Larger and more instructable language models become less reliable.

ABSTRACT: The prevailing methods to make large language models more powerful and amenable have been based on continuous scaling up (that is, increasing their size, data volume and computational resources¹) and bespoke shaping up (including post-filtering^2,3, fine tuning or use of human feedback^4,5). However, larger and more instructable large language models may have become less reliable. By studying the relationship between difficulty concordance, task avoidance and prompting stability of several language model families, here we show that easy instances for human participants are also easy for the models, but scaled-up, shaped-up models do not secure areas of low difficulty in which either the model does not err or human supervision can spot the errors. We also find

SUBMITTER: Zhou L 

PROVIDER: S-EPMC11446866 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature