LLM based QA chatbot builder: A generative AI-based chatbot builder for question answering

Large language model (LLM) based interactive chatbots have been gaining popularity as a tool to serve organizational information among people. Building such a tool goes through several development phases i.e. (a) Data collection and preprocessing, (b) LLM fine-tuning, testing, and inference, and (c) Chat interface development. To streamline this development process, in this paper, we present the LLM Question–Answer (QA) builder, a web application, which assembles all the steps and makes it easy for technical and non-technical users to develop the LLM QA chatbot. The system allows the instruction fine-tuning of following LLMs: Zepyhr, Mistral, Llama-3, Phi, Flan-T5, and user provided model for organization-specific information retrieval (IR), which can be further enhanced by Retrieval Augmented Generation (RAG) techniques. We have added an automatic web crawling based RAG data scrapper. Also, our system contains a human evaluation feature and RAG metrics for assessing model quality.

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Publication Details

Journal
SoftwareX
Published
2024-12-30
DOI
https://doi.org/10.1016/j.softx.2024.102029
Citations
14
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
4.86

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article

LLM based QA chatbot builder: A generative AI-based chatbot builder for question answering

Md. Shahidul Salim, Debdipta Bose, Sk Imran Hossain, Mohammad Jahid Ibna Basher et al.
14 citations
SoftwareX
AI in Service Interactions
4.86
article

LLM based QA chatbot builder: A generative AI-based chatbot builder for question answering

Md. Shahidul Salim, Debdipta Bose, Sk Imran Hossain, Mohammad Jahid Ibna Basher, Tanim Jalal
article en
14 citations

Abstract

Large language model (LLM) based interactive chatbots have been gaining popularity as a tool to serve organizational information among people. Building such a tool goes through several development phases i.e. (a) Data collection and preprocessing, (b) LLM fine-tuning, testing, and inference, and (c) Chat interface development. To streamline this development process, in this paper, we present the LLM Question–Answer (QA) builder, a web application, which assembles all the steps and makes it easy for technical and non-technical users to develop the LLM QA chatbot. The system allows the instruction fine-tuning of following LLMs: Zepyhr, Mistral, Llama-3, Phi, Flan-T5, and user provided model for organization-specific information retrieval (IR), which can be further enhanced by Retrieval Augmented Generation (RAG) techniques. We have added an automatic web crawling based RAG data scrapper. Also, our system contains a human evaluation feature and RAG metrics for assessing model quality.

SoftwareXVol. 29
Chittagong University of Engineering & Technology (BD), Khulna University of Engineering and Technology (BD)
Missouri University of Science and Technology, University Grants Commission of Bangladesh
Openalex Percentile: Top 4%
AI in Service Interactions
4.86
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LLM based QA chatbot builder: A generative AI-based chatbot builder for question answering — Md. Shahidul Salim, Debdipta Bose, et al. · SoftwareX (2024) | TGRS Research Map | TGRS