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Open Source Large Language Models in Action: A Bioinformatics Chatbot for PRIDE database
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  • Jingwen Bai,
  • Selvakumar Kamatchinathan,
  • Deepti J Kundu,
  • Chakradhar Bandla,
  • Juan Antonio Vizcaino,
  • Yasset Perez Riverol
Jingwen Bai
European Bioinformatic Institute
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Selvakumar Kamatchinathan
European Bioinformatic Institute
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Deepti J Kundu
European Bioinformatic Institute
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Chakradhar Bandla
European Bioinformatic Institute
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Juan Antonio Vizcaino
EMBL-EBI
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Yasset Perez Riverol
European Bioinformatic Institute

Corresponding Author:[email protected]

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Abstract

We here present a chatbot assistant infrastructure (https://www.ebi.ac.uk/pride/chatbot/) that simplifies user interactions with the PRIDE database, the most popular proteomics data repository. Our system utilizes two advanced Large Language Models (LLM), llama2-13b and chatglm2-6b, and includes a web service API (Application Programming Interface), web interface, and sophisticated algorithms. We have developed a novel approach to construct vector-based representations for enabling the LLM responses, featuring a curated version and a comprehensive database of relevant links and paragraphs for each generated response. An important part of the framework is a benchmark component based on an Elo-ranking system, providing a scalable method for evaluating not only the performance of llama2-13b and chatglm2-6b but also, of any other available and future open-source LLMs. Throughout the benchmarking process, the PRIDE documentation for external users was refined to enhance the clarity and efficacy in addressing user queries. Importantly, while our infrastructure is exemplified through its application in the PRIDE database context, the modular and adaptable nature of our approach positions it as a valuable tool for improving user experiences across a spectrum of bioinformatics and proteomics tools and resources, among other domains. The integration of advanced LLMs, innovative vector-based construction, the benchmarking framework, and optimized documentation collectively form a robust and transferable chatbot assistant infrastructure.