Leveraging Text-to-Text Pretrained Language Models for Question Answering in Chemistry.

ABSTRACT: In this study, we present a question answering (QA) system for chemistry, named Marie, with the use of a text-to-text pretrained language model to attain accurate data retrieval. The underlying data store is "The World Avatar" (TWA), a general world model consisting of a knowledge graph that evolves over time. TWA includes information about chemical species such as their chemical and physical properties, applications, and chemical classifications. Building upon our previous work on KGQA for chemistry, this advanced version of Marie leverages a fine-tuned Flan-T5 model to seamlessly translate natural language questions into SPARQL queries with no separate components for entity and relation linking. The developed QA system demonstrates competence in providing accurate results for complex que

SUBMITTER: Tran D 

PROVIDER: S-EPMC10976360 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature