Template-Type: ReDIF-Paper 1.0
Author-Name: Fernando Perez-Cruz
Author-X-Name-First: Fernando
Author-X-Name-Last: Perez-Cruz
Author-Name: Hyun Song Shin
Author-X-Name-First: Hyun
Author-X-Name-Last: Song Shin
Title: Testing the cognitive limits of large language models
Abstract: When posed with a logical puzzle that demands reasoning about the knowledge of others and about counterfactuals, large language models (LLMs) display a distinctive and revealing pattern of failure.
The LLM performs flawlessly when presented with the original wording of the puzzle available on the internet but performs poorly when incidental details are changed, suggestive of a lack of true understanding of the underlying logic.
Our findings do not detract from the considerable progress in central bank applications of machine learning to data management, macro analysis and regulation/supervision. They do, however, suggest that caution should be exercised in deploying LLMs in contexts that demand rigorous reasoning in economic analysis.
Length: 9 pages
Creation-Date: 2024-01-04
File-URL: https://www.bis.org/publ/bisbull83.pdf
File-Format: Application/pdf
File-Function: Full PDF document
File-URL: https://www.bis.org/publ/bisbull83.htm
File-Format: text/html
Number: 83
Handle: RePEc:bis:bisblt:83