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Context-Driven Chatbot Development: Leveraging Zephyr-7b with RAG for Improved Response Accuracy

Context-Driven Chatbot Development: Leveraging Zephyr-7b wit...

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2025 international conference on Emerging Technologies for Intelligent Systems, ETIS 2025

作者： Jervas, Sandra Jacob, Chinnu Sundar, Sumod Deepasree Varma, P. Centre for Ai Tkm College of Engineering Kollam India Knowledge Office Ict Academy of Kerala Trivandrum India

ISBN: (纸本)9798331507541

In recent years, the demand for AI-driven conversational agents has increased significantly across various industries. Traditional generative models, while capable of producing human-like responses, often struggle with factual accuracy and context retention. Retrieval Augmented Generation (RAG) presents a novel approach to enhance the performance of AI chatbots by combining the strengths of both retrieval-based and generative models. The paper focuses on the development of an AI-driven chatbot using a RAG framework, integrating the Zephyr-7b model, to serve the needs of the ICT Academy of Kerala (ICTAK). The chatbot is engineered to deliver precise and contextually relevant responses to user queries by integrating advanced language models with structured data extracted from the ICTAK website. The data, which was meticulously scraped and processed, ensures that the chatbot's knowledge base remains current and accurately reflects the ICTAK's services and operations. A critical challenge addressed by this work is the issue of hallucinations in large language models (LLMs), where models may generate seemingly plausible yet incorrect or irrelevant information. To counteract this, the paper employs various chunking methods that enhance the chatbot's capability to retrieve and generate accurate responses. This AI Chatbot incorporates cutting-edge techniques in natural language processing, including document retrieval, context compression, and re-ranking. This multi-faceted approach ensures that the chatbot not only provides accurate responses but also delivers them in a contextually relevant manner. © 2025 IEEE.

关键词： Chatbots

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CoF: Coarse to Fine-Grained Image Understanding for Multi-modal Large language Models

CoF: Coarse to Fine-Grained Image Understanding for Multi-mo...

引用

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

作者： Wang, Yeyuan Gao, Dehong Li, Bin Long, Rujiao Yi, Lei Cai, Xiaoyan Yang, Libin Zhang, Jinxia Yu, Shanqing Xuan, Qi School of Automation Northwestern Polytechnical University Shaanxi Xi'an China School of Cybersecurity Northwestern Polytechnical University Shaanxi Xi'an China Alibaba Group Zhejiang Hangzhou China The Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing210096 China Advanced Ocean Institute Southeast University Nantong226010 China Zhejiang University of Technology Zhejiang Hangzhou China Binjiang Institute of Artificial Intelligence Zhejiang Hangzhou China

ISBN: (纸本)9798350368741

The impressive performance of Large language Model (LLM) has prompted researchers to develop Multi-modal LLM (MLLM), which has shown great potential for various multi-modal tasks. However, current MLLM often struggles to effectively address fine-grained multi-modal challenges. We argue that this limitation is closely linked to the models' visual grounding capabilities. The restricted spatial awareness and perceptual acuity of visual encoders frequently lead to interference from irrelevant background information in images, causing the models to overlook subtle but crucial details. As a result, achieving fine-grained regional visual comprehension becomes difficult. In this paper, we break down multi-modal understanding into two stages, from Coarse to Fine (CoF). In the first stage, we prompt the MLLM to locate the approximate area of the answer. In the second stage, we further enhance the model's focus on relevant areas within the image through visual prompt engineering, adjusting attention weights of pertinent regions. This, in turn, improves both visual grounding and overall performance in downstream tasks. Our experiments show that this approach significantly boosts the performance of baseline models, demonstrating notable generalization and effectiveness. Our CoF approach is available online at https://***/Gavin001201/CoF. © 2025 IEEE.

关键词： fine-grained understanding prompt engineering vision and language

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Empirical Analysis of Prompt-Based LLMs for Structural Classification of Indian Legal Judgements 5

Empirical Analysis of Prompt-Based LLMs for Structural Class...

引用

5th IEEE international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Chandana, Sidda Siri Bysani, Hemanth Garg, Shubham Prasoona, Ghanana Gupta, Deepa Sinha, Sushant Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bangalore India India Kanoon Karnataka Bangalore Urban India

ISBN: (纸本)9798350356236

Legal judgment is a decision formally issued by a court as a conclusion to legal proceedings, analyzing the court's findings, reasoning, and rulings on the matters brought before it. This study explores the analysis of these legal judgments and classifies these judgments with the help of large language models and incorporating prompting techniques. The dataset considered in this study facilitates comprehensive model training and evaluation by assessing performance metrics such as Accuracy, Precision, Recall, and F1-score both Macro and Weighted. The Mistral model, of version OpenHermes-2.5-Mistral-7B-16k-GGUF, achieved an accuracy score of 0.61, performing best using the 0- shot prompting technique. This research aims to provide insights into the efficacy of these Large language Models in automating legal research tasks and streamlining document processing workflows. The findings shed light on how Large language Models interact with and reason through documents in the complex legal domain, offering valuable implications for practitioners and researchers. © 2025 IEEE.

关键词： natural language processing systems

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Swift Cross-Dataset Pruning: Enhancing Fine-Tuning Efficiency in natural language Understanding 31

Swift Cross-Dataset Pruning: Enhancing Fine-Tuning Efficienc...

引用

31st international conference on Computational Linguistics, COLING 2025

作者： Nguyen, Binh-Nguyen He, Yang CFAR Agency for Science Technology and Research Singapore IHPC Agency for Science Technology and Research Singapore VNU University of Engineering and Technology Hanoi Viet Nam

ISBN: (纸本)9798891761964

Dataset pruning aims to select a subset of a dataset for efficient model training. While data efficiency in natural language processing has primarily focused on within-corpus scenarios during model pre-training, efficient dataset pruning for task-specific fine-tuning across diverse datasets remains challenging due to variability in dataset sizes, data distributions, class imbalance and label spaces. Current cross-dataset pruning techniques for fine-tuning often rely on computationally expensive sample ranking processes, typically requiring full dataset training or reference models. We address this gap by proposing Swift Cross-Dataset Pruning (SCDP). Specifically, our approach uses TF-IDF embeddings with geometric median to rapidly evaluate sample importance. We then apply dataset size-adaptive pruning to ensure diversity: for smaller datasets, we retain samples far from the geometric median, while for larger ones, we employ distance-based stratified pruning. Experimental results on six diverse datasets demonstrate the effectiveness of our method, spanning various tasks and scales while significantly reducing computational resources. Source code is available at: https://***/he-y/NLP-Dataset-Pruning. © 2025 Association for Computational Linguistics.

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AgriTalk: Revolutionizing Farming with a Multilingual Chatbot 8

AgriTalk: Revolutionizing Farming with a Multilingual Chatbo...

引用

8th international conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Abhishek, M.V. Sreekar, A Jaya Mohith, D.M. Vekkot, Susmitha Bhavana, V. Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798331520762

Agriculture plays a vital role in the economy of many nations, especially in regions where a significant portion of the population depends on it for their livelihood. Despite its importance, linguistic and technological barriers often hinder access to accurate agricultural information. This article introduces a multilingual chatbot designed to address agricultural queries efficiently. Leveraging natural language processing and deep learning models, this multi-language chatbot accommodates English, Hindi, Malayalam, Kannada, and Telugu. Since the query is matched with relevant responses using cosine similarity on the sentence-transformers architecture, the system ensures that relevant responses are given. With IndicTrans2 (AI4Bharat), a robust neural machine translation model tailored for Indian languages, the facility provides a single-machine foundation on which translations about 22 Indian languages with subsequent additional features-text-to-speech functionality enhances accessibility to varied users. The system design, data preprocessing, implementation, and evaluation metrics are covered in this study, which also shows how it might help farmers and other agricultural stakeholders. © 2025 IEEE.

关键词： Chatbots

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KAN v.s. MLP for Offline Reinforcement Learning

KAN v.s. MLP for Offline Reinforcement Learning

引用

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

作者： Guo, Haihong Li, Fengxin Li, Jiao Liu, Hongyan School of Information Renmin University of China China Institute of Medical Information Medical Library Chinese Academy of Medical Sciences Peking Union Medical College China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Economics and Management Tsinghua University China

ISBN: (纸本)9798350368741

Kolmogorov-Arnold Networks (KAN) is an emerging neural network architecture in machine learning. It has greatly interested the research community about whether KAN can be a promising alternative to the commonly used Multi-Layer Perceptions (MLP). Experiments in various fields demonstrated that KAN-based machine learning can achieve comparable if not better performance than MLP-based methods, but with much smaller parameter scales and are more explainable. In this paper, we explore the incorporation of KAN into the actor and critic networks for offline reinforcement learning (RL). We evaluated the performance, parameter scales, and training efficiency of various KAN and MLP-based conservative Q-learning (CQL) on the classical D4RL benchmark for offline RL. Our study demonstrates that KAN can achieve performance close to the commonly used MLP with significantly fewer parameters. This allows us to choose the base networks according to the offline RL task requirements. © 2025 IEEE.

关键词： KAN Kolmogorov-Arnold networks MLP multilayer perceptrons offline reinforcement learning

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LLM Gesticulator: Leveraging Large language Models for Scalable and Controllable Co-Speech Gesture Synthesis 8

LLM Gesticulator: Leveraging Large Language Models for Scala...

引用

8th international conference on Computer Graphics and Virtuality, ICCGV 2025

作者： Pang, Haozhou Ding, Tianwei He, Lanshan Tao, Ming Zhang, Lu Gan, Qi Soul AI Soulgate Technology Co. Ltd. Shanghai China

ISBN: (纸本)9781510689213

In this work, we present LLM Gesticulator, an LLM-based audio-driven co-speech gesture generation framework that synthesizes full-body animations that are rhythmically aligned with the input audio while exhibiting natural movements and editability. Compared to previous work, our model demonstrates substantial scalability. As the size of the backbone LLM model increases, our framework shows proportional improvements in evaluation metrics (a.k.a. scaling law). Our method also exhibits strong controllability where the content, style of the generated gestures can be controlled by text prompt. To the best of our knowledge, LLM gesticulator is the first work that use LLM on the co-speech generation task. Evaluation with existing objective metrics and user studies indicate that our framework outperforms prior works. © 2025 SPIE.

关键词： Scalability

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PsSR: Hybrid Path Selection Mechanism for Efficient Image Super-Resolution

PsSR: Hybrid Path Selection Mechanism for Efficient Image Su...

引用

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

作者： Ding, Cheng Zhao, ZhongQiu Zhao, Yang Hefei University of Technology School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9798350368741

In practical applications, large images are processed in patches, many of which are simple and smooth, making them suitable for lighter network processing. This paper proposes a hybrid path selection mechanism that enables the dynamic routing of patches through the network, achieving a balance between accuracy and computational efficiency. The proposed method develops a dual-branch architecture, in which the two branches keep the same structural design, but differ in the number of channels. An efficient classification module is integrated for directing patches to the appropriate branches within the model. In addition, within each branch, we introduce an early exit strategy to further reduce the calculation cost. Subsequently, we enhance the restoration capabilities of the lightweight branch through channel and spatial feature distillation. Experimental results demonstrate that our Hybrid Path Selection based Super-Resolution (PsSR) mechanism effectively reduces computational costs while preserving the integrity of the backbone performance. © 2025 IEEE.

关键词： dual-branch knowledge distillation path selection super-resolution

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An Attentive Dual-Encoder Framework Leveraging Multimodal Visual and Semantic Information for Automatic OSAHS Diagnosis

An Attentive Dual-Encoder Framework Leveraging Multimodal Vi...

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

作者： Wei, Yingchen Qiu, Xihe Tan, Xiaoyu Huang, Jingjing Chu, Wei Xu, Yinghui Qi, Yuan School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai China Co. Ltd. China ENT Institute Department of Otorhinolaryngology Eye & ENT Hospital Fudan University Shanghai China Artificial Intelligence Innovation and Incubation Institute Fudan University Shanghai China

ISBN: (纸本)9798350368741

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a common sleep disorder caused by upper airway blockage, leading to oxygen deprivation and disrupted sleep. Traditional diagnosis using polysomnography (PSG) is expensive, time-consuming, and uncomfortable. Existing deep learning methods using facial image analysis lack accuracy due to poor facial feature capture and limited sample sizes. To address this, we propose a multimodal dual encoder model that integrates visual and language inputs for automated OSAHS diagnosis. The model balances data using randomOverSampler (ROS), extracts key facial features with attention grids, and converts basic physiological data into meaningful text. Cross attention combines image and text data for better feature extraction, and ordered regression loss ensures stable learning. Our approach improves diagnostic efficiency and accuracy, achieving 91.3% top-1 accuracy in a four class severity classification task, demonstrating state of the art performance. Code is available at https://***/luboyan6/VTA-OSAHS. © 2025 IEEE.

关键词： Attention Mesh Clinical BERT Cross Attention Modality Interaction OSAHS Diagnosis

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A Hybrid Model Combining Vision Transformer and Generative Pre-trained Transformer for Image Captioning

A Hybrid Model Combining Vision Transformer and Generative P...

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2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Gupta, Hina Vishwesh, Jatangi Y D M V Prasada Jagadeesh, M. Sucharitha, M. Pradyothan, Vitta Sai Bennett University School of Computer Science Engineering & Technology Noida India

ISBN: (纸本)9798331508685

Creating descriptive captions for images is now becoming a mission-critical application area in the intersection of natural language processing and computer vision. This work provides the hybrid model VisionGPT2, combining Vision Transformer for encoding images and GPT2 for text generation, which enables accurate, coherent, and context-aware image descriptions. The model is tested over a dataset by using BLEU score metrics and has achieved a high BLEU score of 0.4877, which ascertains the strength of cross-modal architectures for image captioning. In this paper, the design and training process of VisionGPT2 has been discussed along with the benefits and challenges faced while proceeding and the potential applications the model has gained in performance for future use. © 2025 IEEE.

关键词： Computer vision

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