# Closed Domain QA System for LBL ScienceIT: Fine-Tuned and Retrieval Augmented Generation Models

- Institution: CERN
- Department: HEP Computing
- Speaker: Fengchen Liu
- Year: 2024
- Language: English
- Subjects: Computer science
- Source: [CERN](https://videos.cern.ch/record/3009575)
- Licence: [CERN](http://copyright.web.cern.ch)
- Who can watch: Tweens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

This paper proposes the development of a closed-domain Question-Answering (QA) system for LBL ScienceIT, using the ScienceIT website as the data source. The focus is on evaluating different models, specifically two fine-tuned pre-trained language models and three retrieval-augmented generation (RAG) models. Through this comparison, insights into the performance of these models, based on several evaluation metrics, are derived, ultimately highlighting the potential of a certain approach for the specific task. Through this comparative study, we aspire not only to present a robust QA framework for LBL ScienceIT but also to shed light on the dynamics of model selection and optimization for domain-specific tasks, setting the stage for future advancements in the realm of specialized QA systems..

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## Lectures

1. [Closed Domain QA System for LBL ScienceIT: Fine-Tuned and Retrieval Augmented Generation Models](https://invisiblenetwork.ai/learn/cern/3009575/3d5669551c20), 16 min (CERN's player)