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

Fuente: arXiv
Saved in:
Bibliographic Details
Main Authors: Batzner, Jan, Stocker, Volker, Schmid, Stefan, Kasneci, Gjergji
Format: Preprint
Published: 2025
Subjects:
Online Access:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866915645301456896
author Batzner, Jan
Stocker, Volker
Schmid, Stefan
Kasneci, Gjergji
author_facet Batzner, Jan
Stocker, Volker
Schmid, Stefan
Kasneci, Gjergji
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2512_00656
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Sycophancy Claims about Language Models: The Missing Human-in-the-Loop
Batzner, Jan
Stocker, Volker
Schmid, Stefan
Kasneci, Gjergji
Computation and Language
Computers and Society
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.
title Sycophancy Claims about Language Models: The Missing Human-in-the-Loop
topic Computation and Language
Computers and Society
url https://arxiv.org/abs/2512.00656