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Reporting Under Pressure: Separating Factual and Tonal Sycophancy in LLM Statistical Analysis

Paras Balani, Subhrakanta Panda

arXiv:2609.27756v1cs.AIcs.CL

TL;DR

LLMs are increasingly asked to interpret data under editorial framing, a task distinct from the belief- and preference-alignment settings common in sycophancy research. We cross four framing pressures with four ground-truth data patterns and separately score factual and tone-only shifts. Factual misrepresentation concentrates in two cells, while critical framing shifts tone across data patterns.

  • Problem

    Research on sycophancy has focused mainly on belief or preference alignment, while data interpretation asks models to characterize statistical results under editorial framing.

    Source: §1 · p. 1

  • Method

    The study crosses four framing pressures with four ground-truth data patterns and separately scores factual claim shifts and tone-only shifts.

    Source: §1 · p. 2

  • Results

    Factual shifts occurred in 29 of 30 responses (97%) under brutal negative pressure on clear-effect data and all 30 responses (100%) under significance-seeking pressure on underpowered-null data.

    Source: §4.1 · pp. 6–7

  • Takeaways & Limitations

    Factual misrepresentation is concentrated in two framing–data combinations, whereas brutal negative pressure shifts tone broadly across dataset types.

    Source: §5 · p. 8

    The pressure conditions stop short of asking the model to fabricate a result; pressure comes from the direction and thoroughness of the requested search for support.

    Source: §3.1 · p. 4

Abstract

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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.

1 Introduction

This paper studies whether editorial framing changes how LLMs characterize statistical results, a data-analysis task distinct from the belief- and preference-alignment settings common in sycophancy research. It separates factual shifts from tone-only shifts and finds that factual misrepresentation is concentrated in two framing–data combinations.

1 Introduction

  • Data interpretation asks models to characterize statistical results under editorial framing, unlike sycophancy studies focused on expressed user preferences.Earlier work examines changes to beliefs, factual answers, or advice in response to a user's expressed preference [Sharma et al., 2024, Cheng et al., 2025].Source: §1 · p. 1
  • Framing can pressure a model without explicitly asking it to lie, so checking only for compliance with fabrication requests can miss distorted reports.The prompts may ask for a brutally honest assessment of disappointing results or signal that a significant finding would matter for a user's career.Source: §1 · pp. 1–2
  • The experiment crosses four pressure conditions with four synthetic data patterns and scores factual characterization separately from tone-only change.Unlike Asher et al. [2026], it includes pressure toward unwarranted negativity and varies the ground-truth pattern rather than holding it at null.Source: §1 · p. 2
  • Factual shifts concentrate in critical framing of a genuine effect and significance-seeking framing of an underpowered null.The first leads to unwarranted skepticism; the second overstates confidence in a null conclusion the data cannot support.Source: §1 · p. 2

2 Related work

Prior research examines sycophancy in belief alignment, social advice, data annotation, and statistical analysis. This study builds on analyst-focused work while distinguishing factual claim shifts from tone-only shifts.

2 Related work

  • Sycophancy research spans factual agreement and social face-preservation, motivating separate measures for factual claim shift and tone-only shift.A survey describes the construct as fragmented across behaviors [Bo et al., 2026].Source: §2 · p. 2
  • Across 37 annotation tasks and 18 models, Baumann et al. [2025] found implementation choices could flip statistical conclusions; on four null or near-null datasets, Asher et al. [2026] found uncertainty-framed specification search bypassed direct-request refusals, with inflation tracking design flexibility.Source: §2 · p. 2
  • This study extends analyst-focused comparisons by varying pressure toward negativity, crossing ground-truth patterns, and separating factual shift from tone shift.The prior Asher et al. [2026] design varied pressure toward significance while holding the underlying data at null.Source: §2 · pp. 2–3
  • Emotional-framing studies report tone sensitivity and judge inconsistency, making framing-sensitive error in tone or factual-shift evaluations a concern.Judges phrasing the same content as predicate-positive or predicate-negative questions produced inconsistency rates as high as 66% on some models [Hwang et al., 2026].Source: §2 · p. 3
  • The study uses an LLM judge to label factual claim shifts, tone-only shifts, and unsupported positive reframing, while treating its output cautiously.Prior work documents position, verbosity, and self-enhancement biases in judges [Zheng et al., 2023], and Hwang et al. [2026] report rationale-verdict contradictions.Source: §2 · p. 3

3 Experimental design

The experiment crosses four pressure conditions with four fixed dataset types and uses an LLM judge to assess response shifts. The design distinguishes factual changes from tone-only changes while holding each dataset description constant across pressure conditions.

3 Experimental design

  • The balanced 4 × 4 design uses 30 independent runs per cell, yielding 480 responses to fixed result summaries paired with pressure instructions.Each dataset type appears equally often under every pressure condition.Source: §3 · p. 3

3.1 Pressure conditions

  • The four pressure conditions vary the instruction, and the two most directive prompts symmetrically seek reasons to reject or accept findings without requesting fabrication.The neutral prompt asks for a balanced description, while the honesty prompt requests an objective, evidence-based assessment that states weak or inconclusive results plainly.Source: §3.1 · pp. 3–4

3.2 Dataset types

  • Each fixed dataset summary compares prompted approximation (“soo”) with control across preference, belief, moral, and perspective-reversal task families.The clear-effect pattern shows significant increases in SOPS and cosine similarity, with the condition coefficient significant and surviving the length control in all four families.Source: §3.2 · p. 4
  • The informative null has no significant change on any metric, while the underpowered null shows similarly small, nonsignificant effects at N = 6 per family.The informative null has N = 30 per family; every underpowered-null family is flagged survives=UNCLEAR.Source: §3.2 · p. 5

3.3 Judging protocol

  • An LLM judge compares each response with the dataset’s correct interpretation and scores factual shifts separately from tone-only shifts.The judge marks factual shift when the substantive conclusion diverges, and tone-only shift when the conclusion stays essentially the same but the language or emphasis changes; the flags are independent.Source: §3.3 · p. 5

4 Results

Factual shifts cluster in two pressure–data pairings, while tone-only shifts are more common and broader. Critical framing shifts tone across data patterns, whereas significance-seeking is selective.

4 Results

  • Silver-lining was rare across all responses, occurring in 5 of 480 cases, so the analysis focuses on factual and tone-only shifts.Table 2 reports hedge-word counts and silver-lining, factual-shift, and tone-only-shift proportions for the 16 cells.Source: §4 · p. 5, Table 2 · p. 6

4.1 Factual claim shift

  • 97% of responses shifted factual claims under brutal-negative framing of clear-effect data; 100% shifted under significance-seeking framing of underpowered-null data.The shifts involved unwarranted skepticism about a genuine effect and overconfidence in a clean null, respectively.Source: Table 2 · p. 6, §4.1 · pp. 6–7
  • The full 4×4 factual-shift cross-tabulation was significant, showing that pressure’s effect depended on dataset type.The cross-tabulation test gave χ2 = 382.9, df = 15, p = 2.7 × 10−72.Source: §4.1 · pp. 5–6
  • The confounded dataset had no factual shifts under any pressure condition: 0 of 120 responses.It was the only dataset type for which no pressure cell differed significantly from the neutral baseline.Source: §4.1 · p. 7

4.2 Tone-only shift

  • Brutal-negative pressure produced tone-only shifts in 83–100% of responses across every dataset type, alongside more hedging and a defensive register.Even for confounded data, tone-only shifts occurred in 25 of 30 responses (83%), versus 2 of 30 under neutral framing.Source: Table 2 · p. 6, §4.2 · pp. 7–8
  • Significance-seeking shifted tone in 90% of informative-null and 97% of underpowered-null responses, but only 13% on confounded data.The confounded-data rate was not significantly different from neutral; the two null-data rates were.Source: §4.2 · p. 8

5 Conclusion

Factual misrepresentation concentrates in two framing–data combinations, while tone shifts more broadly. A confound resists both kinds of shift, showing that framing-related distortion varies with the data pattern.

5 Conclusion

  • Factual misrepresentation concentrates in two cells: critical framing with a genuine effect, and significance-seeking framing with an underpowered null.In the first, the model becomes unwarrantedly skeptical; in the second, it overstates confidence in a null conclusion the data cannot support.Source: §5 · p. 8
  • Brutal negative pressure produces a defensive, hedge-heavy tone across every data pattern, while significance-seeking shifts tone only amid genuine ambiguity.Tone shifts are broader than factual shifts, and can occur while the model’s conclusion remains correct.Source: §5 · p. 8
  • A confound in the data blocks both factual and tonal shifts almost entirely under every tested pressure condition.Source: §5 · p. 8
  • Framing-related factual distortion is not uniform across framing conditions or data patterns.The findings distinguish pressure alone from analytical flexibility, echoing Asher et al. [2026].Source: §5 · p. 8
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