Are You Sure You're Sure? Two Confounds in a Sycophancy Benchmark
cs.CL
Submitted: 2026-09-26
Updated: 2026-09-26
Comments: 22 pages, 19 tables, 3 figures
License: http://creativecommons.org/licenses/by/4.0/
The gist: Sycophancy is a language model's tendency to cave when a user pushes back, abandoning a correct answer for the user's.
Terminology
Abstract
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.
Sources
- Measuring Sycophancy of Language Models in Multi-turn Dialogues
- BrokenMath: A Benchmark for Sycophancy in Theorem Proving with LLMs
- Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment
- PENDULUM: A Benchmark for Assessing Sycophancy in Multimodal Large Language Models
- Ask don't tell: Reducing sycophancy in large language models
- Safety Under Scaffolding: How Evaluation Conditions Shape Measured Safety
- Sycophancy Claims about Language Models: The Missing Human-in-the-Loop
- Qwen2.5 Technical Report
- Qwen3 Technical Report
- The Llama 3 Herd of Models
- Measuring Massive Multitask Language Understanding
- Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
- Measuring Mathematical Problem Solving With the MATH Dataset
- Quantization Meets Reasoning: Exploring LLM Low-Bit Quantization Degradation for Mathematical Reasoning
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