Psych Profiling LLMs: It's Not What You Think

So we're profiling AI now? It's wild how psychological profiling of large language models (LLMs) has become the norm, using questionnaires meant for humans. But guess what? It might not work as well as we'd hoped. New findings suggest these profiles don't match up with how LLMs actually behave when interacting with users.

The Testing Flaw

Researchers put eight open-source LLMs to the test, comparing two sets of profiles. One based on self-reported Likert scores from classic questionnaires like PVQ-40, PVQ-21, BFI-44, and BFI-10. The other? Generation probability scores looking at how models respond to real-world queries. The verdict? These profiles differ big time.

This inconsistency raises eyebrows. If LLMs are just reflecting desired behavior rather than stable traits, are we really understanding AI psychology? The labs are scrambling because this challenges earlier work claiming consistent psychological dispositions in LLMs.

Exaggerating Bias

There's another twist. These questionnaires could be amplifying demographic biases in LLMs. That's right, instead of revealing AI's true nature, they might be skewing results. It's a reminder that human-designed tools aren't foolproof in AI evaluation.

The Better Approach?

So what's the alternative? The study hints that generation-based profiling could be the answer. By focusing on real-time responses rather than static questionnaires, we might get a clearer picture of AI's psychological landscape.

This is huge. If LLMs are truly adaptable, shouldn't our methods of understanding them adapt too? Are we missing out on the real AI story by clinging to outdated tools?

JUST IN: Traditional psychometrics might be out. It's time to rethink how we profile our digital counterparts. And just like that, the leaderboard shifts.