Multi-turn Evaluation of Anthropomorphic Behaviours in Large Language Models

Understand

The tendency of users to anthropomorphise large language models (LLMs) is of growing interest to AI developers, researchers, and policy-makers.

• Here, we present a novel method for empirically evaluating anthropomorphic LLM behaviours in realistic and varied settings.
• Going beyond single-turn static benchmarks, we contribute three methodological advances in state-of-the-art (SOTA) LLM evaluation.
• First, we develop a multi-turn evaluation of 14 anthropomorphic behaviours.