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检索条件"任意字段=Conference on empirical methods in natural language processing"

共 15208 条记录，以下是251-260 订阅

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ROUGE-SciQFS: A ROUGE-based Method to Automatically Create Datasets for Scientific Query-Focused Summarization 31

ROUGE-SciQFS: A ROUGE-based Method to Automatically Create D...

引用

31st International conference on Computational Linguistics, COLING 2025

作者： Ramirez-Orta, Juan Milios, Evangelos Maguitman, Ana Soto, Axel J. Faculty of Computer Science Dalhousie University Canada Institute for Computer Science and Engineering UNS - CONICET Department of Computer Science and Engineering Universidad Nacional del Sur Argentina

ISBN: (纸本)9798891761964

So far, the task of Scientific Query-Focused Summarization (Sci-QFS) has lagged in development when compared to other areas of Scientific natural language processing because of the lack of data. In this work, we propose a methodology to take advantage of existing collections of academic papers to obtain large-scale datasets for this task automatically. After applying it to the papers from our reading group, we introduce a novel dataset for Sci-QFS composed of 8,695 examples, each one with a query, the sentences of the full text from a paper and the relevance labels for each. After testing several classical and state-of-the-art embedding models on this data, we found that the task of Sci-QFS is far from being solved, although it is relatively straightforward for humans. Surprisingly, we found that classical methods outperformed modern pre-trained Deep language Models (sometimes by a large margin), showing the need for large datasets to better fine-tune the latter. We share our experiments, data and models at https://***/jarobyte91/rouge_sciqfs. © 2025 Association for Computational Linguistics.

关键词： Structured Query language

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Rule-Based Dialect of Tulang Bawang Stemmer

Rule-Based Dialect of Tulang Bawang Stemmer

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2025 International conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Abidin, Zaenal Junaidi, Akmal Wamiliana Lumbanraja, Favorisen Rosyking Kurniasari, Dian Borman, Rohmat Indra Doctoral Program of Mathematics and Natural Sciences Universitas Lampung Bandar Lampung Indonesia Universitas Lampung Department of Computer Science Bandar Lampung Indonesia Universitas Lampung Department of Mathematics Bandar Lampung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandar Lampung Indonesia

ISBN: (纸本)9798331513320

Stemming, an essential procedure in natural language processing (NLP), diminishes words to their base forms, facilitating tasks such as information retrieval and sentiment analysis. Although stemming techniques for highresource languages are well-developed, numerous low-resource languages, including dialect of Tulang Bawang, suffer from inadequate solutions owing to a scarcity of linguistic data and resources. Existing systems, including rule-based stemmers, have demonstrated efficacy in processing low-resource languages such as Indonesian and Javanese by utilizing established morphological rules. Nonetheless, these methods encounter considerable obstacles, such as restricted adaptability, inability to accommodate unusual root structures, and excessive dependence on fixed rules that might result in over- or understemming. Rule-based methodologies frequently misidentify roots when faced with intricate affixes or unconventional word forms. We introduce an improved rule-based Tulang Bawang Stemmer aimed at overcoming these constraints by enhancing current linguistic rules and integrating new patterns specific to the language's morphology. Assessed on 500 test samples and 200 independent test samples, our improved stemmer attained gold standard evaluation metrics of 96.2% and 94%, respectively, surpassing prior implementations in both precision and generalization. The findings demonstrate the potential of enhanced rule-based techniques to improving NLP for lowresource languages. Improved stemming performance enables better downstream applications, promotes more efficient text analysis, and advances research in underrepresented languages. © 2025 IEEE.

关键词： Linguistics

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Re-Evaluating Group Robustness via Adaptive Class-Specific Scaling

Re-Evaluating Group Robustness via Adaptive Class-Specific S...

引用

2025 IEEE/CVF Winter conference on Applications of Computer Vision, WACV 2025

作者： Seo, Seonguk Han, Bohyung Seoul National University Ece Korea Republic of Seoul National University Ipai Korea Republic of

ISBN: (纸本)9798331510831

Group distributionally robust optimization, which aims to improve robust accuracies-worst-group and unbiased accuracies-is a prominent algorithm used to mitigate spu-rious correlations and address dataset bias. Although ex-isting approaches have reported improvements in robust accuracies, these gains often come at the cost of average accuracy due to inherent trade-offs. To control this trade-off flexibly and efficiently, we propose a simple class-specific scaling strategy, directly applicable to existing debiasing algorithms with no additional training. We further develop an instance-wise adaptive scaling technique to alleviate this trade-off, even leading to improvements in both robust and average accuracies. Our approach reveals that a naive ERM baseline matches or even outperforms the recent debi-asing methods by simply adopting the class-specific scaling technique. Additionally, we introduce a novel unified metric that quantifies the trade-off between the two accuracies as a scalar value, allowing for a comprehensive evaluation of existing algorithms. By tackling the inherent trade-off and offering a performance landscape, our approach provides valuable insights into robust techniques beyond just robust accuracy. We validate the effectiveness of our framework through experiments across datasets in computer vision and natural language processing domains. © 2025 IEEE.

关键词： natural language processing systems

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How to Train Your Llama - Efficient Grammar-Based Application Fuzzing Using Large language Models 29th

How to Train Your Llama - Efficient Grammar-Based Applicatio...

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29th Nordic conference on Secure IT Systems

作者： Mhiri, Ibrahim Boersig, Matthias Stark, Akim Baumgart, Ingmar 1&1 Mail & Media Karlsruhe Germany FZI Res Ctr Informat Technol Karlsruhe Germany

ISBN: (纸本)9783031790065;9783031790072

Fuzzing is an automated testing technique that generates random input to identify software bugs and vulnerabilities by provoking unexpected behavior. Although effective, traditional fuzzing lacks input generation guidance, which often leads to inefficiency and wasted time, especially for complex programs, because many inputs are invalid and are rejected. Grammar-based fuzzers address this problem by generating inputs that match the syntactic structure of the program, although they require expert knowledge to define accurate grammars. Large language Models (LLMs) show remarkable capabilities in natural language processing (NLP), improving efficiency in various domains. These models can be used to generate input for fuzzers, as they can quickly learn or already have familiarity with the required input formats. This paper explores the integration of LLMs with fuzzing methods to streamline directed input generation and thereby increase fuzzing efficiency. We specifically adapt Llama2 for use with American Fuzzy Lop (AFL), focusing on Extensible Markup language (XML) due to its commonality as a structured file format. Our approach demonstrates the potential of LLMs to enhance traditional fuzzing by providing targeted, intelligent input generation. Experimental results show that our approach can achieve up to six times more code coverage after 24 h compared to using AFL alone. Furthermore, in our tests, our method provides up to 50% more coverage than a grammar-based fuzzer.

关键词： Grammar-Based Fuzzing XML Fuzzing Large language Models Llama2 Fine-Tuning Prompt-Tuning

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Social Media User Geolocation Based on Large language Models 2nd

Social Media User Geolocation Based on Large Language Model...

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2nd International conference on Data Security and Privacy Protection, DSPP 2024

作者： Zhang, Meng Luo, Xiangyang Huang, Ningbo Key Laboratory of Cyberspace Situation Awareness of Henan Province Henan Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Zhengzhou China

ISBN: (纸本)9789819785391

In the digital age, social media platforms have amassed a wealth of user-generated content, which contains valuable geographic information. However, the irregularities and noise in user-generated text, have led to suboptimal performance in traditional text-based user geolocation methods. We propose a unsupervised framework for user geolocation based on Large language Models (LLMs), which utilizes the LLMs’ powerful text processing abilities to geolocate users based on user-generated text unsupervisedly. Firstly, preprocess the text using regularization rules and LLM to denoise and normalize user-generated text, thus enhancing data quality. Subsequently, appropriate prompts are designed to guide the knowledgeable LLM in understanding the user text’s geolocating mechanism, thereby profiling users. To refine user geolocation accuracy, five independent positioning iterations are conducted, with the most frequent occurrence identified as the final user location. Through a series of experiments, we have demonstrated the potential of utilizing large language models for processing noisy text and the effectiveness of geolocating users in an unsupervised setting. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Large language model Social media Unsupervised user geolocation User-generated text

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DVM: Towards Controllable LLM Agents in Social Deduction Games

DVM: Towards Controllable LLM Agents in Social Deduction Gam...

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2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Zhang, Zheng Lan, Yihuai Chen, Yangsen Wang, Lei Wang, Xiang Wang, Hao Guangzhou China Singapore Management University Singapore University of Science and Technology of China Hefei China

ISBN: (纸本)9798350368741

Large language Models (LLMs) have advanced the capability of game agents in social deduction games (SDGs). These games rely heavily on conversation-driven interactions and require agents to infer, make decisions, and express based on such information. While this progress leads to more sophisticated and strategic non-player characters (NPCs) in SDGs, there exists a need to control the proficiency of these agents. This control not only ensures that NPCs can adapt to varying difficulty levels during gameplay, but also provides insights into the safety and fairness of LLM agents. In this paper, we present DVM, a novel framework for developing controllable LLM agents for SDGs, and demonstrate its implementation on one of the most popular SDGs, Werewolf. DVM comprises three main components: Predictor, Decider, and Discussor. By integrating reinforcement learning with a win rate-constrained decision chain reward mechanism, we enable agents to dynamically adjust their gameplay proficiency to achieve specified win rates. Experiments show that DVM not only outperforms existing methods in the Werewolf game, but also successfully modulates its performance levels to meet predefined win rate targets. These results pave the way for LLM agents' adaptive and balanced gameplay in SDGs, opening new avenues for research in controllable game agents. © 2025 IEEE.

关键词： controllable agents game agents large language model reinforcement learning

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EventLens: Enhancing Visual Commonsense Reasoning by Leveraging Event-Aware Pretraining and Cross-modal Linking

EventLens: Enhancing Visual Commonsense Reasoning by Leverag...

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2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Ma, Mingjie Yu, Zhihuan Ma, Yichao Li, Guohui Yang, Zhong School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Software Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350368741

Visual Commonsense Reasoning (VCR) is a cognitive task, challenging models to answer visual questions, and to explain the rationale behind their answers. While Large language Models (LLMs) offer potential for this task, VCR's complex scenes require specialized approaches to activate their commonsense reasoning abilities, as existing Multimodal LLMs struggle with VCR's visual events and unique reference tags. To address these challenges, we propose EventLens, which enhances VCR through Event-Aware Pretraining and Cross-modal Linking in Supervised Fine-tuning. First, we introduce a new pretraining stage that emulates human cognitive processes to improve LLM comprehension of complex scenarios. Second, during supervised fine-tuning, we leverage reference tags to explicitly bridge RoI features with text, maintaining semantic integrity across modalities. Additionally, instruct prompts and task-specific adapters help integrate LLMs' knowledge with new commonsense reasoning. Experimental results demonstrate competitive performance with state-of-the-art methods and ablation studies verify the effectiveness of proposed EventLens. © 2025 IEEE.

关键词： Multimedia Understanding Pretrained language Models Visual Commonsense Reasoning

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Large language Model-Based Event Relation Extraction with Rationales 31

Large Language Model-Based Event Relation Extraction with Ra...

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31st International conference on Computational Linguistics, COLING 2025

作者： Hu, Zhilei Li, Zixuan Jin, Xiaolong Bai, Long Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of SciencesSchool of Computer Science and Technology University of Chinese Academy of Sciences

ISBN: (纸本)9798891761964

Event Relation Extraction (ERE) aims to extract various types of relations between different events within texts. Although Large language Models (LLMs) have demonstrated impressive capabilities in many natural language processing tasks, existing ERE methods based on LLMs still face three key challenges: (1) Time Inefficiency: The existing pairwise method of combining events and determining their relations is time-consuming for LLMs. (2) Low Coverage: When dealing with numerous events in a document, the limited generation length of fine-tuned LLMs restricts the coverage of their extraction results. (3) Lack of Rationale: Essential rationales concerning the results that could enhance the reasoning ability of the model are overlooked. To address these challenges, we propose LLMERE, an LLM-based approach with rationales for the ERE task. LLMERE transforms ERE into a question-and-answer task that may have multiple answers. By extracting all events related to a specified event at once, LLMERE reduces time complexity from O(n2) to O(n), compared to the pairwise method. Subsequently, LLMERE enhances the coverage of extraction results by employing a partitioning strategy that highlights only a portion of the events in the document at a time. In addition to the extracted results, LLMERE is also required to generate corresponding rationales/reasons behind them, in terms of event coreference information or transitive chains of event relations. Experimental results on three widely used datasets show that LLMERE achieves significant improvements over baseline methods. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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A Hybrid Deep Learning and natural language processing Model for Plant Ubiquitination Sites Prediction 3rd

A Hybrid Deep Learning and Natural Language Processing Model...

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3rd International conference on Advances in Information and Communication Technology, ICTA 2024

作者： Tran, Thi-Xuan Nguyen, Thi-Tuyen Le, Nguyen Quoc Khanh Nguyen, Van-Nui University of Economics and Business Administration Thai Nguyen Viet Nam University of Information and Communication Technology Thai Nguyen Viet Nam Professional Master Program in Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan

ISBN: (纸本)9783031809422

Protein ubiquitination is a critical post-translational modification involved in numerous biological processes, playing a vital role in regulating both physiological and pathological mechanisms. Despite existing tools for predicting ubiquitination sites, variations in ubiquitination patterns among species remain inadequately understood. This study presents LBiPlantUbi, a novel hybrid deep learning model that incorporates Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) networks combined with natural language processing (NLP) techniques for predicting ubiquitination sites in plants. Our model uniquely learns features directly from raw protein sequences using word embedding and integrates feature extraction and prediction within a single architecture. LBiPlantUbi achieved superior performance with an accuracy of 84%, sensitivity of 88.9%, F1-score of 0.844, MCC of 0.675, and AUC of 0.902 on independent testing sets, outperforming existing methods. This advanced model opens new avenues for studying ubiquitination in plants and other species. The implementation is publicly accessible on GitHub at https://***/nuinvtnu/LBiPlantUbi. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Customer-Based Opinion Analysis Using Clustering and Classification Techniques 5th

Customer-Based Opinion Analysis Using Clustering and Classif...

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5th International conference on Data Science, Machine Learning and Applications

作者： Ahad, Abdul Riyazuddin Sadiq, Jaffar Raju, Basava Lakshmi, Rama Anurag Univ Dept AI Hyderabad India Anurag Univ Dept IT Hyderabad India Sreenidhi Inst Sci & Tech Dept CSE DS Hyderabad India SRKR Engn Coll Dept CSD Bhimavaram India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

This article accentuates the opinion examination in view of the order of flipkart client surveys. Feeling Examination known as Feeling Man-made consciousness or Assessment Mining. Computerized reasoning (artificial intelligence) alludes to the efficient recognizable proof, extraction, measurement, and investigation of emotional states and abstract information utilizing regular language handling and text examination. An application challenge in message mining and computational phonetics research is opinion examination of item audits. Here, the relationship between Flipkart item audits and the rating of the items given by the clients is to be considered. Different machine learning methods are utilized. In the first place, the surveys can be changed into vector portrayal utilizing various strategies, i.e., Sack of-words, and TF-IDF. Then, train the system by applying the Strategic Relapse, XGBoost, and Decision Trees. From that point forward, assess the models utilizing F1-Score. Thus, opinion examination is unmistakably applied to audits, review reactions, web and virtual entertainment, and medical care assets for purposes going from showcasing to client assistance to clinical medication.

关键词： natural language processing Opinion analysis Logistic regression K-mean clustering SVM classification

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