About
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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The talk
Credits and licence
- Institution
- CERN
- Department
- HEP Computing
- Speaker
- Fengchen Liu
- Year
- 2024
- Language
- English
- Subjects
- Computer science
- Source
- CERN
- Licence
- CERN, as CERN states it. CERN's terms
- Attribution
- © CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
- Who can watch
- CERN gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed