# Acquiescence Bias in Large Language Models

arXiv: https://arxiv.org/abs/2509.08480 · HTML: https://mlsanity.com/paper/2509.08480/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 10 Sep 2025.

## Builds on (most cited first, 11 of 11)

- [LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models](https://mlsanity.com/paper/2308.11462/) (2023)
- [Do LLMs exhibit human-like response biases? A case study in survey design](https://mlsanity.com/paper/2311.04076/) (2023)
- [The Effect of Sampling Temperature on Problem Solving in Large Language Models](https://mlsanity.com/paper/2402.05201/) (2024)
- [Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models](https://mlsanity.com/paper/2402.16786/) (2024)
- [Cognitive Bias in Decision-Making with LLMs](https://mlsanity.com/paper/2403.00811/) (2024)
- [Capturing Failures of Large Language Models via Human Cognitive Biases](https://mlsanity.com/paper/2202.12299/) (2022)
- [How Susceptible are LLMs to Influence in Prompts?](https://mlsanity.com/paper/2408.11865/) (2024)
- [Exploring the Sensitivity of LLMs' Decision-Making Capabilities: Insights from Prompt Variation and Hyperparameters](https://mlsanity.com/paper/2312.17476/) (2023)
- [This is the way: designing and compiling LEPISZCZE, a comprehensive NLP benchmark for Polish](https://mlsanity.com/paper/2211.13112/) (2022)
- [AGB-DE: A Corpus for the Automated Legal Assessment of Clauses in German Consumer Contracts](https://mlsanity.com/paper/2406.06809/) (2024)
- [Parameterized Argumentation-based Reasoning Tasks for Benchmarking Generative Language Models](https://mlsanity.com/paper/2505.01539/) (2025)

## Built on by (most cited first, 5 of 5)

- [AMEL: Accumulated Message Effects on LLM Judgments](https://mlsanity.com/paper/2605.22714/) (2026)
- [The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment](https://mlsanity.com/paper/2607.05552/) (2026)
- [This Is a Good Paper, Right? How Tag Questions Affect Sycophancy Across 106 Language Models](https://mlsanity.com/paper/2607.23976/) (2026)
- [SPOT: Selective Prompt Projection via Total Variation for Inference-Only Safe Text-to-Image Generation](https://mlsanity.com/paper/2602.00616/) (2026)
- [Distributional Validity of a Korean Synthetic Persona Panel: Evidence From the Korea Media Panel Survey](https://mlsanity.com/paper/2608.28615/) (2026)

## Abstract (arXiv)

Acquiescence bias, i.e. the tendency of humans to agree with statements in surveys, independent of their actual beliefs, is well researched and documented. Since Large Language Models (LLMs) have been shown to be very influenceable by relatively small changes in input and are trained on human-generated data, it is reasonable to assume that they could show a similar tendency. We present a study investigating the presence of acquiescence bias in LLMs across different models, tasks, and languages (English, German, and Polish). Our results indicate that, contrary to humans, LLMs display a bias towards answering no, regardless of whether it indicates agreement or disagreement.