Work

UZH_Pandas at SimpleText2024: Multi-Prompt Minimum Bayes Risk with Diverse Prompts

Year: 2024

Type: conference-paper

Abstract: This paper serves as a summary of further experiments of the paper "SimpleText Best of Labs in CLEF-2023: Scien-tific Text Simplification Using Multi-Prompt Minimum Bayes Risk Decoding" [1], adapted to the SimpleText2024 Shared Task 3.1 dataset. We observe how candidate simplifications generated by the off-the-shelf Llama3 perform differently depending on the prompt, and whether Minimum Bayes Risk (MBR) re-ranking is beneficial with underperforming candidates. Finally, on a small sample, we investigate the agreement of simplification candidate re-rankings between MBR and a human annotator.

Source: Zurich Open Repository and Archive (University of Zurich)

DOI: 10.5167/uzh-271151

Authors: Michail, Andrianos, Andermatt, Pascal Severin, Fankhauser, Tobias

Language: English

Topic: Text Readability and Simplification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6929437012

FWCI:

Cites:

Cited by:

Related to: