Machine individuality: Separating genuine idiosyncrasy from response bias in large language models

Build a benchmarking library to differentiate model response bias from genuine idiosyncratic behavior. This is critical for developers aiming to deploy LLMs in high-stakes human-facing roles.

Suggested repo: SoulCheck

"Is your model just mimicking your tone, or does it have an 'individual' disposition?"

Estimated effort: 35h