IEMOCAP and MELD datasets.

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Zhengliang Shi, Shen Gao, Zhen Zhang, Xiuying Chen, Zhumin Chen, Pengjie Ren, Zhaochun Ren

UniEvent: Unified Generative Model with Multi-Dimensional Prefix for Zero-Shot Event-Relational Reasoning (2023.acl-long)

Challenge:	Existing methods for related work generation use human-annotated references as information sources.
Approach:	They propose a model which combines reference retrieval and related work generation processes in a unified framework based on the large language model.
Outcome:	The proposed model outperforms the state-of-the-art models on two wide-applied datasets.

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Zhengwei Tao, Zhi Jin, Haiyan Zhao, Chengfeng Dou, Yongqiang Zhao, Tao Shen, Chongyang Tao

Automatic Annotation of Direct Speech in Written French Narratives (2023.acl-long)

Challenge:	Reasoning about events and their relations is an indispensable ability to fulfill various event-centric or common-sense reasoning tasks.
Approach:	They propose a multi-task learning framework that organizes event relational reasoning tasks into a coordinate system with multiple axes, representing inter-event relations and reasoning formulations.
Outcome:	The proposed framework achieves state-of-the-art or competitive performance on zero-shot and supervised reasoning tasks.

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Noé Durandard, Viet Anh Tran, Gaspard Michel, Elena Epure

AutoDetect: Towards a Unified Framework for Automated Weakness Detection in Large Language Models (2024.findings-emnlp)

Challenge:	a new framework for AADS annotation in written text is needed for literary studies.
Approach:	They propose to use automatic annotation of direct speech (AADS) in written text to compare works by different authors . they adapted a large-to-date French narrative dataset annotated with DS per word .
Outcome:	The proposed framework is a step further to encourage more research on the topic.

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Jiale Cheng, Yida Lu, Xiaotao Gu, Pei Ke, Xiao Liu, Yuxiao Dong, Hongning Wang, Jie Tang, Minlie Huang

A Unified Framework for Synaesthesia Analysis (2023.findings-emnlp)

Challenge:	Large Language Models (LLMs) exhibit significant but subtle weaknesses, such as mistakes in instruction-following or coding tasks.
Approach:	They propose a framework to automatically expose weaknesses in Large Language Models (LLMs) they use three LLM-powered agents to perform comprehensive weakness identification .
Outcome:	The proposed framework shows that it is more effective than untargeted data augmentation methods like Self-Instruct to identify weaknesses in LLMs.

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Kun Sheng, Zhongqing Wang, Qingqing Zhao, Xiaotong Jiang, Guodong Zhou

Cognitive Modeling

Challenge:	Synaesthesia is a cognitive phenomenon structuring human thought and action, which makes understanding it challenging.
Approach:	They propose a framework for annotating synaesthetic elements and exploring their relationship . they propose to include sensory modalities, cues and stimuli in the framework .
Outcome:	The proposed framework yields state-of-the-art results, demonstrating its effectiveness.

A Unified Generative Framework for Bilingual Euphemism Detection and Identification (2024.findings-acl)

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Yuxue Hu, Junsong Li, Tongguan Wang, Dongyu Su, Guixin Su, Ying Sha

Graph Pre-training for AMR Parsing and Generation (2022.acl-long)

Challenge:	Existing euphemism datasets are only domain-specific or language-specific.
Approach:	They propose a unified model to jointly conduct bilingual euphemism detection and identification tasks.
Outcome:	The proposed model is effective and provides a new reference standard for euphemism detection and identification.

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Xuefeng Bai, Yulong Chen, Yue Zhang

PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity (2021.acl-long)

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.

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Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang

Challenge:	Existing personalized news recommendation methods have difficulties in making accurate recommendations to cold-start users.
Approach:	They propose to incorporate news popularity information to improve cold-start recommendations . they propose to use a popularity-aware user encoder to eliminate popularity bias .
Outcome:	The proposed method improves accuracy and diversity of personalized news recommendation on two real-world datasets.

MS2SL: Multimodal Spoken Data-Driven Continuous Sign Language Production (2024.findings-acl)

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Jian Ma, Wenguan Wang, Yi Yang, Feng Zheng

UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning (2022.acl-long)

Challenge:	Existing solutions for sign language production are limited due to phonological differences and data scarcity.
Approach:	They propose a unified framework for continuous sign language production that generates sign predictions step by step from text or speech embeddings.
Outcome:	The proposed model achieves competitive performance on how2sign and PHOENIX14T datasets.

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Yuning Mao, Lambert Mathias, Rui Hou, Amjad Almahairi, Hao Ma, Jiawei Han, Scott Yih, Madian Khabsa

DocNLI: A Large-scale Dataset for Document-level Natural Language Inference (2021.findings-acl)

Challenge:	Existing methods for parameter-efficient language model tuning (PELT) match the performance of fine-tuning with fewer trainable parameters.
Approach:	They propose a framework which integrates different PELT methods as submodules and learns to activate the ones that best suit the current data or task setup via gating mechanism.
Outcome:	The proposed framework outperforms fine-tuning methods on the GLUE benchmark and achieves 14% gains over the best individual PELT method.

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Wenpeng Yin, Dragomir Radev, Caiming Xiong

FiDeLiS: Faithful Reasoning in Large Language Models for Knowledge Graph Question Answering (2025.findings-acl)

Challenge:	Existing studies focus on sentence-level inference, which limits its application in downstream NLP problems.
Approach:	They propose to construct a large-scale dataset for document-level NLI that can be used to study NLP problems.
Outcome:	The proposed model performs well on popular sentence-level benchmarks and generalizes well to out-of-domain NLP tasks that rely on inference at document granularity.

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Yuan Sui, Yufei He, Nian Liu, Xiaoxin He, Kun Wang, Bryan Hooi

Alloc-MoE: Budget-Aware Expert Activation Allocation for Efficient Mixture-of-Experts Inference (2026.acl-long)

Challenge:	Existing retrieval-based or agent-based methods are prone to generating erroneous or hallucinated outputs.
Approach:	They propose a framework to leverage knowledge graphs as external knowledge sources to improve the factuality of LLM responses by anchoring answers to verifiable reasoning steps retrieved from KGs.
Outcome:	The proposed framework improves factuality and interpretability across benchmarks and reduces computational costs.

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Baihui Liu, Kaiyuan Tian, Wei Wang, Zhaoning Zhang, Linbo Qiao, Dongsheng Li

Green & Sustainable NLP

Challenge:	Existing approaches that reduce expert activations lead to severe model performance degradation.
Approach:	They propose a framework that optimizes budget allocation coordinately at layer and token levels to minimize model performance degradation.
Outcome:	The proposed framework achieves 1.15 prefill and 1.34 decode speedups on DeepSeek-V2-Lite at half of the original budget.

Causal-LLM: A Unified One-Shot Framework for Prompt- and Data-Driven Causal Graph Discovery (2025.findings-emnlp)

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Amartya Roy, N Devharish, Shreya Ganguly, Kripabandhu Ghosh

AdaJudge: Adaptive Multi-Perspective Judging for Reward Modeling (2026.acl-long)

Challenge:	Current causal discovery methods rely on pairwise or iterative strategies that fail to capture global dependencies, amplify local biases, and reduce overall accuracy.
Approach:	They propose a framework for one-step full causal graph discovery using prompt-based discovery and a data-driven method for settings without metadata.
Outcome:	The proposed framework outperforms state-of-the-art models by approximately 40% in edge accuracy on datasets like Asia and Sachs while maintaining strong performance on more complex graphs.

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Yongliang Miao, Yangyang Liang, Mengnan Du

Challenge:	Existing reward models rely on a static pooling strategy to condense sequences into scalar scores, which is ill-suited for fine-grained discrimination.
Approach:	They propose a framework that jointly adapts representation and aggregation to address these limitations by integrating a static inductive bias with a representational mismatch.
Outcome:	Experiments on RM-Bench and JudgeBench show that AdaJudge outperforms strong off-the-shelf reward models and traditional pooling baselines.

Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework (2023.findings-acl)

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Lifan Yuan, YiChi Zhang, Yangyi Chen, Wei Wei

A Unified Generative Framework for Various NER Subtasks (2021.acl-long)

Challenge:	Existing methods for adversarial samples are poorly applied in computer vision . however, textual adversarials are still vulnerable to small perturbations .
Approach:	They propose a framework to extend existing adversarial attack methods to textual adversarials by adding optimized perturbations to embedding layer and amplifying them in forward propagation process.
Outcome:	The proposed framework achieves better performance even using proxy gradient information and produces more fluent and grammatical adversarial samples compared to baseline methods.

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Hang Yan, Tao Gui, Junqi Dai, Qipeng Guo, Zheng Zhang, Xipeng Qiu

Jointly Extracting Explicit and Implicit Relational Triples with Reasoning Pattern Enhanced Binary Pointer Network (2021.naacl-main)

Challenge:	Named Entity Recognition (NER) is the task of identifying spans that represent entities in sentences.
Approach:	They propose to formulate NER subtasks as entity span sequence generation task . framework can be used to solve all three kinds of NER tasks without tagging schema .
Outcome:	The proposed framework achieves state-of-the-art (SoTA) or near SoTA performance on eight English NER datasets.

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Yubo Chen, Yunqi Zhang, Changran Hu, Yongfeng Huang

ACIArena: Toward Unified Evaluation for Agent Cascading Injection (2026.acl-long)

Challenge:	Existing methods for relational triple extraction ignore implicit triples that lack explicit expressions, leading to incomplete knowledge graphs.
Approach:	They propose a binary pointer network to extract explicit and implicit relational triples from sentences and to retain the information of extracted triples in an external memory.
Outcome:	The proposed framework extracts overlapping triples relevant to each word sequentially and retains the information of extracted triples in an external memory.

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Hengyu An, Minxi Li, Jinghuai Zhang, Naen Xu, Chunyi Zhou, Changjiang Li, Xiaogang Xu, Tianyu Du, Shouling Ji

Position Offset Label Prediction for Grammatical Error Correction (2022.coling-1)

Challenge:	Existing studies consider only limited attack strategies and simplified MAS settings, limiting their generalizability and comprehensive evaluation.
Approach:	They propose a framework to evaluate the robustness of Multi-Agent Systems (MAS) they propose unified evaluation suites spanning attack surfaces and attack objectives .
Outcome:	ACIArena provides a benchmark of 1,356 test cases for evaluating MAS robustness . it covers six widely used MAS implementations and provides measurable results .

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Xiuyu Wu, Jingsong Yu, Xu Sun, Yunfang Wu

Syntactic Text Processing

Challenge:	Experimental results show that our proposed POL-Pc framework improves baseline models and yields consistent performance gain over various data augmentation methods.
Approach:	They propose a position offset label prediction subtask to integrate correction editing operations into a unified framework.
Outcome:	The proposed model outperforms baseline models on Chinese, English and Japanese datasets by a wide margin.

InstructDiff: Domain-Adaptive Data Selection via Contrastive Entropy for Efficient LLM Fine-Tuning (2026.acl-long)

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Junyou Su, He Zhu, Xiao Luo, Liyu Zhang, Hong-Yu Zhou, Yun Chen, Peng Li, Yang Liu, Guanhua Chen

Model-Agnostic Meta Learning for Class Imbalance Adaptation (2026.findings-acl)

Challenge:	Existing data selection methods suffer from severe domain specificity . existing methods for general instruction-following fail on reasoning tasks .
Approach:	They propose a framework that operationalizes contrastive entropy as a domain-adaptive selection criterion through warmup calibration, bi-directional NLL filtering, and entropic-based ranking.
Outcome:	Experiments show that InstructDiff outperforms baseline training on reasoning tasks while using only 10% of the data.

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Hanshu Rao, Guangzeng Han, Xiaolei Huang

A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining (P19-1)

Challenge:	Existing approaches to address class imbalance and data difficulty have been used to train models.
Approach:	They propose a framework that prioritizes challenging samples and minority classes over hard examples and their semantically similar neighbors to address class imbalance.
Outcome:	The proposed framework outperforms baselines on six imbalanced datasets and achieves substantial improvements for minority classes.

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Shweta Yadav, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya

A Unified MRC Framework for Named Entity Recognition (2020.acl-main)

Challenge:	Existing methods of identifying ADRs are reliable but time-consuming and offer a limited amount of ADR relevant information.
Approach:	They propose a neural network-inspired multi-task learning framework that can simultaneously extract ADRs from various sources.
Outcome:	The proposed framework achieves state-of-the-art performance on three publicly available real-world benchmark pharmacovigilance datasets, a Twitter dataset from PSB 2016 Social Me- dia Shared Task, CADEC corpus and Medline ADR corpus.

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Xiaoya Li, Jingrong Feng, Yuxian Meng, Qinghong Han, Fei Wu, Jiwei Li

CoBA: Counterbias Text Augmentation for Mitigating Various Spurious Correlations via Semantic Triples (2025.emnlp-main)

Challenge:	Named entity recognition is divided into nested NER and flat NER depending on whether entities are nesting.
Approach:	They propose to formulate named entity recognition task as machine reading comprehension task instead of sequence labeling problem .
Outcome:	The proposed framework achieves vast amount of performance boost over current models on nested and flat NER datasets.

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Kyohoon Jin, Juhwan Choi, JungMin Yun, Junho Lee, Soojin Jang, YoungBin Kim

SpeechT-RAG: Reliable Depression Detection in LLMs with Retrieval-Augmented Generation Using Speech Timing Information (2025.findings-acl)

Challenge:	Spurious correlations are patterns that appear in datasets but do not represent genuine relationships.
Approach:	They propose a more general form of counterfactual data augmentation that tackles multiple biases . they propose 'CoBA' that decomposes text into subject-predicate-object triples and modifies them to disrupt spurious correlations.
Outcome:	The proposed framework reduces biases and strengthens out-of-distribution resilience.

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Xiangyu Zhang, Hexin Liu, Qiquan Zhang, Beena Ahmed, Julien Epps

SPECTRA: Sparse Structured Text Rationalization (2021.emnlp-main)

Challenge:	Large Language Models (LLMs) have been extensively utilized for health-related tasks, yet their performance in depression detection remains limited when relying solely on text input.
Approach:	They propose a system that leverages speech timing features for depression detection and reliable confidence estimation.
Outcome:	The proposed system outperforms text-based RAG systems in depression detection and confidence estimation.

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Nuno M. Guerreiro, André F. T. Martins

QuestEval: Summarization Asks for Fact-based Evaluation (2021.emnlp-main)

Challenge:	Sparse attention mechanisms are a deterministic alternative, but they lack a way to regularize rationale extraction.
Approach:	They propose a framework for deterministic extraction of structured explanations via constrained inference on a factor graph, forming a differentiable layer.
Outcome:	The proposed framework outperforms previous studies on performance and plausibility of extracted rationales.

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Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano, Alex Wang, Patrick Gallinari

Summarization

Challenge:	Existing evaluation metrics for summarization evaluation are limited and do not correlate well with human judgments.
Approach:	They propose to extend existing evaluation metrics to include question answering models to assess whether a summary contains all relevant information in its source document.
Outcome:	The proposed framework significantly improves the correlation with human judgments over four evaluation dimensions.

Evaluating Input Feature Explanations through a Unified Diagnostic Evaluation Framework (2025.naacl-long)

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Jingyi Sun, Pepa Atanasova, Isabelle Augenstein

More than Classification: A Unified Framework for Event Temporal Relation Extraction (2023.acl-long)

Challenge:	Input feature explanations reveal how a model makes decisions based on a specific input.
Approach:	They propose a framework that facilitates an automated comparison between highlight and interactive explanations comprised of four diagnostic properties.
Outcome:	The proposed framework compares highlight and interactive explanations across two datasets and two models and shows that interactive span explanations outperform other explanation types across most diagnostic properties.

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Quzhe Huang, Yutong Hu, Shengqi Zhu, Yansong Feng, Chang Liu, Dongyan Zhao

Why Do Emotions Change? Appraisal-Guided Reasoning for Emotion–Cause Triplet Extraction in Conversations (2026.acl-long)

Challenge:	Existing methods for event temporal relation extraction ignore meaning of relations and wipe out their intrinsic dependency.
Approach:	They propose a unified event temporal relation extraction framework that transforms temporal relations into logical expressions of time points and completes the ETRE by predicting the relations between certain time points.
Outcome:	The proposed framework outperforms the state-of-the-art model on TB-Dense and MATRES by 0.3% on both datasets.

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Qiao Liang, Ying Shen, Yao Liu, Tiantian Chen, Lin Zhang

DesignCLIP: Multimodal Learning with CLIP for Design Patent Understanding (2025.findings-emnlp)

Challenge:	Existing methods for multi-turn, multi-speaker multimodal affect understanding are difficult to maintain conversation-level consistency under within-speaks' emotion shifts.
Approach:	They propose a framework that combines appraisal-guided structured generation with graph-structured reinforcement learning to extract triplets from multi-turn multimodal conversations.
Outcome:	The proposed framework outperforms baselines on public MECTEC benchmarks and improves structure-aware metrics on emotion shift coherence and core events.

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Zhu Wang, Homaira Huda Shomee, Sathya N. Ravi, Sourav Medya

UniTE: Unified Translation Evaluation (2022.acl-long)

Challenge:	patent images often lack comprehensive visual context and semantic information, authors say . recent advances in vision-language models offer promising opportunities for patent analysis .
Approach:	They develop a framework for design patent analysis using large-scale patent dataset . they validate the effectiveness of DesignCLIP across various downstream tasks .
Outcome:	The proposed framework outperforms baseline and SOTA models on all tasks.

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Yu Wan, Dayiheng Liu, Baosong Yang, Haibo Zhang, Boxing Chen, Derek Wong, Lidia Chao

Capability Decomposition for Unified Information Extraction via Hierarchical Mixture-of-Experts (2026.acl-long)

Challenge:	Recent methods for evaluation of translation quality are focused on one task, ignoring commonalities .
Approach:	They propose a unified framework engaged with abilities to handle all three evaluation tasks.
Outcome:	The proposed framework can universally surpass state-of-the-art or winner methods across tasks.

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Jing Zhou, Peng Wang, Wenjun Ke, Jiajun Liu, Yao He

Contextual Augmentation for Entity Linking using Large Language Models (2025.coling-main)

Challenge:	Existing methods for IE tasks suffer from inconsistent schema representation and implicitly intermediate reasoning . UC-UIE adopts a low-rank adapted hierarchical Mixture-of-Experts adapter for UIE tasks .
Approach:	They propose a framework that decomposes IE reasoning into three universal capabilities . UC-UIE adopts a low-rank Adaptation adapter to fine-tune LLMs for IE tasks .
Outcome:	The proposed framework outperforms full-parameter tuning methods with 1.24% trainable parameters and outperformed existing methods in generalization and interpretability.

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Daniel Vollmers, Hamada Zahera, Diego Moussallem, Axel-Cyrille Ngonga Ngomo

When Generative Adversarial Networks Meet Sequence Labeling Challenges (2024.emnlp-main)

Challenge:	Entity Linking involves detecting and linking entity mentions in natural language texts to a knowledge graph.
Approach:	They propose a fine-tuned model that integrates entity recognition and disambiguation in a unified framework.
Outcome:	The proposed model achieves state-of-the-art on out-of domain datasets and compares with baselines.

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Yu Tong, Ge Chen, Guokai Zheng, Rui Li, Jiang Dazhi

UECA-Prompt: Universal Prompt for Emotion Cause Analysis (2022.coling-1)

Challenge:	Existing approaches for sequence labeling use a feature extractor and sequence tagger . a recent study shows that SLGAN is versatile and highly effective .
Approach:	They propose a framework that harnesses the capabilities of Generative Adversarial Networks to address sequence labeling challenges.
Outcome:	The proposed framework exhibits strong adaptability to various sequence labeling tasks.

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Xiaopeng Zheng, Zhiyue Liu, Zizhen Zhang, Zhaoyang Wang, Jiahai Wang

Few-shot Event Detection: An Empirical Study and a Unified View (2023.acl-long)

Challenge:	Existing methods adopt fine-tuning paradigm to solve certain types of ECA tasks. Existing models suffer from dataset bias.
Approach:	They propose a universal prompt tuning method to solve different ECA tasks in a unified framework and a sequential learning module to ease the dataset bias.
Outcome:	The proposed method achieves competitive performance on the ECA datasets.

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Yubo Ma, Zehao Wang, Yixin Cao, Aixin Sun

Do What? Teaching Vision-Language-Action Models to Reject the Impossible (2025.findings-emnlp)

Challenge:	Extensive studies have been carried out on fewshot event detection (ED) however, there are noticeable discrepancies among existing methods from three aspects.
Approach:	They propose a unified view of ED models and a better unified baseline for fair evaluation.
Outcome:	The proposed framework outperforms existing methods by a large margin on three datasets.

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Wen-Han Hsieh, Elvis Hsieh, Dantong Niu, Trevor Darrell, Roei Herzig, David M. Chan

On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey (2024.findings-acl)

Challenge:	Recent studies show that VLAs can recognize, interpret, and respond to false-premise instructions.
Approach:	They propose a framework that detects when an instruction cannot be executed due to a false premise and engages in language-based clarification or correction.
Outcome:	The proposed framework detects when an instruction cannot be executed due to a false premise and engages in language-based clarification or correction.

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Lin Long, Rui Wang, Ruixuan Xiao, Junbo Zhao, Xiao Ding, Gang Chen, Haobo Wang

Challenge:	Large Language Models (LLMs) provide a data-centric solution to alleviate limitations of real-world data with synthetic data generation.
Approach:	They propose a generic workflow for LLM-driven synthetic data generation.
Outcome:	The proposed workflows highlight gaps in existing research and outline avenues for future studies.

AICA-Bench: Holistically Examining the Capabilities of VLMs in Affective Image Content Analysis (2026.findings-acl)

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Dong She, Xianrong Yao, Liqun Chen, Jinghe Yu, Yang Gao, Zhanpeng Jin

Sentiment Analysis

Challenge:	Recent studies have focused on factual correctness, semantic grounding, visual reasoning, or multimodal large language models.
Approach:	They propose a benchmark to assess AICA, which integrates perception, reasoning, and generation into a unified framework.
Outcome:	The proposed framework corrects intensity errors and significantly enhances descriptive depth.

Few-shot domain adaptation for named-entity recognition via joint constrained k-means and subspace selection (2025.coling-main)

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Ayoub Hammal, Benno Uthayasooriyar, Caio Corro

AGTAO: Robust and Stabilized LLM Unlearning via Adversarial Gating Training with Adaptive Orthogonality (2026.findings-acl)

Challenge:	Named-entity recognition (NER) requires large annotated datasets, which limits its applicability across domains with varying entity definitions.
Approach:	They propose a weakly-supervised algorithm that combines small labeled datasets with large amounts of unlabeled data.
Outcome:	The proposed approach achieves state-of-the-art results in few-shot NER . it combines label supervision, cluster size constraints, and domain-specific discriminative subspace selection.

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Pengyu Li, Lingling Zhang, Zhitao Gao, Yanrui Wu, Yuxuan Dong, Huan Liu, Bifan Wei, Jun Liu

Grammar-Constrained Decoding for Structured NLP Tasks without Finetuning (2023.emnlp-main)

Challenge:	Large Language Models (LLMs) unintentionally memorize sensitive data, posing privacy and security risks.
Approach:	They propose a framework that reconciles unlearning efficacy and utility preservation by using a latent-space gating mechanism to simulate internal recovery attempts.
Outcome:	The proposed framework achieves superior trade-off between unlearning efficacy and model utility.

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Saibo Geng, Martin Josifoski, Maxime Peyrard, Robert West

RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers (2020.acl-main)

Challenge:	Existing grammar-constrained decoding methods are limited to specific tasks . a grammar constraint is used to control the generation of LMs, but it is limited to a few tasks a task is not performed.
Approach:	They propose grammar-constrained decoding to control the generation of large language models . they demonstrate that grammars can describe the output space for a wider range of tasks .
Outcome:	The proposed grammars outperform unconstrained models on information extraction, entity disambiguation, and constituency parsing.

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Bailin Wang, Richard Shin, Xiaodong Liu, Oleksandr Polozov, Matthew Richardson

Programming Languages in NLP

Challenge:	Existing semantic parsing models struggle to generalize to unseen database schemas.
Approach:	They propose a framework to address schema encoding, schema linking, and feature representation within a text-to-SQL encoder.
Outcome:	The proposed framework boosts the match accuracy to 57.2% on the spider dataset, surpassing its best counterparts by 8.7%.

CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation (2021.emnlp-main)

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Yue Wang, Weishi Wang, Shafiq Joty, Steven C.H. Hoi

Dynamic Stochastic Decoding Strategy for Open-Domain Dialogue Generation (2024.findings-acl)

Challenge:	Pre-trained models for Natural Languages (NL) like BERT and GPT have been shown to transfer well to Programming Languages.
Approach:	They propose a unified pre-trained encoder-decoder Transformer model that leverages the code semantics conveyed from the developer-assigned identifiers.
Outcome:	The proposed model outperforms existing models on understanding and generation tasks and can capture semantic information from code.

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Yiwei Li, Fei Mi, Yitong Li, Yasheng Wang, Bin Sun, Shaoxiong Feng, Kan Li

UniRAG: Unified Query Understanding Method for Retrieval Augmented Generation (2025.acl-long)

Challenge:	Stochastic sampling strategies are not widely used in open-domain dialogue systems.
Approach:	They propose a dynamic decoding strategy which can adjust the decoding space w.r.t. different contexts.
Outcome:	The proposed decoding strategy can improve the performance of pre-trained models when coupled with four well-used stochastic decoding algorithms.

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Rui Li, Liyang He, Qi Liu, Zheng Zhang, Heng Yu, Yuyang Ye, Linbo Zhu, Yu Su

One Agent to Serve All: a Lite-Adaptive Stylized AI Assistant for Millions of Multi-Style Official Accounts (2026.findings-acl)

Challenge:	Existing query augmentation methods face knowledge update lag and hallucinations in large language models (LLMs) Existing methods face two key challenges: (1) separation of query augmented and encoding tasks, which hinders information sharing and introduces cumulative errors; (2) difficulty of selecting optimal augmentation strategy for different scenarios.
Approach:	They propose a unified framework for query understanding in RAG that integrates internal and external knowledge to enhance query augmentation and encoding tasks.
Outcome:	The proposed framework outperforms traditional query augmentation methods in five knowledge-intensive benchmark tasks in both closed and open domain question answering.

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Xingyu Fan, Li Feifei, Que Wenhui, Hailong Li

Is Multi-Hop Reasoning Really Explainable? Towards Benchmarking Reasoning Interpretability (2021.emnlp-main)

Challenge:	Existing methods for prompting in official accounts are computationally prohibitive and lack contextually grounded responses.
Approach:	They propose a lite-adaptive framework for stylized contextual question answering that scales to millions of official accounts.
Outcome:	The proposed framework can serve large volumes of official accounts with minimal overhead while maintaining stylistic diversity.

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Xin Lv, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Yichi Zhang, Zelin Dai

Implicit Personalization in Language Models: A Systematic Study (2024.findings-emnlp)

Challenge:	Existing models for multi-hop reasoning are not able to evaluate their interpretability . a recent study found that many paths are unreasonable .
Approach:	They propose a framework to evaluate the interpretability of multi-hop reasoning models . they annotate all possible rules and establish a benchmark .
Outcome:	The proposed framework outperforms existing models in terms of performance and interpretability.

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Zhijing Jin, Nils Heil, Jiarui Liu, Shehzaad Dhuliawala, Yahang Qi, Bernhard Schölkopf, Rada Mihalcea, Mrinmaya Sachan

TAGPRIME: A Unified Framework for Relational Structure Extraction (2023.acl-long)

Challenge:	Existing studies have focused on the implicit personalization problem, but no unified framework exists to study it.
Approach:	They propose a mathematical formulation and a moral reasoning framework to study the phenomenon of Implicit Personalization (IP) they propose 'direct intervention' to estimate causal effect of mediator variable that cannot be directly intervened upon.
Outcome:	The proposed method estimates the causal effect of a mediator variable that cannot be directly intervened upon.

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I-Hung Hsu, Kuan-Hao Huang, Shuning Zhang, Wenxin Cheng, Prem Natarajan, Kai-Wei Chang, Nanyun Peng

TVQA+: Spatio-Temporal Grounding for Video Question Answering (2020.acl-main)

Challenge:	Existing models for natural language processing (NLP) do not address common tasks.
Approach:	They propose to take a unified view of all the tasks and introduce a model that appends priming words about the condition to the input text.
Outcome:	The proposed model is based on ten datasets across five different languages and covers ten tasks that cover ten languages.

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Jie Lei, Licheng Yu, Tamara Berg, Mohit Bansal

Visual Data in NLP

Challenge:	Existing video QA datasets only contain QA pairs without labels for key clips or regions needed to answer the question.
Approach:	They propose a framework that grounds evidence in both spatial and temporal domains to answer questions about videos using bounding boxes.
Outcome:	The proposed framework can produce interpretable spatio-temporal attention visualizations.

CoLA: A Choice Leakage Attack Framework to Expose Privacy Risks in Subset Training (2026.acl-long)

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Qi Li, Cheng-Long Wang, Yinzhi Cao, Di Wang

LAVa: Layer-wise KV Cache Eviction with Dynamic Budget Allocation (2025.findings-emnlp)

Challenge:	Existing threat models underestimate subset-training privacy risks because of the scale of modern datasets.
Approach:	They propose a unified framework for analyzing privacy leakage in subset selection based on side-channel metadata from the subset process or via the outputs of the target model.
Outcome:	The proposed framework analyzes privacy leakage in subset selection based on two different scenarios .

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Yiqun Shen, Song Yuan, Zhengze Zhang, Xiaoliang Wang, Daxin Jiang, Nguyen Cam-Tu

Challenge:	Existing methods for cache compression are heuristic and lack dynamic budget allocation . cnn's john mccartney and johnny mccain present a new approach for cache eviction and dynamic budgets .
Approach:	They propose a unified framework for cache compression that minimizes information loss in transformer residual streams.
Outcome:	The proposed method consistently maintains top performance across task types.

PPC-GPT: Federated Task-Specific Compression of Large Language Models via Pruning and Chain-of-Thought Distillation (2025.emnlp-main)

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Tao Fan, Guoqiang Ma, Yuanfeng Song, Lixin Fan, Qiang Yang

OpenFactCheck: Building, Benchmarking Customized Fact-Checking Systems and Evaluating the Factuality of Claims and LLMs (2025.coling-main)

Challenge:	Large Language Models (LLMs) are becoming more popular and are gaining widespread use in artificial intelligence.
Approach:	They propose a unified framework that addresses both privacy preservation and model compression in federated settings.
Outcome:	The proposed framework maintains competitive performance comparable to full-sized LLMs while ensuring robust privacy protection through its federated architecture.

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Yuxia Wang, Minghan Wang, Hasan Iqbal, Georgi N. Georgiev, Jiahui Geng, Iryna Gurevych, Preslav Nakov

Holistic Prediction on a Time-Evolving Attributed Graph (2023.acl-long)

Challenge:	Large language models (LLMs) generate naturallysounding answers over a broad range of human inquiries, but they still produce content that deviates from real-world facts.
Approach:	They propose a framework for building customized automatic fact-checking systems, benchmarking their accuracy, evaluating factuality of LLMs, and verifying claims in a document.
Outcome:	The proposed framework assesses the factuality of free-form responses in open domains and evaluates factually of LLMs.

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Shohei Yamasaki, Yuya Sasaki, Panagiotis Karras, Makoto Onizuka

Jointly Learning to Repair Code and Generate Commit Message (2021.emnlp-main)

Challenge:	Existing methods for predicting future links, nodes, and attributes of time-evolving attributed graphs are not accurate.
Approach:	They propose a framework that predicts node attributes and topology changes such as appearance and disappearance of links and the emergence and loss of nodes.
Outcome:	The proposed framework improves on existing methods that assume that each link, node, and attribute prediction is independent and fails to predict new nodes that were not observed in the past.

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Jiaqi Bai, Long Zhou, Ambrosio Blanco, Shujie Liu, Furu Wei, Ming Zhou, Zhoujun Li

RankCSE: Unsupervised Sentence Representations Learning via Learning to Rank (2023.acl-long)

Challenge:	Existing work performs code repair and commit message generation independently.
Approach:	They propose a cascaded method to repair program codes and generate commit messages in a unified framework.
Outcome:	The proposed model significantly outperforms baselines on a buggy-fixed-commit dataset.

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Jiduan Liu, Jiahao Liu, Qifan Wang, Jingang Wang, Wei Wu, Yunsen Xian, Dongyan Zhao, Kai Chen, Rui Yan

Balancing Fidelity and Plasticity: Aligning Mixed-Precision Fine-Tuning with Linguistic Hierarchies (2026.findings-acl)

Challenge:	Unsupervised sentence representation learning is one of the fundamental problems in natural language processing . contrastive learning methods fail to capture fine-grained ranking information among the sentences .
Approach:	They propose a novel approach for unsupervised sentence representation learning that integrates ranking consistency and ranking distillation with contrastive learning into a unified framework.
Outcome:	The proposed approach performs better over state-of-the-art models on STS and TR tasks.

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Changhai Zhou, Shiyang Zhang, Yuhua Zhou, Jun Gao, Qian Qiao, Shichao Weng, Weizhong Zhang, Cheng Jin

PMPO: Probabilistic Metric Prompt Optimization for Small and Large Language Models (2025.findings-emnlp)

Challenge:	Existing quantization-aware fine-tuning methods decouple weight precision and adapter capacity, overlooking that a layer’s ability to adapt is constrained by the information preserved in its frozen weights.
Approach:	They propose a framework that jointly optimizes per-layer quantization bit-width and LoRA rank.
Outcome:	Experiments on LLaMA and Qwen models show that the proposed framework matches or approaches 16-bit baselines while using substantially less memory.

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ChenZhuo Zhao, Ziqian Liu, Xinda Wang, Junting Lu, Chaoyi Ruan

OmniDiagram: Advancing Unified Diagram Code Generation via Visual Interrogation Reward (2026.findings-acl)

Challenge:	Existing methods evaluate candidate prompts by sampling full outputs, often coupled with self critique or human annotated preferences, which limits scalability, especially for smaller models or models that are not instruction tuned.
Approach:	They propose a framework that uses token level cross entropy as a direct, lightweight evaluation signal to evaluate candidate prompts.
Outcome:	The proposed framework outperforms prior prompt optimizers across model sizes and datasets.

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Haoyue Yang, Xuanle Zhao, Xuexin Liu, Feibing Jiang, Yao Zhu

RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation (2025.acl-long)

Challenge:	Existing studies on programmable diagram generation focus on a narrow set of tasks and languages.
Approach:	They propose a unified framework that integrates diverse diagram code languages and task definitions.
Outcome:	The proposed framework can bridge complex visual information with executable code across diverse tasks and languages.

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Xiaoxi Li, Jiajie Jin, Yujia Zhou, Yongkang Wu, Zhonghua Li, Ye Qi, Zhicheng Dou

Agent-GWO: Collaborative Agents for Dynamic Prompt Optimization in Large Language Models (2026.findings-acl)

Challenge:	Existing methods rely on separate retrievers to fetch top-k text chunks for generating evidence, and they lack joint optimization.
Approach:	They propose a framework that integrates retrieval and generation into a single, auto-regressive process, enabling LLMs to directly generate fine-grained evidence from the corpus with constrained decoding.
Outcome:	Extensive experiments on five open-domain QA datasets demonstrate the proposed framework’s superior performance across both in-domain and out-of-domain tasks.

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Xudong Wang, Chaoning Zhang, Chenghao Li, Shuxu Chen, Qigan Sun, Jiaquan Zhang, Fachrina Dewi Puspitasari, Tae-Ho Kim, Jiwei Wei, Malu Zhang, Guoqing Wang, Yang Yang, Heng Tao Shen

A Survey on Multi-modal Intent Recognition: Recent Advances and New Frontiers (2025.findings-emnlp)

Challenge:	Existing automatic prompt optimization methods fail to optimize prompts and decoding hyperparameters within a unified framework to achieve stable global improvements.
Approach:	They propose a dynamic prompt optimization framework for complex reasoning that unifies prompt templates and decodes hyperparameters as inheritable agent configurations.
Outcome:	Experiments on multiple mathematical and hybrid reasoning benchmarks show that Agent-GWO improves accuracy and stability over existing prompt optimization methods.

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Zhihong Zhu, Fan Zhang, Yunyan Zhang, Jinghan Sun, Zhiqi Huang, Qingqing Long, Bowen Xing, Xian Wu

HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigation (2025.findings-acl)

Challenge:	Multi-modal intent recognition (MIR) requires integrating non-verbal cues from real-world contexts to enhance human intention understanding.
Approach:	They present a comprehensive review of multi-modal intent recognition . they provide a survey of the field covering textual, visual, and acoustic signals .
Outcome:	The present survey summarises the current state of multi-modal intent recognition . it includes a comprehensive taxonomy and advanced methods .

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Naquee Rizwan, Seid Muhie Yimam, Daryna Dementieva, Dr. Florian Skupin, Tim Fischer, Daniil Moskovskiy, Aarushi Ajay Borkar, Robert Geislinger, Punyajoy Saha, Sarthak Roy, Martin Semmann, Alexander Panchenko, Chris Biemann, Animesh Mukherjee

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents (2026.acl-long)

Challenge:	Existing approaches to manage hate speech rely on reactive measures such as blocking or suspending offensive messages . despite regulations imposed by nations and social media platforms, hateful content remains a challenge .
Approach:	They propose a framework for automated hate speech moderation based on different strategies . they examine hate speech regulations and strategies from three perspectives .
Outcome:	The proposed framework could be based on a combination of country regulations, social platform policies, and NLP research datasets.

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Lei Ding, Bin He, Chenguang Wang, Yang Liu

TellWhisper: Tell Whisper Who Speaks When (2026.acl-long)

Challenge:	Existing approaches to measuring and optimizing proactive task-oriented agents lack generalizable end-to-end solutions.
Approach:	They propose a framework for conversational task scheduling that integrates proactiveness reinforcement learning with a domain-agnostic annotation methodology.
Outcome:	The proposed framework enables scalable proactiveness reinforcement learning (RL) Experiments on two newly auto-annotated datasets demonstrate significant improvements in proactive timing while maintaining action consistency comparable to state-of-the-art baselines.

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Yifan Hu, Peiji Yang, Zhisheng Wang, Yicheng Zhong, Rui Liu

TLSA: LLM-Guided Text-Label Space Alignment with Contrastive Learning for Generalized Category Discovery (2026.acl-long)

Challenge:	Existing approaches decouple temporal modeling and speaker modeling when addressing 'when' and 'who' . a new framework that couples temporal structure with speaker dynamics is proposed to address these limitations .
Approach:	They propose a framework that couples temporal and speaker identity within the speech encoder . they propose TS-RoPE, a time-speaker rotary positional encoding that partitions Query/Key channels into temporal, speaker subspaces and applies region-specific rotations to align "when" and "who" cues in selfattention.
Outcome:	The proposed framework couples temporal structure with speaker dynamics in speech encoder . it uses frame-level speaker activity to estimate speaker-activity estimates .

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Wenxi Xu, Chuan Qin, Xi Chen, Chuyu Fang, Yuanchun Zhou, Hengshu Zhu

Knowledge Poisoning Attacks on Medical Multi-Modal Retrieval-Augmented Generation (2026.acl-long)

Challenge:	Existing methods for generalized category discovery suffer from weak text–label alignment, inconsistent objectives across known and novel categories, and poor discrimination of semantically similar clusters.
Approach:	They propose a unified framework that enforces contrastive alignment between text and label representations within a shared semantic space.
Outcome:	The proposed framework outperforms state-of-the-art methods on four benchmark datasets.

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Peiru Yang, Haoran Zheng, Tong Ju, Shiting Wang, Wanchun Ni, Jiajun Liu, Shangguang Wang, Yongfeng Huang, Tao Qi

A Comprehensive Evaluation of Biomedical Entity Linking Models (2023.emnlp-main)

Challenge:	Existing studies have investigated knowledge poisoning attacks in medical RAG systems . knowledge poison attacks can disrupt model outputs and undermine system reliability .
Approach:	They propose a knowledge poisoning framework that injects misinformation into textual data . they propose to use paired visual data as a query-agnostic trigger to promote retrieval .
Outcome:	The proposed framework produces clinically plausible but incorrect generations on five LLMs and datasets.

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David Kartchner, Jennifer Deng, Shubham Lohiya, Tejasri Kopparthi, Prasanth Bathala, Daniel Domingo-Fernández, Cassie Mitchell

Fine-Mem: Fine-Grained Feedback Alignment for Long-Horizon Memory Management (2026.acl-long)

Challenge:	Current methods struggle to correctly link genes and proteins and often have difficulty incorporating context into linking decisions.
Approach:	They evaluate nine recent state-of-the-art biomedical entity linking models under a unified framework.
Outcome:	The proposed models are compared along axes of accuracy, speed, ease of use, generalization, adaptability and adaptability to new ontologies and datasets.

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Weitao Ma, Xiaocheng Feng, Lei Huang, Xiachong Feng, Zhanyu Ma, Jun Xu, Jiuchong Gao, Jinghua Hao, Renqing He, Bing Qin

FSUIE: A Novel Fuzzy Span Mechanism for Universal Information Extraction (2023.acl-long)

Challenge:	Existing approaches to memory management rely on final task performance as the primary reward, resulting in severe reward sparsity and ineffective credit assignment.
Approach:	They propose a framework for fine-grained feedback alignment using a Chunk-level step reward and Evidence-Anchored Reward Attribution to redistribute global rewards based on memory items utilized as evidence in reasoning.
Outcome:	The proposed framework outperforms baselines and supports generalization across different model configurations and backbones.

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Tianshuo Peng, Zuchao Li, Lefei Zhang, Bo Du, Hai Zhao

Towards A Unified View of Sparse Feed-Forward Network in Pretraining Large Language Model (2023.emnlp-main)

Challenge:	Existing Universal Information Extraction models rely heavily on span boundaries in data during training, which does not reflect the reality of span annotation challenges.
Approach:	They propose a framework that uses fuzzy spans to model various IE tasks . they propose generative Universal Information Extraction (UIE) to unify various ie tasks based on fuzzy span boundaries .
Outcome:	The proposed framework improves on a series of main IE tasks with small amounts of data and training epochs.

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Zeyu Liu, Tim Dettmers, Xi Lin, Veselin Stoyanov, Xian Li

Safe-SAIL: Towards a Fine-grained Safety Landscape of Large Language Models via Sparse Autoencoder Interpretation Framework (2026.findings-acl)

Challenge:	Large and sparse feed-forward layers (S-FFN) have proven effective in scaling up the model size for pretraining large language models.
Approach:	They compare S-FFN architectures for language modeling and compare their performance and efficiency . they found a simpler selection method that selects blocks through their mean aggregated hidden states .
Outcome:	The proposed model size and selection method achieve lower perplexity in language model pretraining compared to existing MoE architectures.

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Jiaqi Weng, Han Zheng, Hanyu Zhang, Ej Zhou, Qinqin He, Jialing Tao, Hui Xue, Zhixuan Chu, Xiting Wang

Linear Steerability in Language Models: When It Emerges and How It Evolves (2025.findings-emnlp)

Challenge:	Existing studies on how SAEs derive most fine-grained latent features for safety remain unexplored.
Approach:	They propose a framework for interpreting SAE features in safety-critical domains . they train a suite of SAEs with human-readable explanations and systematic evaluations based on pornography, politics, violence, and terror .
Outcome:	The proposed framework reduces interpretation cost by 55% and improves safety-critical features.

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Jianshu She, Xinyue Li, Eric P. Xing, Zhengzhong Liu, Qirong Ho

Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents (2026.acl-long)

Challenge:	a new framework for steering language models reveals how concepts become linearly separable as training progresses .
Approach:	They propose a framework to analyze steerability in language models by using hidden state and representation analysis.
Outcome:	The proposed framework reveals how steerability evolves over training . concepts become linearly separable as training progresses, the framework shows .

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Yi Yu, Liuyi Yao, Yuexiang Xie, Qingquan Tan, Jiaqi Feng, Yaliang Li, Libing Wu

EchoMLLM: Incentivizing Echocardiographic Video Understanding with Keyframe Grounding and Report Generation (2026.findings-acl)

Challenge:	Existing methods handle long-term memory (LTM) and short-term (STM) as separate components, relying on heuristics or auxiliary controllers, which limits adaptability and end-to-end optimization.
Approach:	They propose a framework that integrates LTM and STM management directly into the agent's policy and propose 'agentic memory' to train such unified behaviors.
Outcome:	The proposed framework outperforms strong memory-augmented baselines on five long-horizon benchmarks and achieves higher-quality long-term memory and more efficient context usage.

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Heyu Huang, Wanran Sun, Chi Chen, Bo Chen, Zonghao Guo, Yuhua Li, Ruixuan Li, Kunlun He, Maosong Sun

Visual Data in NLP

Challenge:	Echocardiography analysis requires a dual capability: rigorous quantitative keyframe localization and comprehensive qualitative synthesis.
Approach:	They propose a unified framework designed for real-world echocardiography video understanding.
Outcome:	a new framework is designed to support real-world echocardiography video understanding . it reduces temporal grounding errors by up to 76% and improves report generation quality by 65% .

MIRTH: Mutual-Information Reasoning with Temporal Hubs for Vision-Language-Action Agents (2026.acl-long)

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Hao Sun, Yu Song, Teng Shiyu, Ziwei Niu, Yen-wei Chen

Self-Correcting RAG: Enhancing Faithfulness via MMKP Context Selection and NLI-Guided MCTS (2026.findings-acl)

Challenge:	a recent study shows that VLA models suffer from temporal myopia that discards historical dynamics and reasoning gaps between high-level instructions and low-level motor commands.
Approach:	They propose a framework to address temporal myopia and autoregressive scalar decoding in VLAs . they propose two memory hubs that compress long-term scene evolution and short-term motion trends .
Outcome:	The proposed framework achieves state-of-the-art performance and exhibiting emergent error recovery capabilities.

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Shijia Xu, Zhou Wu, Xiaolong Jia, Yu Wang, Kai Liu, April Xiaowen Dong

Interpretation Meets Safety: A Survey on Interpretation Methods and Tools for Improving LLM Safety (2025.emnlp-main)

Challenge:	Existing approaches to retrieval-augmented generation still face problems with low context utilization and frequent hallucinations.
Approach:	They propose a framework that reformulates retrieval and generation as constrained optimization and path planning.
Outcome:	The proposed framework significantly improves reasoning accuracy on complex queries while reducing hallucinations.

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Seongmin Lee, Aeree Cho, Grace C. Kim, ShengYun Peng, Mansi Phute, Duen Horng Chau

AT²PO: Agentic Turn-based Policy Optimization via Tree Search (2026.acl-long)

Challenge:	Existing surveys focus on interpretation or safety, but safety and understanding are core motivations for interpretation research.
Approach:	They propose a framework that connects interpretation methods, enhancements they inform, and tools that operationalize them.
Outcome:	The proposed framework summarizes nearly 70 studies at their intersections and concludes with open challenges and future directions.

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Zefang Zong, Dingwei Chen, Yang Li, Qi Yi, Bo Zhou, Chengming Li, BO Qian, Peng Chen, Jie Jiang

Decoding the Multimodal Mind: Generalizable Brain-to-Text Translation via Multimodal Alignment and Adaptive Routing (2026.findings-acl)

Challenge:	Recent advances in large language models (LLMs) have catalyzed the development of autonomous agents capable of executing complex, multi-turn tasks.
Approach:	They propose a framework for agentic reinforcement learning that integrates turn-level tree search with tree search to address key challenges.
Outcome:	The proposed framework addresses key challenges: limited exploration diversity, sparse credit assignment, and misaligned policy optimization.

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Chunyu Ye, Yunhao Zhang, Jingyuan Sun, Chong Li, Yang Zhao, Shaonan Wang

IFCap: Image-like Retrieval and Frequency-based Entity Filtering for Zero-shot Captioning (2024.emnlp-main)

Challenge:	Current approaches to decoding language from the human brain rely on unimodal representations, neglecting the brain’s inherently multimodal processing.
Approach:	They propose a framework that leverages Multimodal Large Language Models to align brain signals with a shared semantic space encompassing text, images, and audio.
Outcome:	The proposed framework achieves an 8.48% improvement on the most commonly used benchmark on fMRI datasets with textual, visual, and auditory stimuli.

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Soeun Lee, Si-Woo Kim, Taewhan Kim, Dong-Jin Kim

GUI Agents: A Survey (2025.findings-acl)

Challenge:	Existing text-only training methods overlook the modality gap between using text data during training and employing images during inference.
Approach:	They propose a novel approach that aligns text features with visually relevant features to mitigate the modality gap between using text data during training and employing images during inference.
Outcome:	The proposed method outperforms the state-of-the-art methods in image captioning and video captioning by a significant margin compared to training with text data.

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Dang Nguyen, Jian Chen, Yu Wang, Gang Wu, Namyong Park, Zhengmian Hu, Hanjia Lyu, Junda Wu, Ryan Aponte, Yu Xia, Xintong Li, Jing Shi, Hongjie Chen, Viet Dac Lai, Zhouhang Xie, Sungchul Kim, Ruiyi Zhang, Tong Yu, Mehrab Tanjim, Nesreen K. Ahmed, Puneet Mathur, Seunghyun Yoon, Lina Yao, Branislav Kveton, Jihyung Kil, Thien Huu Nguyen, Trung Bui, Tianyi Zhou, Ryan A. Rossi, Franck Dernoncourt

Challenge:	Large Foundation Models (LFMs) have transformed the landscape of AI research and day-to-day life.
Approach:	They propose a framework that delineates GUI agents' perception, reasoning, planning, and acting capabilities.
Outcome:	The proposed framework delineates their perception, reasoning, planning, and acting capabilities.

Mixture-of-Modules: Reinventing Transformers as Dynamic Assemblies of Modules (2024.emnlp-main)

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Zhuocheng Gong, Ang Lv, Jian Guan, Wei Wu, Huishuai Zhang, Minlie Huang, Dongyan Zhao, Rui Yan

HalluLens: LLM Hallucination Benchmark (2025.acl-long)

Challenge:	Empirical results show that MoMs consistently outperform vanilla transformers .
Approach:	They propose an architecture that allows for a mixture-of-modules computation that uses a finite set of modules defined by multi-head attention and feed-forward networks.
Outcome:	The proposed architecture outperforms vanilla Transformers and their variants in multiple ways.

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Yejin Bang, Ziwei Ji, Alan Schelten, Anthony Hartshorn, Tara Fowler, Cheng Zhang, Nicola Cancedda, Pascale Fung

TeCES: Collaborative Geometric Knowledge Representation Framework under Evolving Fact Snapshots (2026.acl-long)

Challenge:	Large language models (LLMs) generate responses that deviate from user input or training data, a phenomenon known as "hallucination" .
Approach:	They propose a hallucination benchmark HalluLens that includes both extrinsic and intrinsic evaluation tasks to distinguish between extrindic and intrinsic hallucines.
Outcome:	The proposed framework disentangles LLM hallucination from "factuality" and distinguishes between extrinsic and intrinsic hallucines to promote consistency and facilitate research.

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Jiujiang Guo, Zhengliang Guo, Kai Wang, Meiyang Wang, Dehua Peng, Shaozu Yuan, Chengyin Hu, Shuan Ai, Yiwei Wei

TABARD: A Novel Benchmark for Tabular Anomaly Analysis, Reasoning and Detection (2025.findings-emnlp)

Challenge:	Existing knowledge graphs represent static facts but lack collaborative modeling of both . e.g., existing knowledge graph models lack a framework for integrating snapshots into knowledge graph.
Approach:	They propose a framework for high-fidelity modeling of evolving snapshots using concept of snapshots.
Outcome:	The proposed framework outperforms existing models on six benchmarks.

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Manan Roy Choudhury, Anirudh Iyengar Kaniyar Narayana Iyengar, Shikhhar Siingh, Sugeeth Puranam, Vivek Gupta

LoRE-Merging: Exploring Low-Rank Estimation For Large Language Model Merging (2025.findings-emnlp)

Challenge:	a new benchmark is constructed to evaluate the accuracy of large language models for tabular data . the benchmark uses direct, indirect, and Chain-of-Thought prompting .
Approach:	They propose a framework that uses prompting, self-verification and constraint-based rule execution to improve accuracy.
Outcome:	The proposed framework significantly improves accuracy and recall in tabular data.

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Zehua Liu, Han Wu, Yuxuan Yao, Xiaojin Fu, Ruifeng She, Xiongwei Han, Tao Zhong, Mingxuan Yuan

Preserving Language Capabilities in Vision-Language Models via Representation Regulation (2026.findings-acl)

Challenge:	a framework for model merging is proposed without additional training . task vectors from fine-tuned models exhibit a limited number of dominant singular values .
Approach:	They propose a framework for model merging based on low-rank estimation of task vectors without access to the base model.
Outcome:	The proposed framework improves models without additional training without additional inputs.

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ZiXuan Chen, Juncheng Tao, Ziqian Zeng

Chain-of-Reasoning: Towards Unified Mathematical Reasoning in Large Language Models via a Multi-Paradigm Perspective (2025.acl-long)

Challenge:	Vision-Language Models (VLMs) provide a unified framework to process both text-only and vision-language tasks.
Approach:	They propose a method to reduce the distance between visual and textual representations by introducing a Representation Distribution Difference (RDD) loss.
Outcome:	Empirical evidence shows that finetuning VLMs on vision-language data has degraded language capabilities.

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Yiyao Yu, Yuxiang Zhang, Dongdong Zhang, Xiao Liang, Hengyuan Zhang, Xingxing Zhang, Mahmoud Khademi, Hany Hassan Awadalla, Junjie Wang, Yujiu Yang, Furu Wei

Does Memory Need Graphs? A Unified Framework and Empirical Analysis for Long-Term Dialog Memory (2026.acl-long)

Challenge:	Existing work shows that LLMs rely on single-paradigm reasoning that limits their effectiveness across diverse tasks.
Approach:	They propose a new framework that integrates multiple reasoning paradigms to enable synergistic collaboration.
Outcome:	The proposed model outperforms current SOTA models in theorem proving tasks and the MATH benchmark in arithmetic tasks.

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Sen Hu, Yuxiang Wei, Jiaxin Ran, Xueran Han, Zhiyuan Yao, Huacan Wang, Ronghao Chen, Lei Zou

MEXA: Towards General Multimodal Reasoning with Dynamic Multi-Expert Aggregation (2025.findings-emnlp)

Challenge:	Existing literature on dialog memory systems is inconsistent on their effectiveness . empirical findings on graph structures are difficult to attribute to specific design choices .
Approach:	They propose a framework that decomposes dialog memory systems into core components . they conduct stage-wise experiments on LongMemEval and HaluMeM, and compare implementation details .
Outcome:	The proposed framework compares graph-based and non-graph memory architectures on long-term dialog memory systems.

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Shoubin Yu, Yue Zhang, Ziyang Wang, Jaehong Yoon, Mohit Bansal

EffiVLM-BENCH: A Comprehensive Benchmark for Evaluating Training-Free Acceleration in Large Vision-Language Models (2025.acl-long)

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 .

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Zekun Wang, MingHua Ma, Zexin Wang, Rongchuan Mu, Liping Shan, Ming Liu, Bing Qin

HER: Human-like Reasoning and Reinforcement Learning for LLM Role-playing (2026.findings-acl)

Challenge:	Existing methods for accelerating Large Vision-Language Models lack comprehensive evaluation across diverse backbones, benchmarks, and metrics.
Approach:	They propose EffiVLM-BENCH framework for evaluating absolute performance and generalization and loyalty.
Outcome:	The proposed framework offers insights into optimal strategies for accelerating LVLMs.

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Chengyu Du, Xintao Wang, Aili Chen, Weiyuan Li, Rui Xu, Junteng Liu, Zishan Huang, Rong Tian, Zijun Sun, Yuhao Li, Liheng Feng, Deming Ding, Pengyu Zhao, Yanghua Xiao

Mehran Kazemi, Bahare Fatemi, Hritik Bansal, John Palowitch, Chrysovalantis Anastasiou, Sanket Vaibhav Mehta, Lalit K Jain, Virginia Aglietti, Disha Jindal, Peter Chen, Nishanth Dikkala, Gladys Tyen, Xin Liu, Uri Shalit, Silvia Chiappa, Kate Olszewska, Yi Tay, Vinh Q. Tran, Quoc V Le, Orhan Firat

Challenge:	Existing models for LLM role-playing lack high-quality datasets with explicit reasoning traces and reliable reward signals aligned with human preferences.
Approach:	They propose a unified framework for cognitive-level persona simulation that strictly distinguishes characters’ first-person thinking processes from LLMs’ third-person reasoning.
Outcome:	The proposed framework outperforms the Qwen3-32B baseline model and achieves a 30.26% and 14.97% performance on the minimax benchmarks.

BIG-Bench Extra Hard (2025.acl-long)

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Just a Scratch: Enhancing LLM Capabilities for Self-harm Detection through Intent Differentiation and Emoji Interpretation (2025.acl-long)

Challenge:	Current benchmarks for large language model reasoning focus on math and coding abilities, leaving a gap in evaluating broader reasoning proficiencies.
Approach:	They propose a benchmark to evaluate general reasoning in large language models . they use BIG-Bench and its harder version BIG-Benefit Hard to assess general reasoning .
Outcome:	The new benchmark pushes the boundaries of LLM reasoning evaluation.

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Soumitra Ghosh, Gopendra Vikram Singh, Shambhavi Shambhavi, Sabarna Choudhury, Asif Ekbal

TLoRA: Task-aware Low Rank Adaptation of Large Language Models (2026.acl-long)

Challenge:	Self-harm detection on social media is critical for early intervention and mental health support, yet remains challenging due to the subtle, context-dependent nature of such expressions.
Approach:	They propose a framework to distinguish intent through nuanced language–emoji interplay.
Outcome:	The proposed framework improves self-harm detection and explanation tasks on three state-of-the-art LLMs.

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Weicheng Lin, Yi Zhang, Jiawei Dang, Liang-Jie Zhang

ReFLAIR: Enhancing Multimodal Reasoning via Structured Reflection and Reward-Guided Learning (2025.findings-emnlp)

Challenge:	Existing low-rank Adaptation (LoRA) methods address only one factor, often at the cost of increased training complexity or reduced practical efficiency.
Approach:	They propose a low-rank Adaptation framework that optimizes initialization and resource allocation at the outset of training.
Outcome:	The proposed framework performs excellently across various tasks while reducing the number of trainable parameters.

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Jiazhou Ji, Xinru Lu

Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement (2025.acl-long)

Challenge:	Existing training methods for large models do not address the trade-off between reflection and accuracy.
Approach:	a unified framework teaches large models to perform structured reflection via an explicit $think re-think answer $ format and hybrid reward learning.
Outcome:	The proposed framework improves model performance on mathematical benchmarks and reduces inference cost by nearly 23%.

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Yaxuan Kong, Yiyuan Yang, Yoontae Hwang, Wenjie Du, Stefan Zohren, Zhangyang Wang, Ming Jin, Qingsong Wen

Towards Visually Grounded Multimodal Summarization via Cross-Modal Transformer and Gated Attention (2026.findings-acl)

Challenge:	Existing time series models focus on a narrow spectrum of tasks, such as forecasting or anomaly detection.
Approach:	They propose a framework that enables natural language queries across multiple time series tasks such as numerical analytical tasks and open-ended question answering with reasoning.
Outcome:	The proposed framework enables natural language queries across multiple time series tasks and allows for more advanced and intuitive interactions with temporal data.

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Abid Ali, Diego Molla, Usman Naseem

UniVocal: Unified Speech-Singing Code-Switching Synthesis (2026.acl-long)

Challenge:	Existing methods for multimodal summarization often inject shallow visual features into deep models, leading to representational mismatches and weak cross-modal grounding.
Approach:	They propose a framework that performs text summarization and representative image selection . a deep visual processor aligns the visual encoder with the language model at corresponding depths .
Outcome:	The proposed framework produces more accurate, visually grounded summaries and selects more representative images.

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YuFei Shi, Qian Chen, Wen Wang, Xiangang Li, Zhen-Hua Ling, Yang Ai

Speech & Audio in NLP

Challenge:	Existing systems cannot automatically determine when to switch between modes based on text content.
Approach:	They propose a unified framework that implicitly infers vocal modes from text context to pioneer SCS Synthesis.
Outcome:	The proposed framework infers vocal modes solely from text context to pioneer SCS Synthesis.

SCOPE: Preserving Modality-Specific Cues to Mitigate Modality Laziness in Multimodal Learning (2026.findings-acl)

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Jingfan Yang, Rui Zhang, Liang Hong, Ke Yuan

SMART: Semantic Header Flattening and Pseudo-Code-Style Reasoning for LLM-based Complex Table Question Answering (2026.findings-acl)

Challenge:	Existing approaches to learning multimodal representations emphasize shared semantics and overlook modality-specific cues.
Approach:	They propose a framework for learning complete multimodal representations using shared and practical cues.
Outcome:	SCOPE outperforms SOTA benchmarks on four datasets and achieves 27.10% accuracy improvement.

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Yongshuo Zhang, Jixiong Chen, Kaihe xu, Wei Wei, Shihao Zou

On the Proper Treatment of Units in Surprisal Theory (2026.acl-long)

Challenge:	Existing approaches to complex table question answering rely on handcrafted table linearization or prompts . Existing methods rely only on hand-crafted table and require hierarchical hierarchies to align conditions, attributes, and values.
Approach:	They propose a framework that explicitly decouples table structure understanding from reasoning execution.
Outcome:	Experiments show that SMART improves accuracy and robustness of complex table question answering (TQA) . SMart decouples table structure understanding from reasoning execution, enabling state-of-the-art performance.

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Samuel Kiegeland, Vésteinn Snæbjarnarson, Tim Vieira, Ryan Cotterell

Linguistics & Cognitive NLP

Challenge:	empirical work often leaves the notion of a unit underspecified . empirical work has sought to characterize the processing difficulty comprehenders experience .
Approach:	They propose a framework for reasoning about surprisal over arbitrary unit inventories . they argue that surprises should be explicit and treat tokenization as implementation detail .
Outcome:	The proposed framework disentangles the models' definitions and the regions of interest and treats tokenization as an implementation detail rather than a scientific primitive.

TALAS: Teacher-Anchored Layer Alignment with Adaptive Sharpness-Aware Minimization for Embedding Distillation (2026.acl-long)

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Quoc Phong Dao, Hoang Son Nguyen, Pham Khanh Chi, Linh Ngo Van, Nguyen Thi Ngoc Diep, Thien Huu Nguyen, Trung Le

A Comprehensive Framework to Operationalize Social Stereotypes for Responsible AI Evaluations (2025.emnlp-main)

Challenge:	Existing methods that require a student to strictly mimic the teacher’s sentence embeddings or internal features often incur prohibitive computational costs and yield suboptimal performance due to the inherent capacity gap.
Approach:	They propose a Teacher-Anchored mechanism that selectively distills final sentence embeddings only into the student’s upper layers, thereby reducing overhead while respecting capacity constraints.
Outcome:	Empirical results show that TALAS outperforms existing methods while maintaining high performance.

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Aida Mostafazadeh Davani, Sunipa Dev, Héctor Pérez-Urbina, Vinodkumar Prabhakaran

UniDataBench: Evaluating Data Analytics Agents Across Structured and Unstructured Data (2026.acl-long)

Challenge:	Recent years have seen unprecedented gains in generative AI models' capabilities across modalitieslanguage, image, audio, and video domains across the globe.
Approach:	They propose a framework to operationalize stereotypes in generative AI evaluations using social psychological research and NLP data.
Outcome:	The proposed framework identifies key components of stereotypes that are crucial in AI evaluation, including the target group, associated attribute, relationship characteristics, perceiving group, and context.

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Han Weng, Zhou Liu, Yuanfeng Song, Xiaoming Yin, Xing Chen, Wentao Zhang

Structured Dialogue Refinement: Building Retrieval-Augmented Question Answering on Goal-Oriented Dialogues (2026.findings-acl)

Challenge:	Existing benchmarks do not assess agents’ capabilities across data types . Existing tools only evaluate agents' ability to extract reasonable insights across data formats.
Approach:	They propose a multi-source benchmark to evaluate the performance of data analytics agents in handling diverse data sources.
Outcome:	The proposed agent performs end-to-end analysis over diverse data sources by automatically discovering cross-source linkages, decomposing goals, and generating robust, self-correcting code to extract actionable insights.

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Bin Wu, Sawan Kumar, Prasetya Ajie Utama, Mohamed Yahya

SpiralThinker: Latent Reasoning through an Iterative Process with Text–Latent Interleaving (2026.findings-acl)

Challenge:	Retrieval-Augmented Generation (RAG) is widely used for knowledgeintensive question answering (QA), but a large amount of real-world problem-solving knowledge is captured in goal-oriented dialogues.
Approach:	They propose a framework that adapts dialogue corpora for RAG at both retrieval and generation stages without altering the underlying pipeline.
Outcome:	The proposed framework improves retrieval quality and QA performance under dialogue-specific structural challenges.

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Shengmin Piao, Sanghyun Park

KG-ViP: Bridging Knowledge Grounding and Visual Perception in Multi-modal LLMs for Visual Question Answering (2026.acl-long)

Challenge:	Existing latent reasoning methods lack mechanisms to ensure stable reasoning dynamics in latent space and a systematic way to interleave implicit and explicit reasoning.
Approach:	They propose a framework that performs iterative updates over latent representations while enabling interleaved reasoning across latent and textual steps.
Outcome:	SpiralThinker achieves state-of-the-art among latent reasoning baselines.

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Zhiyang Li, Ao Ke, Yukun Cao, Xike Xie

Multi-Drafter Speculative Decoding with Alignment Feedback (2026.findings-acl)

Challenge:	Existing approaches to Visual Question Answering lack synergistic potential of scene graphs and scene graph.
Approach:	They propose a retrieval-and-fusion pipeline that fuses scene graphs and commonsense graphs to enable multi-modal reasoning.
Outcome:	Experiments on FVQA 2.0+ and MVQA benchmarks show that KG-ViP outperforms existing methods.

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Taehyeon Kim, Hojung Jung, Se-Young Yun

Challenge:	Existing methods to accelerate large language model (LLM) inference use a smaller model to draft future tokens, which are then verified by the target LLM.
Approach:	They propose a unified framework that integrates multiple drafters into the SD process.
Outcome:	Extensive experiments show that MetaSD outperforms single-drafter approaches.

COSMOS: Connectivity-Oriented Submodular Maximization for Optimal Subgraph Retrieval (2026.acl-long)

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Boci Peng, Xiao Liu, Boren Hu, Yun Zhu, Xuanbo Fan, Yanwei Yue, Chunyu Yang, Yan Zhang

LeCoDe: A Benchmark Dataset for Interactive Legal Consultation Dialogue Evaluation (2026.acl-long)

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.

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Weikang Yuan, Kaisong Song, Zhuoren Jiang, Junjie Cao, Yujie Zhang, Jun Lin, Kun Kuang, Ji Zhang, Xiaozhong Liu

InsAT: Instance-aware Semantic Alignment and Transfer from Human–Object Keypoints for Zero-to-Few-shot Action Understanding (2026.acl-long)

Challenge:	Current systems for legal consultation are insufficient to handle the knowledge-intensive nature of real-world consultations.
Approach:	They propose a multi-turn benchmark dataset to evaluate LLMs in legal consultation settings.
Outcome:	The proposed framework assesses LLMs’ consultation capabilities in terms of (1) clarification capability and (2) professional advice quality.

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Kazuki Tsutsukawa

PRIME: Large Language Model Personalization with Cognitive Dual-Memory and Personalized Thought Process (2025.emnlp-main)

Challenge:	Existing zero-shot (ZS) approaches emphasize human motion while underutilizing contextual information, particularly human–object interactions.
Approach:	They propose a framework for ZS recognition and zero-to-few-shot adaptation that leverages instance-level language descriptions.
Outcome:	The proposed framework outperforms keypoint-based ZS methods while remaining data-efficient and robust.

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Xinliang Frederick Zhang, Nicholas Beauchamp, Lu Wang

MTR-Suite: A Framework for Evaluating and Synthesizing Conversational Retrieval Benchmarks (2026.acl-long)

Challenge:	Large language model (LLM) personalization aims to align outputs with individuals’ unique preferences and opinions.
Approach:	They integrate a cognitive dual-memory model into LLM personalization by mirroring episodic memory to historical user engagements and semantic memory to long-term, evolving user beliefs.
Outcome:	The proposed framework integrates the well-established cognitive dual-memory model into LLM personalization, using episodic and semanticmemories.

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Junhao Ruan, Abudukeyumu Abudula, Bei Li, Yongjing Yin, Xinyu Liu, Kechen Jiao, Xin Chen, Jingang Wang, Xunliang Cai, Tong Xiao, JingBo Zhu

Can Large Language Models Adequately Perform Symbolic Reasoning Over Time Series? (2026.findings-acl)

Challenge:	Existing conversational retrieval benchmarks suffer from costly, sparse human annotation or rigid, unnatural automated heuristics.
Approach:	They propose a framework for auditing, synthesizing, and benchmarking conversational retrieval.
Outcome:	The proposed framework is based on three LLM-based auditors and a multi-agent system . it mimics production-style challenges (hard topic switching, verbosity) and offers superior discriminative power.

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Zewen Liu, Juntong Ni, Xianfeng Tang, Max SY Lau, Qi He, Wenpeng Yin, Wei Jin

EPIR: Capturing Promoting and Inhibiting Relationships between Events (2026.findings-acl)

Challenge:	Large Language Models (LLMs) and Multimodal LLMs (MLLMs) show strong performance in complex reasoning tasks, but their ability to extract symbolic laws from time series data remains underexplored.
Approach:	They propose a benchmark to assess symbolic reasoning over real-world time series across three tasks: multivariate symbolic regression, Boolean network inference, and causal discovery.
Outcome:	The proposed framework integrates LLMs with genetic programming to form a closed-loop symbolic reasoning system.

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Bowen Dong, Wenjun Wang, Xueli Liu, Quanlin Qiu

Challenge:	promoting and inhibiting relationships capture directional, probabilistic, and context-dependent shifts in event likelihood.
Approach:	They propose a framework for estimating promoting and inhibiting relationships from observed event data.
Outcome:	The proposed framework outperforms state-of-the-art models on real-world datasets in accuracy.

Understanding and Mitigating Spurious Signal Amplification in Test-Time Reinforcement Learning for Math Reasoning (2026.findings-acl)

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Yongcan Yu, Lingxiao He, Jian Liang, Kuangpu Guo, Meng Wang, Qianlong Xie, Xingxing Wang, Ran He

A Learnable Skill Combination Strategy for Multi-task Learning in Natural Language Understanding (2026.findings-acl)

Challenge:	a framework to mitigate spurious optimization signals is proposed for test-time reinforcement learning (TTRL) Reinforcement learning with verifiable rewards (RLVR) is an effective paradigm for improving large language models on structured challenging reasoning tasks.
Approach:	They propose a framework to mitigate spurious optimization signals from label noise . they propose to use a frequency-based sampling strategy to exclude ambiguous samples .
Outcome:	The proposed framework outperforms existing TTRL baselines on three large language models across multiple mathematical reasoning benchmarks.

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Zhe Yang, Yi Huang, Yaqin Chen, Mengfei Guo, Xiaoting Wu, Junlan Feng

ProactiveEval: A Unified Evaluation Framework for Proactive Dialogue Agents (2026.acl-long)

Challenge:	a novel multi-task learning framework for domain-specific natural language understanding tasks addresses these limitations by combing multiple tasks into a single framework.
Approach:	They propose a multi-task learning framework that decomposes the language model into modular skill components and employs a dynamic, learnable skill-combination mechanism to adaptively handle diverse tasks.
Outcome:	The proposed framework surpasses conventional multi-task learning approaches in performance.

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Tianjian Liu, Fanqi Wan, Jiajian Guo, Xiaojun Quan

MASPO: Unifying Gradient Utilization, Probability Mass, and Signal Reliability for Robust and Sample-Efficient LLM Reasoning (2026.acl-long)

Challenge:	Existing studies on proactive dialogue models focus on domain-specific or task-oriented scenarios, which leads to fragmented evaluations and limits the comprehensive exploration of models’ proactive dialogue abilities.
Approach:	They propose a framework for evaluating proactive dialogue capabilities of large language models that decomposes proactive dialogue into target planning and dialogue guidance, establishing evaluation metrics across various domains.
Outcome:	The proposed framework decomposes proactive dialogue into target planning and dialogue guidance, establishing evaluation metrics across various domains, and enables automatic generation of diverse and challenging evaluation data.

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Xiaoliang Fu, Jiaye Lin, Yangyi Fang, Binbin Zheng, Chaowen Hu, Zekai Shao, Cong Qin, Lu Pan, Ke Zeng, Xunliang Cai

PsychePass: Calibrating LLM Therapeutic Competence via Trajectory-Anchored Tournaments (2026.findings-acl)

Challenge:	Existing RLVR algorithms rely on rigid, uniform, and symmetric trust region mechanisms . current algorithms lack robustness, asymmetric signal reliability and inefficient gradient utilization .
Approach:	They propose a framework to harmonize three dimensions of RLVR algorithms, a paper argues . a binary cutoff is used to discard valuable reinforcement signals, they argue .
Outcome:	The proposed framework outperforms baselines in evaluating a robust RLVR solution.

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Zhuang Chen, Dazhen Wan, Zhangkai Zheng, Guanqun Bi, Xiyao Xiao, Binghang Li, Minlie Huang

Challenge:	evaluating therapeutic competence of large language models remains challenging due to unstructured and longitudinal nature of counseling.
Approach:	They propose a framework that calibrates the therapeutic competence of LLMs via trajectory-anchored tournaments.
Outcome:	The proposed framework calibrates the therapeutic competence of LLMs via trajectory-anchored tournaments.

Small Data, Big Noise: Adversarial Training for Robust ParameterEfficient Fine-Tuning (2026.findings-acl)

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Eitan Cohen, Idan Simai, Uri Shaham

DarwinTOD: LLM-Driven Lifelong Self-evolution for Task-oriented Dialog Systems (2026.acl-long)

Challenge:	Parameter-Efficient Fine-Tuning (PEFT) is essential for adapting foundation models to downstream tasks, but current methods struggle with robustness to noise and performance degradation on limited training data.
Approach:	They propose a framework that brings adversarial training to PEFT to enhance model robustness and generalization, outperforming alternative approaches.
Outcome:	Experiments with two variants of the proposed framework show that it outperforms existing methods in low-resource settings and under word-level and character-level corruptions.

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Shuyu Zhang, Yujie Liu, Xinru Wang, Cheng Zhang, Yanmin Zhu, Bin Li

CLAOCS-TX: Cross-Lingual Triplet Extraction with Aspect-Opinion-Aware Code-Switched Prompting and LLM-Guided Contrastive Distillation (2026.acl-long)

Challenge:	Continual learning approaches fail to achieve autonomy lifelong improvement in dynamic environments . current task-oriented dialog systems are static, unable to learn from ongoing interactions .
Approach:	They propose a lifelong self-evolving dialog framework that integrates evolutionary computation and LLM driven self-improvement into a single framework.
Outcome:	The proposed framework surpasses state-of-the-art methods and exhibits continuous performance gains throughout evolution.

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Lipika Dewangan, Chandresh Kumar Maurya

See or Say Graphs: Agent-Driven Scalable Graph Understanding with Vision-Language Models (2026.findings-acl)

Challenge:	Existing work on cross-lingual aspect–opinion–sentiment triplet extraction has focused on coarse-grained sentiment classification or aspect extraction.
Approach:	They propose a framework that leverages large language models as pseudo-label generators and semantic teachers for ASTE.
Outcome:	The proposed framework generates reliable pseudo triplets for unlabeled languages, while maintaining high-confidence supervision.

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Shuo Han, Yukun Cao, Zezhong Ding, Zengyi Gao, S Kevin Zhou, Xike Xie

Learning Faster with Better Tokens: Parameter-Efficient Vocabulary Adaptation for Specialized Text Summarization (2026.acl-long)

Challenge:	Existing studies have explored textual graph descriptions and visual modalities for VLMs to understand graphs.
Approach:	They propose a unified framework that enhances both scalability and modality coordination in graph understanding by integrating textual and visual modalities.
Outcome:	GraphVista scales to large graphs, 200 larger than those used in existing benchmarks, and consistently outperforms existing textual, visual, and fusion-based methods.

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Gunjan Balde, Soumyadeep Roy, Mainack Mondal, Niloy Ganguly

An Information-Theoretic Foundation for the Subregular Hierarchy (2026.acl-long)

Challenge:	a new approach to adapt generalist models to expert domains is needed to overcome this problem.
Approach:	They propose a parameter-efficient domain adaptation approach that combines vocabulary adaptation with pretraining for LLM-based text summarization.
Outcome:	The proposed approach reduces training time by 35-55% over continual pretraining and reduces parameter counts up to 37% w.r.t expansion-only methods.

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Mai Phan Quoc Hung, Khanh Nguyen Quoc, Đoàn Minh Luong, Duong Thu Ngan, Duong Thi Phuong Thao, Tuan Do

Linguistic Theories

Challenge:	Subregular theory posits that phonological patterns in natural languages occupy restricted region of formal hierarchy . phonology patterns in SL, SP, and Tier-based Strictly Local (TSL) languages are restricted . experimental evidence demonstrates humans fail to learn patterns outside these subregulate classes .
Approach:	They propose a subregular hypothesis that phonological patterns in natural languages occupy a restricted region of the formal language hierarchy.
Outcome:	The proposed framework offers a framework for understanding computational restrictions on natural language phonology.

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Wenqiang Wang, Peng Chen, Yan Xiao, Yangshijie Zhang, Xiaoyue Lu, Jianjie Huang, Xiaochun Cao