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

arXiv: https://arxiv.org/abs/2512.00656 · HTML: https://mlsanity.com/paper/2512.00656/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 23 references that are in the map.
- First posted on arXiv 29 Nov 2025.

## Builds on (most cited first, 16 of 16)

- [Towards Understanding Sycophancy in Language Models](https://mlsanity.com/paper/2310.13548/) (2023)
- [Discovering Language Model Behaviors with Model-Written Evaluations](https://mlsanity.com/paper/2212.09251/) (2022)
- [Gender Bias in Coreference Resolution](https://mlsanity.com/paper/1804.09301/) (2018)
- [Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models](https://mlsanity.com/paper/2406.10162/) (2024)
- [From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuning](https://mlsanity.com/paper/2409.01658/) (2024)
- [NormAd: A Framework for Measuring the Cultural Adaptability of Large Language Models](https://mlsanity.com/paper/2404.12464/) (2024)
- [Monitoring Latent World States in Language Models with Propositional Probes](https://mlsanity.com/paper/2406.19501/) (2024)
- [How Trustworthy are Open-Source LLMs? An Assessment under Malicious Demonstrations Shows their Vulnerabilities](https://mlsanity.com/paper/2311.09447/) (2023)
- [Prompt Leakage effect and defense strategies for multi-turn LLM interactions](https://mlsanity.com/paper/2404.16251/) (2024)
- [Understanding Impact of Human Feedback via Influence Functions](https://mlsanity.com/paper/2501.05790/) (2025)
- [Refined Direct Preference Optimization with Synthetic Data for Behavioral Alignment of LLMs](https://mlsanity.com/paper/2402.08005/) (2024)
- [Chaos with Keywords: Exposing Large Language Models Sycophantic Hallucination to Misleading Keywords and Evaluating Defense Strategies](https://mlsanity.com/paper/2406.03827/) (2024)
- [Multi-objective Reinforcement learning from AI Feedback](https://mlsanity.com/paper/2406.07295/) (2024)
- [Measuring Agreeableness Bias in Multimodal Models](https://mlsanity.com/paper/2408.09111/) (2024)
- [Unmasking the Shadows of AI: Investigating Deceptive Capabilities in Large Language Models](https://mlsanity.com/paper/2403.09676/) (2024)
- [Social Evolution of Published Text and The Emergence of Artificial Intelligence Through Large Language Models and The Problem of Toxicity and Bias](https://mlsanity.com/paper/2402.07166/) (2024)

## Built on by (most cited first, 4 of 4)

- [Moral Sycophancy in Vision Language Models](https://mlsanity.com/paper/2602.08311/) (2026)
- [Are You Sure You're Sure? Two Confounds in a Sycophancy Benchmark](https://mlsanity.com/paper/2609.32867/) (2026)
- [The Rise of Verbal Tics in Large Language Models: A Systematic Analysis Across Frontier Models](https://mlsanity.com/paper/2604.19139/) (2026)
- [Sycophancy is an Educational Safety Risk: Why LLM Tutors Need Sycophancy Benchmarks](https://mlsanity.com/paper/2605.14604/) (2026)

## Abstract (arXiv)

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.