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Extending Token Computation for LLM Reasoning 24

Extending Token Computation for LLM Reasoning

引用

8th international conference on Computer Science and Artificial Intelligence, CSAI 2024

作者： Li, Bingli Vargas, Danilo Vasconcellos Laboratory of Intelligent Systems The Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka Fukuoka Japan

ISBN: (纸本)9798400718182

Large language Models (LLMs) are pivotal in advancing natural language processing but often struggle with complex reasoning tasks due to inefficient attention distributions. In this paper, we explore the effect of increased computed tokens on LLM performance and introduce a novel method for extending computed tokens in the Chain-of-Thought (CoT) process, utilizing attention mechanism optimization. By fine-tuning an LLM on a domain-specific, highly structured dataset, we analyze attention patterns across layers, identifying inefficiencies caused by non-semantic tokens with outlier high attention scores. To address this, we propose an algorithm that emulates early layer attention patterns across downstream layers to re-balance skewed attention distributions and enhance knowledge abstraction. Our findings demonstrate that our approach not only facilitates a deeper understanding of the internal dynamics of LLMs but also significantly improves their reasoning capabilities, particularly in non-STEM domains. Our study lays the groundwork for further innovations in LLM design, aiming to create more powerful, versatile, and responsible models capable of tackling a broad range of real-world applications. © © 2024 Copyright held by the owner/author(s).

关键词： Semantics

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AI Chatbots as Virtual Teaching Assistants 27th

AI Chatbots as Virtual Teaching Assistants

引用

27th international conference on Interactive Collaborative Learning, ICL 2024

作者： Kasmaee, Ali Shiraee Mahyar, Hamidreza Faculty of Engineering McMaster University HamiltonON Canada

ISBN: (纸本)9783031835193

Recent advancements in artificial intelligence (AI), particularly in natural language processing (NLP), have led to the development of sophisticated chatbots like ChatGPT. These chatbots excel in answering questions and can significantly improve the learning experience by providing immediate feedback and support to students. They enable continuous learning outside traditional classrooms by understanding and responding to questions with human-like proficiency. However, their responses are limited to the information they were trained on. In this study, we employ the Retrieval-Augmented Generation technique to enable these chatbots to access and retrieve relevant information from external knowledge sources (such as course material) to improve the generation capabilities of these models as virtual teaching assistants. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Chatbots

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CogniDual Framework: Self-Training Large language Models within a Dual-System Theoretical Framework for Improving Cognitive Tasks

CogniDual Framework: Self-Training Large Language Models wit...

引用

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

作者： Deng, Yongxin Qiu, Xihe Tan, Xiaoyu Qu, Chao Pan, Jing Cheng, Yuan Xu, Yinghui Chu, Wei School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai China Co. Ltd. Shanghai China School of Art Design and Architecture Monash University Melbourne Australia Artificial Intelligence Innovation and Incubation Institute Fudan University Shanghai China

ISBN: (纸本)9798350368741

Cognitive psychology investigates perception, attention, memory, language, problem-solving, decision-making, and reasoning. Kahneman's dual-system theory elucidates the human decision-making process, distinguishing between the rapid, intuitive System 1 and the deliberative, rational System 2. Recent advancements have positioned large language Models (LLMs) as formidable tools nearing human-level proficiency in various cognitive tasks. Nonetheless, the presence of a dual-system framework analogous to human cognition in LLMs remains unexplored. This study introduces the CogniDual Framework for LLMs (CFLLMs), designed to assess whether LLMs can, through self-training, evolve from deliberate deduction to intuitive responses, thereby emulating the human process of acquiring and mastering new information. Our findings reveal the cognitive mechanisms behind LLMs' response generation, enhancing our understanding of their capabilities in cognitive psychology. Practically, self-trained models can provide faster responses to certain queries, reducing computational demands during inference. © 2025 IEEE.

关键词： cognitive psychology language processing large language model

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Chatbot for Start-Ups and Entrepreneurs with Machine Learning and natural language processing Techniques

Chatbot for Start-Ups and Entrepreneurs with Machine Learnin...

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2025 international conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Jegan, P. Sidharth, N. Naren Akilandeswari, V. Perumalraja, R. Velammal College of Engineering and Technology Department of Cse Madurai India Velammal College of Engineering and Technology Madurai India

ISBN: (纸本)9798331509828

In the ever-digitalized environment, entrepreneurs encounter challenges to engage their customers while managing business operations. The study investigates the development of a chatbot specifically for entrepreneurs designed to enhance customer interactions and support. Based on the technique of natural language processing and machine learning, this chatbot can understand and explain hundreds of questions with personalized assistance tailored to user interactions. The study emphasizes that contextual comprehension and adaptability should be integral elements in the design of a chatbot to please the diverse needs of entrepreneurs. Various data from business scenarios-especially customer feedback and market trends-explain how the chatbot is constantly improved to maintain responsiveness and vitality. The study appraised the chatbot's performance through metrics of user satisfaction and engagement rates, significantly pointing out its potential to streamline communication, reduce response times, and enhance the total customer experience. Through the integration of this innovative solution in their operations, entrepreneurs have the possibility to drive responsiveness and agility, leading to business growth and strong customer relationships in a competitive market. © 2025 IEEE.

关键词： Sales

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Tokenization's Potential for Santhali language processing 3rd

Tokenization's Potential for Santhali Language Processing

引用

3rd international conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Ohm, Anand Kumar Panday, Aman Kumar Singh, Koushlendra Kumar Machine Vision and Intelligence Lab Department of CSE National Institute of Technology Jamshedpur Jharkhand India

ISBN: (纸本)9789819743582

Santhali is one of the popular local languages and is mainly spoken by tribal people of States like Jharkhand, Odisha, and West Bengal, as well as a few other parts of the country. Tokenization is one of the major challenges in natural language processing (NLP). Tokenization reduces the length of sentences and paragraphs. There are various methods in use to tokenize different languages depending on the writing and structure of the language. Tokenization is important in many tasks like machine translation, text summarization, chatbot creation, and spam detection. In natural language processing (NLP) most of the Indian spoken languages have high resource availability and have improved technologically in various fields. Still, a low-resource language like SANTHALI does not make much of its existence in technology. The present work is based on the tokenization of Santhali. All available tokenizers to tokenize in the Santhali language have been explored. Ol-Chiki font has been used for the present study. A detailed comparison of tokenization has been represented in the manuscript in the present study. It has been observed that for Santhali a writing system is alphabetic and most of the characters are made of real-world symbols. spaCy model performs better than other available tokenizer models based on the mechanism of the Santhali language. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： natural language processing systems

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Tunisian Arabic Understanding: Resources Analysis and Evaluation 3rd

Tunisian Arabic Understanding: Resources Analysis and Evalu...

引用

3rd international conference on language processing and knowledge Management, LPKM 2024

作者： Besdouri, Fatma Zahra Zribi, Inès Belguith, Lamia Hadrich ANLP Research group MIRACL Labratory University of Sfax Sfax Tunisia University of Monastir Monastir Tunisia

ISBN: (纸本)9783031850660

natural language Understanding (NLU) is considered a core component in implementing Dialogue Systems (DS). This paper introduces an NLU module tailored to the Tunisian Dialect (TD) that can further be integrated with Automatic Speech Recognition (ASR), a Dialogue Manager (DM), a natural language Generator (NLG), and a Speech Synthesis (SS) module to build a fully working DS. Designed specifically for academic and research domain, our closed-domain TUN-NLU corpus undergoes preprocessing, including scraping, automatic normalization, data augmentation, and annotation. We employed the RASA NLU model (https://***/) to evaluate our corpus. The NLU model encompasses tokenization, featurization, intent detection, and entity recognition. Evaluation using standard metrics, including accuracy and F1 score, yielded promising results: 93% accuracy and 92% F1 score for intent detection, and 94% accuracy and 91% F1 score for entity recognition. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： natural language processing systems

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DEBT: Enhancing Entity Alignment in knowledge Graphs through Description Enrichment and Bootstrap Training

DEBT: Enhancing Entity Alignment in Knowledge Graphs through...

引用

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

作者： Xiang, Ting Zhang, Jiapeng Chen, Changjian Tang, Zhuo College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9798350368741

Entity alignment has emerged as a powerful technique for integrating knowledge graphs, facilitating the fusion of heterogeneous knowledge into a unified graph. The state-of-the-art methods combine both graph structures and side information for effective entity alignment. However, they neglect low-quality issues in data. Specifically, the emerging knowledge graphs in diverse fields amass a wealth of entities that lack not only adequate descriptions but also annotated alignments. These two limitations lead to the overfitting problem and degrade the alignment performance. To tackle these challenges, we propose DEBT, an innovative approach that systematically enhances entity alignment. It first enriches the descriptions of entities by aggregating their neighbors and attributes. Then, a bootstrap strategy is utilized to expand the training set by incorporating entity pairs with similarity scores exceeding a dynamically decreasing threshold. Experimental results demonstrate that our method achieves the state-of-the-art accuracy while reducing the number of annotated entity alignment pairs. © 2025 IEEE.

关键词： bootstrap strategy description enrichment entity alignment knowledge graph

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Can Large language Models Grasp Event Signals? Exploring Pure Zero-Shot Event-based Recognition

Can Large Language Models Grasp Event Signals? Exploring Pur...

引用

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

作者： Yu, Zongyou Qu, Qiang Chen, Xiaoming Wang, Chen School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing China School of Computer Science The University of Sydney Sydney Australia

ISBN: (纸本)9798350368741

Recent advancements in event-based zero-shot object recognition have demonstrated promising results. However, these methods heavily depend on extensive training and are inherently constrained by the characteristics of CLIP. To the best of our knowledge, this research is the first study to explore the understanding capabilities of large language models (LLMs) for event-based visual content. We demonstrate that LLMs can achieve event-based object recognition without additional training or fine-tuning in conjunction with CLIP, effectively enabling pure zero-shot event-based recognition. Particularly, we evaluate the ability of GPT-4o/4turbo and two other open-source LLMs to directly recognize event-based visual content. Extensive experiments are conducted across three benchmark datasets, systematically assessing the recognition accuracy of these models. The results show that LLMs, especially when enhanced with well-designed prompts, significantly improve event-based zero-shot recognition performance. Notably, GPT-4o outperforms the compared models and exceeds the recognition accuracy of state-of-the-art event-based zero-shot methods on N-ImageNet by five orders of magnitude. © 2025 IEEE.

关键词： event camera large language model object recognition

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Research on Methods of Large language Models in the Field of Sensitive Data Governance 24

Research on Methods of Large Language Models in the Field of...

引用

2nd international conference on Artificial Intelligence, Systems and Network Security, AISNS 2024

作者： Zhang, Zhixian Bai, Yin Yu, Xiaoting Wang, Yu Yang, Siqi Yang, Shuo China Mobile Chengdu Institute of Research and Development Sichuan Chengdu China

ISBN: (纸本)9798400711237

With the rapid advancement of big data and artificial intelligence technologies, the governance of sensitive data has become a critical and pressing issue. Traditional data de-identification approaches exhibit inefficiencies and a lack of flexibility when processing unstructured data. This paper introduces a sensitive data governance method based on Large language Models, leveraging the natural language processing capabilities of LLMs to construct prompts that enable the identification of fields requiring de-identification and the selection of appropriate de-identification methods. Furthermore, it harnesses the LLMs to automatically generate Python processing code, thus circumventing the limitations of singular and inflexible built-in de-identification functionalities in big data platforms. The objective of this study is to reduce the technical barriers in sensitive data governance and to enhance the flexibility and efficiency of data processing. The paper provides a detailed exposition of the technical implementation of this method, encompassing multiple stages such as data identification, instruction processing, model invocation, result adjudication, and data caching, and discusses its advantages and potential value in practical applications. © 2024 Copyright held by the owner/author(s).

关键词： Data assimilation

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A Dataset of Semantically Related Multiword Terms of the Electrical engineering Domain 24

A Dataset of Semantically Related Multiword Terms of the Ele...

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8th international conference on natural language processing and Information Retrieval, NLPIR 2024

作者： Druselmann, Maria Harbusch, Karin Institute for Computational Visualistics University of Koblenz Koblenz Germany

ISBN: (纸本)9798400717383

This paper presents the EEMWT dataset, a collection of 1873 triplets of co-hyponymic multiword terms of the electrical engineering domain. Each triplet combines an anchor term with closely and distantly related co-hyponymic terms. The degree of semantic relatedness is determined by the presence or absence of shared domain-specific semantic features. The primary purpose of this dataset is to serve as a tool for evaluating domain-specific language representation models and embedding vector pooling techniques by assessing the semantic relatedness of co-hyponymic multiword terms. The novelty of the dataset lies in the development approach, which replaces intuition-based scaling of relatedness with linguistically justified semantic-feature-based judgment. Furthermore, the traditional method of calculating interrater agreement rate is replaced with a statistical analysis of vector space distance between low and highly related terms. These innovations in dataset construction and evaluation have the potential to significantly reduce the costs associated with expert questionnaires in dataset development. © 2024 Copyright held by the owner/author(s).

关键词： dataset of semantically related terms

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