PENDULUM: A Benchmark for Assessing Sycophancy in Multimodal Large Language Models

TL;DR

This paper introduces PENDULUM, a benchmark with 2,000 visual question-answering pairs designed to evaluate and analyze sycophantic tendencies in multimodal large language models, revealing their susceptibility to agreement bias and hallucinations.

Contribution

The paper presents a new comprehensive benchmark and metrics for assessing sycophancy in multimodal models, addressing a significant gap in current evaluation methods.

Findings

MLLMs show high variability in robustness against sycophancy.

Models are notably susceptible to sycophantic and hallucinatory responses.

The benchmark enables systematic analysis of visual and contextual factors influencing sycophancy.

Abstract

Sycophancy, an excessive tendency of AI models to agree with user input at the expense of factual accuracy or in contradiction of visual evidence, poses a critical and underexplored challenge for multimodal large language models (MLLMs). While prior studies have examined this behavior in text-only settings of large language models, existing research on visual or multimodal counterparts remains limited in scope and depth of analysis. To address this gap, we introduce a comprehensive evaluation benchmark, \textit{PENDULUM}, comprising approximately 2,000 human-curated Visual Question Answering pairs specifically designed to elicit sycophantic responses. The benchmark spans six distinct image domains of varying complexity, enabling a systematic investigation of how image type and inherent challenges influence sycophantic tendencies. Through extensive evaluation of state-of-the-art MLLMs. we observe substantial variability in model robustness and a pronounced susceptibility to sycophantic and hallucinatory behavior. Furthermore, we propose novel metrics to quantify sycophancy in visual reasoning, offering deeper insights into its manifestations across different multimodal contexts. Our findings highlight the urgent need for developing sycophancy-resilient architectures and training strategies to enhance factual consistency and reliability in future MLLMs. Our proposed dataset with MLLMs response are available at https://github.com/ashikiut/pendulum/.