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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161-170 订阅

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Evaluating Large language Models: Challenges, Limitations, and Future Directions 10

Evaluating Large Language Models: Challenges, Limitations, a...

引用

10th International conference on Digital Arts, Media and Technology and 8th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2025

作者： Li, Li-Hua Nugroho, Agus Cahyo Chaoyang University of Technology College of Informatics Department of Information Management Taichung Taiwan

ISBN: (纸本)9798331543273

Large language Models (LLMs) have transformed natural language processing (NLP) by providing previously unheard-of capabilities in text production, translation, and comprehension. Large language Models have advanced rapidly. Nevertheless, there are several obstacles and restrictions associated with the implementation and assessment of these models. This study offers a thorough analysis of the situation of LLMs today, with an emphasis on the methods for assessing their effectiveness and significance. Key issues like prejudice, moral dilemmas, scalability, and transparency are noted, and the shortcomings of the current frameworks for evaluation are also covered. Our goal in examining these problems is to identify the areas that require improvement. In addition, we suggest possible direction for future investigation, stressing the importance of inclusive datasets, improved interpretability, and stronger assessment criteria. In addition to guiding the creation of more morally and effectively constructed AI systems, our mission is to promote a greater understanding of LLMs. © 2025 IEEE.

关键词： natural language processing systems

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Spatiotemporal-Aware Visual Captioning using Vision-language Pre-Training Model

Spatiotemporal-Aware Visual Captioning using Vision-Language...

引用

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

作者： Wu, Shuai Yang, Weidong Wu, Shuyan School of Computer Science Fudan University Shanghai China Faculty of Electronic and Information Engineering Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798350368741

Current visual captioning technologies typically transform 3D/2D visual information into one-dimensional sequential data and employ language models to generate corresponding descriptions. This approach, however, compromises the spatiotemporal information in visual data, making it difficult for models to capture temporal variations and the relative spatial relationships between objects. To address this issue, we propose STPos-VC, a pre-trained vision-language model that maps visual information from the visual vector space to the textual vector space through a visual-text mapper and generates natural language descriptions using a decoder. The mapper incorporates three-dimensional rotational position encoding, which effectively preserves the relative spatiotemporal positional relationships. Furthermore, we pre-train the model on a mixed dataset comprising images and videos through a visual question-answering framework, enabling the model to perform well even with small sample sizes. Experimental results across multiple datasets demonstrate that, compared to existing methods, STPos-VC achieves superior performance in both general-purpose and domain-specific applications. © 2025 IEEE.

关键词： Multimodality Pre-training Spatiotemporal position encoding Visual language Model

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Exploring the Extractive Method of Text Summarization

Exploring the Extractive Method of Text Summarization

引用

International conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Dam, Mehuli Roy, Sumanta Sarkar, Darothi Institute of Engineering and Management Kolkata India University of Engineering and Management Kolkata India

ISBN: (纸本)9789819613472

Whenever we collect the main points from a different documents or from a big paragraphs, it becomes quite a tough and a tedious task to summarize it. Extractive text summarization is a technique which provides a brief summary extracting significant sentences and main features from a particular text, document or a paragraph. In this paper, a comparative study and a literature survey have been performed on extractive learning-based text summarization which includes classification of different algorithms, introducing techniques to compare between current models and traditional models and an analysis and observation based on these criteria that which is a best method. Text summarization using natural language processing (NLP) is indeed a valuable tool for organizations dealing with vast amounts of customer feedback and data. It helps in extracting meaningful insights from large volumes of text, making it easier for people to understand semantic relationship behind that text. This survey mainly focusses on a wide range of study that includes feature representation to sentence selection and tells that how to generate a summary using machine learning models, graph methods, frequency driven approach, etc. The overall survey will definitely help us how to effectively and easily handle large sets of data in building an effective natural language processing applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： natural language processing systems

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Intelligent Question Generator System using natural language processing 3

Intelligent Question Generator System using Natural Language...

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3rd International conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Sangeetha, V. Pani, Ishwariya Sharma, Ankit Vishwanatha Kanchana, C. Siddarth, R. Srikar Ramaiah Institute of Technology Department of Cse Bangalore India Ramaiah Institute of Technology Department of Iem Bangalore India

ISBN: (纸本)9798331515911

Question generation system in most educational institutes is not automated. There are various methods in which objective and subjective question generation is existing, but not those algorithms are not very accurate. Automation of question generation systems can assist learners to discover the levels of their expertise and increase their knowledge by helping them in clarifying their queries. It can also be widely used in various other fields such as for placement activities. In this paper, the proposed model main objective is to design and implement an automated question generator using natural language processing(NLP) techniques. The proposed system will use T5 transformer and Bidirectional Encoder Representations from Transformers (BERT) technique. The performance of the automated question generator is evaluated using accuracy, relevance, and fluency of the generated questions. The results of the system will Incorporate user feedback to improve the performance and effectiveness of the question generator. © 2025 IEEE.

关键词： Adversarial machine learning

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Whether Current Large language Models is Suitable for Multimodal Aspect-based Sentiment Analysis? 24

Whether Current Large Language Models is Suitable for Multim...

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2nd International conference on Advances in Artificial Intelligence and Applications, AAIA 2024

作者： Song, Shezheng Zhao, Shan National University of Defense Technology Hunan Changsha China Hefei University of Technology Hefei China

ISBN: (纸本)9798400712883

The Definition of Multimodal Aspect-Based Sentiment Analysis (MABSA) task is to extract aspects (i.e. objects) and the corresponding sentiment from text and image. Recently, the traditional MABSA methods based on the small model have shown effectiveness and propose different models. There is still uncertainty about the adaptability of large language models (LLMs) to MABSA. With the rapid development of large language models, more and more researchers pay attention to LLMs. Recent advances in LLMs, such as Llama2, LLaVA, and ChatGPT, demonstrate good capabilities in most tasks. However, the performance of these LLMs in complex and fine-grained tasks, such as MABSA, is not sure. In this study, we investigate the suitability of LLMs for MABSA. To this end, we construct a framework to evaluate Llama2, LLaVA, and ChatGPT on MABSA tasks and compare them with state-of-the-art methods based on small model. Through our experimental results, we found that LLM exhibits strong capabilities in most multimodal understanding scenarios. However, they face significant challenges in implementing complex MABSA tasks, and their accuracy is not satisfactory. Additionally, the common problem with LLM is the high inference time consumption. Based on our experimental findings, we discussed the limitations of current LLM and looked forward to future research directions in LLM to consider enhancing its capabilities in multimodal emotion analysis. © 2024 Copyright held by the owner/author(s).

关键词： natural language processing systems

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SUVAD: Semantic Understanding Based Video Anomaly Detection Using MLLM

SUVAD: Semantic Understanding Based Video Anomaly Detection ...

引用

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

作者： Gao, Shibo Yang, Peipei Huang, Linlin Beijing Jiaotong University China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China

ISBN: (纸本)9798350368741

Video anomaly detection (VAD) aims at detecting anomalous events in videos. Most existing VAD methods distinguish anomalies by learning visual features of the video, which usually face several challenges in real-world applications. First, these methods are mostly scene-dependent, whose performances degrade obviously once the scene changes. Second, these methods are incapable of giving explanations to the detected anomalies. Third, these methods cannot adjust definitions of normal or abnormal events during test time without retraining the model. One important reason for the drawbacks is that these visual-based methods mainly detect anomalies by fitting visual patterns rather than semantically understanding the events in videos. In this paper, we propose a training-free method named Semantic Understanding based Video Anomaly Detection (SUVAD) using multi-modal large language model (MLLM). By exploiting MLLMs to generate detailed texture descriptions for videos, SUVAD achieves semantic video understanding, and then detects anomalies directly by large language models. We also designed several techniques to mitigate the hallucination problem of MLLMs. Compared to the methods based on visual features, SUVAD obtains obviously better scene generalization, anomaly interpretability, and the ability of flexible adjustment of anomaly definitions. We evaluate our method on five mainstream datasets. The results show that SUVAD achieves the best performance among all the training-free methods. © 2025 IEEE.

关键词： Multi-modal Large-language Model Training-free Video Anomaly Detection

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Automated Implementation Scheme for Low-Code Development Based on Large language Models 24

Automated Implementation Scheme for Low-Code Development Bas...

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4th International conference on Signal processing and Communication Technology, SPCT 2024

作者： Sun, Tao Lan, Xingfei Jing, Guoshuai Zhang, Judong Jin, Yuchao Xu, Lexi Yang, Lei JDCloud *** Inc. Beijing China Research Institute China United Network Communications Corporation Beijing China

ISBN: (纸本)9798400710636

This paper presents an implementation scheme that integrates low-code platforms with artificial intelligence (AI) technology to enhance the development process. By leveraging AI for automatic code generation, intelligent component recommendations, and error detection/repair, the efficiency of low-code development is significantly improved. The combination of natural language requirements and visual drag-and-drop operations lowers the technical barrier. A new model for evaluating efficiency improvement is introduced and validated using actual business data, demonstrating the feasibility and effectiveness of this integration. © 2024 Copyright held by the owner/author(s).

关键词： Codes (symbols)

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Enhancing Causal Relationship Detection Using Prompt Engineering and Large language Models 9

Enhancing Causal Relationship Detection Using Prompt Enginee...

引用

Joint Workshop of the 9th Financial Technology and natural language processing, FinNLP 2025, the 6th Financial Narrative processing, FNP 2025, and the 1st Workshop on Large language Models for Finance and Legal, LLMFinLegal 2025, co-located with the 31st International conference on Computational Linguistics, COLING 2025

作者： Chatwal, Pulkit Agarwal, Amit Mittal, Ankush Rajiv Gandhi Institute of Petroleum Technology Jais India AICoE Wells Fargo International Solutions Private Limited Bangalore India COER University Roorkee India

ISBN: (纸本)9798891762091

Causal relationships are essential for understanding financial systems, offering insights into market dynamics, regulatory impacts, and organizational decisions. Traditional approaches to detecting causality in financial texts often struggle with the nuanced and domain-specific language of such disclosures. The FinCausal 2025 shared task provided a benchmark for evaluating advanced methods in multilingual financial causality detection. In this work, we employ prompt engineering with large language models (LLMs) to identify causal relationships in financial disclosures across languages. Our system achieved Semantic Answer Similarity (SAS) scores of 0.9086 in English and 0.8987 in Spanish, with Exact Match (EM) scores of 0.5110 and 0.0619, respectively. These results demonstrate the potential of LLMs for tackling the challenges of causality detection in multilingual and domain-specific contexts, while also identifying areas for future refinement. © 2025 Association for Computational Linguistics.

关键词： Decentralized finance

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LTNER: Large language Model Tagging for Named Entity Recognition with Contextualized Entity Marking 27th

LTNER: Large Language Model Tagging for Named Entity Recogni...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Yan, Faren Yu, Peng Chen, Xin Computer Network Information Center Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031784941

The use of LLMs for natural language processing has become a popular trend in the past two years, driven by their formidable capacity for context comprehension and learning, which has inspired a wave of research from academics and industry professionals. However, for certain NLP tasks, such as NER, the performance of LLMs still falls short when compared to supervised learning methods. In our research, we developed a NER processing framework called LTNER (Code is available at https://***/YFR718/LTNER) that incorporates a revolutionary Contextualized Entity Marking Gen Method. By leveraging the cost-effective GPT-3.5 coupled with context learning that does not require additional training, we significantly improved the accuracy of LLMs in handling NER tasks. The F1 score on the CoNLL03 dataset increased from the initial 85.9% to 91.9%, approaching the performance of supervised fine-tuning. This outcome has led to a deeper understanding of the potential of LLMs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： natural language processing systems

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A natural language processing (NLP) Evaluation Framework and its Application in Analyzing Online Public Opinion of Nursing Care Food Market 24

A Natural Language Processing (NLP) Evaluation Framework and...

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8th International conference on Computing and Data Analysis, ICCDA 2024

作者： Chen, Zhengyang Fang, Pan Wang, Hongwu Chen, Huiling Public Health Science and Engineering College Tianjin University of Traditional Chinese Medicine Tianjin Tianjin China School of Public Health Nanjing Medical University Jiangsu Nanjing China

ISBN: (纸本)9798400710445

The paper presents a comprehensive natural language processing (NLP) evaluation framework for online public opinion analysis, addressing the limitations of singular NLP techniques. It highlights the significance of public opinion analysis in the era of big data and reviews commonly employed NLP methods. The proposed framework offers universality, comprehensiveness, and flexibility, aiding managers and users in better understanding product sentiments and purchasing behaviors for informed decision-making. The proposed NLP evaluation framework includes five stages, Data Collection, Qualitative Analysis, Sentiment Analysis, Semantic Relationship Mining, and Comprehensive Analysis. It involves techniques like Sentiment Analysis, Topic Modeling, and Co-Occurrence Networks to analyze sentiment and identify semantic relationships. Our case study on nursing care food market validates the framework’s feasibility, proposing enhancements in taste, gifting nature, pricing, and product strategy. Future research will expand its applicability across domains and refine inter-stage connections while maintaining flexibility. © 2024 Copyright held by the owner/author(s).

关键词： natural language processing

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