Content area

Abstract

Translate

Large language models have the potential to simplify formal theorem proving and make it more accessible. But how to get the most out of these models is still an open question. To answer this question, we take a step back and explore the failure cases of these models using common prompting-based techniques. Our talk will discuss these failure cases and what they can teach us about how to get more out of these models.

Details

1009240
Subject
Questions;
Large language models
Identifier / keyword
Formal Languages and Automata Theory
URL
http://arxiv.org/abs/2305.04369
Title
Getting More out of Large Language Models for Proofs
Author
Zhang, Shizhuo Dylan; Ringer, Talia; First, Emily
Publication title
arXiv.org; Ithaca
Publication year
2023
Publication date
May 31, 2023
Section
Computer Science
Publisher
Cornell University Library, arXiv.org
Source
arXiv.org
Place of publication
Ithaca
Country of publication
United States
University/institution
Cornell University Library arXiv.org
Publication subject
Biology, Statistics, Physics, Mathematics, Engineering--Electrical Engineering, Computers--Computer Engineering, Business And Economics--Banking And Finance
e-ISSN
2331-8422
Source type
Working Paper
Language of publication
English
Document type
Working Paper
Publication history
 
 
Online publication date
2023-06-02
Milestone dates
2023-05-07 (Submission v1); 2023-05-31 (Submission v2)
Publication history
 
 
   First posting date
02 Jun 2023
ProQuest document ID
2811359378
Document URL
https://www.proquest.com/working-papers/getting-more-out-large-language-models-proofs/docview/2811359378/se-2?accountid=208611
Full text outside of ProQuest
http://arxiv.org/abs/2305.04369
Copyright
© 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2023-06-03
Database
ProQuest One Academic