mlsanityData to 14 Oct 2026

Are You Sure You're Sure? Two Confounds in a Sycophancy Benchmark

arXiv 2609.32867 ↗First posted 26 Sep 2026cs.CLcited by 1

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Abstract (arXiv)

Sycophancy is a language model's tendency to cave when a user pushes back, abandoning a correct answer for the user's. Several benchmarks now measure it by scripting an objection and recording how often the model caves. Because that objection is a prompt template, whatever else the template varies is measured along with the property it claims to isolate. We audit SycEval, which reports that objections raised before a model answers (preemptive) cause more caving than those raised after (in-context), and attributes the gap to timing. Two features of its templates vary alongside the property each is meant to test. First, SycEval's objections escalate through four strength levels, and at the two weakest only the preemptive template names a target answer, so timing and naming vary together. We build the missing comparison and test it on multiple-choice questions and SycEval's own free-form pipeline. Naming a target answer raises the follow rate, the share of samples matching the user's assertion, by 14.1--49.5 percentage points (pp), and once both templates name one, the timing comparison reverses on three of the five model conditions we test: models cave less under preemptive objections than in-context ones, opposite to SycEval. Second, the output-format instruction benchmarks append for automatic grading also varies with objection placement. Moving it from the pushback into the question flips its effect in opposite directions across models on the same items (p=0.0059). The same effect appears in SycEval's free-form pipeline: relocating its instruction lowers caving by 5.1pp on Llama-3.1-8B, while an equivalence test confirms no effect on Qwen3-4B. Both confounds live in the template rather than the models under test, so both are correctable: we close with three checks benchmark authors can apply before publishing.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 22 of its 23 references that are in the map. First posted on arXiv 26 Sep 2026.

Builds on

22 of 23 references.

References · 22

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2025Qwen3 Technical Reportcited by 8,820
  3. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  4. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,371
  5. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,886
  6. 2018Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answeringcited by 2,164
  7. 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,822
  8. 2023Towards Understanding Sycophancy in Language Modelscited by 1,040
  9. 2022Discovering Language Model Behaviors with Model-Written Evaluationscited by 836
  10. 2023Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formattingcited by 817
  11. 2023Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questionscited by 263
  12. 2019A Question-Entailment Approach to Question Answeringcited by 134
10 more
  1. 2025SycEval: Evaluating LLM Sycophancycited by 108
  2. 2025Measuring Sycophancy of Language Models in Multi-turn Dialoguescited by 104
  3. 2023Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experimentcited by 77
  4. 2025When Truth Is Overridden: Uncovering the Internal Origins of Sycophancy in Large Language Modelscited by 50
  5. 2025Quantization Meets Reasoning: Exploring LLM Low-Bit Quantization Degradation for Mathematical Reasoningcited by 37
  6. 2025BrokenMath: A Benchmark for Sycophancy in Theorem Proving with LLMscited by 27
  7. 2026Ask don't tell: Reducing sycophancy in large language modelscited by 14
  8. 2026Safety Under Scaffolding: How Evaluation Conditions Shape Measured Safetycited by 9
  9. 2025Sycophancy Claims about Language Models: The Missing Human-in-the-Loopcited by 4
  10. 2025PENDULUM: A Benchmark for Assessing Sycophancy in Multimodal Large Language Modelscited by 4

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