SUPR
Large language models for question answering on local data.
Dnr:

NAISS 2023/22-971

Type:

NAISS Small Compute

Principal Investigator:

Peter Berck

Affiliation:

Lunds universitet

Start Date:

2023-09-18

End Date:

2024-10-01

Primary Classification:

10208: Language Technology (Computational Linguistics)

Webpage:

Allocation

Abstract

The project aims to create models for question answering on local data by exploring generative question-answering models. Generative models can create new responses based on what they have learned as opposed to simpler "answer extraction" models which extract answers from a given context. To make these models work best with our specific local data, we need to fine-tune existing models, hence the need for GPU time. The PI has a PhD in language modelling, and is employed as a research engineer at the Humanities Lab in Lund. The PI is involved in other NLP projects focusing on Named Entity Extraction and Relation Extraction on medical texts.