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Samyuktha M S, S. Charumathi, Darsana R, S. Lovelyn Rose
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- Student, Student, Student, Associate Professor, PSG College of Technology, Coimbatore,, PSG College of Technology, Coimbatore,, PSG College of Technology, Coimbatore,, PSG College of Technology, Coimbatore,, Tamil Nadu, Tamilnadu, Tamilnadu, Tamilnadu, India, India, India, India
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Abstract
nThe “LLM-based Chatbot for Course-Based Question Answering” project addresses the pressing need for tailored and efficient learning tools in education. By using a state-of-the-art Large Language Model (LLM) with a diverse dataset, including textbooks, professor slides, and web scraping data, the chatbot offers accurate and contextually enriched responses to students’ course-related queries. Using recent advances in language modeling, this work presents a Longformer-based Language Model (LLM) for constructing a smart chatbot optimized for course-based question answering (CBQA). The suggested LLM-based chatbot has a user-friendly interface where students can submit questions on various subjects, topics, or concepts presented in their courses. The chatbot uses natural language understanding techniques to extract relevant information and generate responses to these requests. The LLM-based chatbot’s key feature is its capacity to answer a wide range of question kinds, including factual, conceptual, and problem-solving queries. Through fine-tuning individual course materials, the chatbot adapts to the unique vocabulary and content of different disciplines, ensuring accurate and contextually relevant responses. It also features a function for extracting chapter summaries and generating concise study notes, facilitating efficient revision. The project’s outcomes include providing students with a robust tool for course clarification, enabling them to practice with relevant materials, and streamlining the revision process, ultimately enhancing the learning experience within the specified college.
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Keywords: LLM based Chatbot, Large Language Model (LLM), Natural Language Processing (NLP), Question Answering (QA) Systems, processing, Chapter summaries, Efficient revision, Course clarification, Academic enhancement
n[if 424 equals=”Regular Issue”][This article belongs to International Journal of Electronics Automation(ijea)]
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References
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| Volume | 01 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 02 | |
| Received | March 15, 2024 | |
| Accepted | March 30, 2024 | |
| Published | May 10, 2024 |
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