Evaluating LLMs on document-based QA: Exact answer selection and numerical extraction using CogTale dataset

Evaluating LLMs on document-based QA: Exact answer selection and numerical extraction using CogTale dataset

Document-based Question-Answering (QA) tasks are crucial for precise information retrieval. While some existing work focus on evaluating large language model’s (LLMs) performance on retrieving and answering questions from documents, assessing the LLMs performance on QA types that require exact answe...

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Bibliographic Details
Main Authors: Zafaryab Rasool, Stefanus Kurniawan, Sherwin Balugo, Scott Barnett, Rajesh Vasa, Courtney Chesser, Benjamin M. Hampstead, Sylvie Belleville, Kon Mouzakis, Alex Bahar-Fuchs
Format: Article
Language:English
Published: Elsevier 2024-09-01
Series:Natural Language Processing Journal
Subjects:
Large language models
Document-based information retrieval
Question-answering
Retrieval augmented generation
CogTale dataset
Healthcare
Online Access:http://www.sciencedirect.com/science/article/pii/S2949719124000311

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http://www.sciencedirect.com/science/article/pii/S2949719124000311

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