Sycophancy Claims about Language Models: The Missing Human-in-the-Loop

TL;DR

This paper reviews the challenges in measuring sycophantic responses in large language models, emphasizing the need to incorporate human perception and clarify operational definitions for better understanding and evaluation.

Contribution

It identifies key methodological challenges and operationalizations in studying LLM sycophancy, highlighting gaps in current research regarding human-centric evaluation.

Findings

Five core operationalizations of sycophancy identified

Current research lacks evaluation of human perception

Highlights difficulties in distinguishing sycophancy from related concepts

Abstract

Sycophantic response patterns in Large Language Models (LLMs) have been increasingly claimed in the literature. We review methodological challenges in measuring LLM sycophancy and identify five core operationalizations. Despite sycophancy being inherently human-centric, current research does not evaluate human perception. Our analysis highlights the difficulties in distinguishing sycophantic responses from related concepts in AI alignment and offers actionable recommendations for future research.