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arxiv: 2606.20205 · v1 · pith:VQLGUZ4Dnew · submitted 2026-06-18 · 💻 cs.AI · cs.CL· cs.HC

Apparent Psychological Profiles of Large Language Models are Largely a Measurement Artifact

Jelena Meyer , David Garcia , Dirk U. Wulff This is my paper

Pith reviewed 2026-06-26 17:31 UTC · model grok-4.3

classification 💻 cs.AI cs.CLcs.HC
keywords large language modelspsychological profilesmeasurement artifactdirectional response biaspsychometric frameworkresponse orthogonalityvariance decomposition
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The pith

Apparent psychological profiles of large language models arise mostly from directional response bias rather than the traits the tests target.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper administers personality and risk-preference instruments to 56 instruction-tuned LLMs and human samples, then applies a formal psychometric variance decomposition. It finds that between-model differences are driven by a directional response bias toward one scale end or labeled option, accounting for 81-90 percent of variation versus 9-16 percent in humans. This bias declines with model capability but remains. Because bias dominates, an instrument's apparent reliability is predicted almost entirely by its response orthogonality—the share of items where trait and bias point oppositely—and model profiles shift or can be manufactured by changing the item set. The result is that borrowed human instruments produce artifacts rather than valid LLM traits.

Core claim

Using a battery of self-report and behavioral instruments, the authors show that differences between LLMs are driven not by targeted traits but by directional response bias; variance decomposition attributes 81-90% of between-model variation to this bias. The bias declines with capability but is not eliminated. Instrument reliability is almost entirely predicted by response orthogonality, and profiles shift with item selection or can be manufactured through it.

What carries the argument

Directional response bias (tendency to respond toward one end of the scale regardless of item content), isolated through variance decomposition within a formal psychometric framework applied to self-report and behavioral tasks.

If this is right

  • Apparent reliability of any instrument for LLMs is almost entirely predicted by its response orthogonality.
  • Model profiles shift with the items used and can be manufactured through item selection.
  • The bias declines with model capability but is not eliminated by it.
  • Instruments borrowed from human psychology rarely achieve full orthogonality and may lack validity for LLMs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Dedicated LLM assessments should be built around response orthogonality rather than direct adoption of human scales.
  • Using LLMs as proxies for human participants in psychological research may introduce systematic artifacts traceable to response bias.
  • Similar directional biases could affect other survey-style evaluations of AI systems beyond personality and risk measures.

Load-bearing premise

Directional response bias can be cleanly separated from the targeted trait effects by the chosen instruments and the variance decomposition method.

What would settle it

A replication in which the same variance decomposition on new LLMs and instruments attributes most between-model variation to the intended traits rather than directional bias, or in which model profiles remain stable when items are swapped for others measuring the same trait.

read the original abstract

Psychological instruments designed for humans are increasingly used to assign large language models (LLMs) stable psychological profiles that affect their usability, safety assessment, and use as proxies for human participants in research. Using a formal psychometric framework, we show that these profiles are largely a measurement artifact. Administering a battery of personality and risk-preference instruments spanning self-reports and behavioral tasks to 56 instruction-tuned LLMs alongside large human reference samples, we report four findings. First, differences between models are driven not by the traits an instrument targets but by a directional response bias, a tendency to respond toward one end of the scale, or one labeled option, regardless of item content; a variance decomposition attributes 81-90% of between-model variation to this bias, against 9-16% in humans. Second, the bias declines with model capability but is not eliminated by it. Third, because bias rather than trait drives responding, an instrument's apparent reliability is almost entirely predicted by its response orthogonality, a term we coin for the proportion of items for which trait and bias point in opposite directions. Fourth, the profile a model appears to have shifts with the items used and can be manufactured through item selection. These results demonstrate that the apparent psychological profiles of LLMs are artifacts of the instrument used to measure them, not properties of the models themselves. As instruments borrowed from human psychology are rarely fully orthogonal and may inherently lack validity for LLMs, we call for dedicated assessments centered on response orthogonality.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper administers a battery of personality and risk-preference instruments (self-report and behavioral) to 56 instruction-tuned LLMs and human reference samples. It reports four findings: (1) between-model differences are driven primarily by directional response bias (tendency toward one scale end or labeled option, independent of item content), with a variance decomposition attributing 81-90% of between-model variation to this bias versus 9-16% in humans; (2) the bias declines with model capability but is not eliminated; (3) instrument reliability is almost entirely predicted by 'response orthogonality' (proportion of items where trait and bias oppose); (4) apparent profiles shift with item selection and can be manufactured. The conclusion is that LLM psychological profiles are measurement artifacts, not model properties, and calls for new assessments centered on response orthogonality.

Significance. If the variance decomposition and orthogonality results hold under scrutiny, the work would substantially undermine the use of borrowed human psychological instruments for profiling LLMs in usability, safety, and research-proxy contexts. It supplies concrete empirical numbers from a large model sample and introduces a falsifiable construct (response orthogonality) that directly predicts reliability, offering a clear path for improved instrumentation.

major comments (2)
  1. [variance decomposition / findings 1] The central variance decomposition (abstract and § on findings) attributes 81-90% of between-model variation to directional bias under the assumption that bias is additive and orthogonal to trait effects. For LLMs this isolation is not obviously guaranteed, because next-token generation conditions on full item semantics and scale labels; any content-by-bias interaction would be misattributed to pure directional bias. Explicit residual diagnostics or simulation checks confirming orthogonality across the administered items are required to support the reported percentages.
  2. [response orthogonality definition and reliability prediction] Response orthogonality is defined and used to predict reliability (finding 3), yet the manuscript provides no direct test that the proportion of opposing items is independent of the specific trait scales or LLM prompting regime. If orthogonality itself covaries with item content or model capability, the predictive claim would be circular and the 81-90% bias attribution would be overstated.
minor comments (2)
  1. [methods] The abstract states four findings but the methods description of how bias was isolated from trait effects and how orthogonality was computed is referenced only at high level; a dedicated methods subsection with item lists, exact variance formulas, and human-LLM comparison tables would improve reproducibility.
  2. [finding 4] The claim that profiles 'can be manufactured through item selection' (finding 4) is important but would benefit from a supplementary table showing the exact item subsets and resulting profile shifts for at least two instruments.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for these constructive comments on the assumptions underlying our variance decomposition and the independence of the response orthogonality measure. We respond to each point below.

read point-by-point responses

  1. Referee: [variance decomposition / findings 1] The central variance decomposition (abstract and § on findings) attributes 81-90% of between-model variation to directional bias under the assumption that bias is additive and orthogonal to trait effects. For LLMs this isolation is not obviously guaranteed, because next-token generation conditions on full item semantics and scale labels; any content-by-bias interaction would be misattributed to pure directional bias. Explicit residual diagnostics or simulation checks confirming orthogonality across the administered items are required to support the reported percentages.

    Authors: We agree that the reported variance decomposition assumes additivity and orthogonality of directional bias to trait effects, and that next-token prediction could in principle introduce content-by-bias interactions. Our decomposition was obtained by fitting a linear model that separates an item-specific trait direction term from a model-specific directional bias term; the large between-model variance component attributed to bias is therefore conditional on that specification. To strengthen the claim, the revised manuscript will include (i) residual plots and formal tests for remaining interactions from the fitted models and (ii) Monte Carlo simulations that inject controlled content-by-bias interactions and recover the original attribution percentages. These additions will be placed in the methods and results sections alongside the existing decomposition. revision: yes

  2. Referee: [response orthogonality definition and reliability prediction] Response orthogonality is defined and used to predict reliability (finding 3), yet the manuscript provides no direct test that the proportion of opposing items is independent of the specific trait scales or LLM prompting regime. If orthogonality itself covaries with item content or model capability, the predictive claim would be circular and the 81-90% bias attribution would be overstated.

    Authors: Response orthogonality was computed on the same set of instruments administered to all 56 models, which already span multiple trait domains and a wide capability range; the reported reliability prediction held uniformly across those instruments. Nevertheless, we accept that an explicit check for covariance between orthogonality and either model capability or item features would further rule out circularity. The revision will therefore report (a) the correlation between orthogonality scores and model capability and (b) the correlation between orthogonality and item-level semantic features (e.g., length, polarity). If any modest covariance is detected, we will note its magnitude and re-estimate the reliability prediction after residualizing orthogonality on those covariates. revision: partial

Circularity Check

0 steps flagged

No significant circularity: empirical variance decomposition on administered instruments

full rationale

The paper's claims rest on direct administration of personality/risk instruments to 56 LLMs and human samples, followed by variance decomposition that partitions observed response variation into trait-targeted vs. directional bias components. The 81-90% bias attribution is an output of this decomposition applied to collected data, not a quantity defined in terms of itself or recovered by fitting parameters to the target result. 'Response orthogonality' is coined as a descriptive proportion of opposing items and then shown to empirically predict reliability; this is a post-hoc correlation on the data, not a self-definitional loop. No self-citation chains, uniqueness theorems, or ansatzes imported from prior author work appear in the derivation. The analysis is self-contained against external benchmarks of instrument administration and standard psychometric variance partitioning.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The paper contributes empirical measurements across 56 models and a new analytic concept (response orthogonality) rather than relying on many free parameters or unverified entities; the central analysis rests on the applicability of human psychometric tools to LLMs.

axioms (1)
  • domain assumption Psychometric instruments designed for humans can be meaningfully administered to LLMs to decompose responses into trait and bias components
    The entire analysis and variance decomposition rest on this separation being valid for non-human responders.
invented entities (1)
  • response orthogonality no independent evidence
    purpose: Quantifies the proportion of items for which trait and bias point in opposite directions to predict apparent reliability
    Newly coined term introduced to explain why some instruments appear more reliable than others for LLMs.

pith-pipeline@v0.9.1-grok · 5809 in / 1465 out tokens · 35382 ms · 2026-06-26T17:31:30.802845+00:00 · methodology

discussion (0)

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