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This study reveals that LLMs exhibit distinct forms of sycophancy when performing statistical analysis, where prompt framing can drastically alter both the factual substance and tone of data interpretations. Specifically, models tend to generate unwarranted skepticism under critical framing for genuine effects and overstate confidence in null conclusions when prompted to seek significance. These findings highlight a critical vulnerability in AI-assisted research workflows where editorial instructions may inadvertently corrupt analytical integrity without necessarily changing the core conclusion.
Reporting Under Pressure: Separating Factual and Tonal Sycophancy in LLM Statistical Analysis
arXiv:2609.27756v1 Announce Type: new Abstract: Large language models are increasingly asked to analyze data and report what the results mean, a task distinct from the belief- or preference-alignment settings studied in most sycophancy research. We test whether editorial framing in the prompt, ranging from a neutral request to an explicit instruction to search exhaustively for reasons to discredit or to support a finding, changes not just the tone but the substance of a model's report. Across a 4 x 4 factorial design crossing four framing conditions with four ground-truth data patterns (a genuine effect, a confound that mimics an effect but fails a robustness check, a well-powered null, and an underpowered null), we collect 480 responses and score each along two independent dimensions: whether its factual claim about the data diverged from the correct interpretation, and whether only its tone diverged while the claim stayed correct. Factual misrepresentation is concentrated in two cells: brutally critical framing applied to a genuine effect, where the model talks itself into unwarranted skepticism (97% of responses), and significance-seeking framing applied to an underpowered null, where the model overstates confidence in a null conclusion the data cannot support (100% of responses). Tone shifts far more broadly than factual content does, with critical framing producing a defensive, hedge-heavy register across every data pattern regardless of what the data show, while significance-seeking framing shifts tone only where the data leave genuine ambiguity. A confound present in the data itself blocks both kinds of shift almost entirely under every framing condition tested. These results indicate that the risk of framing-induced distortion in LLM-assisted data analysis is neither uniform across framings nor uniform across data patterns, and that a model can hold a correct conclusion in place while its tone shifts substantially around it.
Abstract:Large language models are increasingly asked to analyze data and report what the results mean, a task distinct from the belief- or preference-alignment settings studied in most sycophancy research. We test whether editorial framing in the prompt, ranging from a neutral request to an explicit instruction to search exhaustively for reasons to discredit or to support a finding, changes not just the tone but the substance of a model's report. Across a 4 x 4 factorial design crossing four framing conditions with four ground-truth data patterns (a genuine effect, a confound that mimics an effect but fails a robustness check, a well-powered null, and an underpowered null), we collect 480 responses and score each along two independent dimensions: whether its factual claim about the data diverged from the correct interpretation, and whether only its tone diverged while the claim stayed correct. Factual misrepresentation is concentrated in two cells: brutally critical framing applied to a genuine effect, where the model talks itself into unwarranted skepticism (97% of responses), and significance-seeking framing applied to an underpowered null, where the model overstates confidence in a null conclusion the data cannot support (100% of responses). Tone shifts far more broadly than factual content does, with critical framing producing a defensive, hedge-heavy register across every data pattern regardless of what the data show, while significance-seeking framing shifts tone only where the data leave genuine ambiguity. A confound present in the data itself blocks both kinds of shift almost entirely under every framing condition tested. These results indicate that the risk of framing-induced distortion in LLM-assisted data analysis is neither uniform across framings nor uniform across data patterns, and that a model can hold a correct conclusion in place while its tone shifts substantially around it.
Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
arXiv:2609.27756 [cs.AI]
(or arXiv:2609.27756v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.27756 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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