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TRUTH DECAY: Quantifying Multi-Turn Sycophancy in Language Models

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arxiv 2503.11656 v1 pith:SOXYK4SS submitted 2025-02-04 cs.CL

Joshua Liu , Aarav Jain , Soham Takuri , Srihan Vege , Aslihan Akalin , Kevin Zhu , Sean O'Brien , Vasu Sharma This is my paper · ORCID
classification cs.CL
keywords modelssycophancylanguagedecayinteractionstruthaccuracyagree
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Rapid improvements in large language models have unveiled a critical challenge in human-AI interaction: sycophancy. In this context, sycophancy refers to the tendency of models to excessively agree with or flatter users, often at the expense of factual accuracy. While previous studies have primarily analyzed this behavior in single-turn interactions, its persistence and evolution in multi-step conversations remain largely unexplored. We introduce TRUTH DECAY, a benchmark specifically designed to evaluate sycophancy in extended dialogues, where language models must navigate iterative user feedback, challenges, and persuasion. We prompt models to elicit four types of sycophantic biases. We then propose and test sycophancy reduction strategies, evaluating their effectiveness beyond single-step interactions.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Most LLM Conformity Needs No Speaker: Measuring the Speaker-Free Floor in Peer-Pressure Benchmarks

    cs.CL 2026-07 accept novelty 7.0 of 10

    Across six open-weight LLMs and seven datasets, a speaker-free wrong-answer assertion alone flips 66.5% of initially correct answers, versus 10.3% for a plain re-ask; source labels mainly add a modest increment above ...

  2. Measuring and Detecting Harmful AI Sycophancy

    cs.AI 2026-08 conditional novelty 6.0 of 10

    AI chatbots reverse an initial stance to match user preferences in 5% to 56% of tested cases, and supervised detectors trained on a new 290,460-response benchmark can detect such reversals from response text alone, th...

  3. MedPRESS: A Multi-turn Benchmark for Patient-Pressure-Induced Medical Sycophancy in LLMs

    cs.CL 2026-08 conditional novelty 6.0 of 10

    Across 600 five-turn medical dialogues, most of 20 LLMs shift from safe stances to unsafe agreement once patients apply escalating pressure.

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