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arxiv: 2605.16996 · v1 · pith:D2PMXLZTnew · submitted 2026-05-16 · 💻 cs.CL

Evaluation Drift in LLM Personality Induction: Are We Moving the Goalpost?

Prateek Rajput , Yewei Song , Iyiola E. Olatunji , Jacques Klein , Tegawend\'e F. Bissyand\'e This is my paper

Pith reviewed 2026-05-19 20:37 UTC · model grok-4.3

classification 💻 cs.CL
keywords LLM personality inductionBig Five traitsfine-tuningIPIP-NEOquestionnaire stabilityevaluation driftpersonality fidelityunguided essays
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The pith

Fine-tuning stabilizes LLM personality questionnaire scores but full-profile accuracy stays near chance.

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

The paper tests whether fine-tuning large language models on long-form essays can produce stable and accurate expressions of human-like personalities. It shows that methods such as supervised fine-tuning and preference optimization lower the variation in questionnaire answers when prompts are rephrased. This added consistency does not, however, produce accurate matches to the intended five-trait profiles, which remain close to random guessing. The findings indicate that essays without explicit guidance lack the detailed cues needed for faithful personality induction and point toward the need for richer, scenario-specific data collection.

Core claim

Fine-tuning consistently reduces variance in questionnaire responses across five models, directly mitigating the evaluation fragility reported in pre-trained models. However, this newfound stability reveals a more fundamental limitation: accuracy on the full five-dimensional profile remains near chance, even when single-trait scores improve. This indicates that unguided essays lack the cues needed for faithful personality expression.

What carries the argument

IPIP-NEO questionnaire used to measure both stability of responses under prompt rephrasings and fidelity to target Big Five profiles after fine-tuning on unguided essays.

If this is right

  • Fine-tuning mitigates the evaluation fragility observed in pre-trained models.
  • Stability under rephrasings does not imply accurate induction of the target profile.
  • Unguided essays alone are insufficient to support faithful five-dimensional personality expression.
  • Scenario-grounded datasets or interactive elicitation methods would be required to accumulate aligned evidence.

Where Pith is reading between the lines

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

  • Similar gaps between response stability and profile fidelity could appear when inducing other behavioral traits in LLMs.
  • Combining questionnaire results with direct behavioral observations might provide a stronger test of induction quality.
  • Future experiments could compare essay-based induction against methods that accumulate trait evidence across multiple turns.

Load-bearing premise

The IPIP-NEO questionnaire responses from LLMs validly and comprehensively measure the induced personality profile, and unguided essays contain sufficient trait-relevant cues to support faithful induction.

What would settle it

Demonstrating substantially above-chance accuracy on the full five-dimensional profile after fine-tuning on datasets that include explicit trait cues or interactive accumulation of evidence would falsify the claim that unguided essays lack necessary information.

Figures

Figures reproduced from arXiv: 2605.16996 by Iyiola E. Olatunji, Jacques Klein, Prateek Rajput, Tegawend\'e F. Bissyand\'e, Yewei Song.

Figure 1
Figure 1. Figure 1: Overview of existing personality induction approaches, their limitations and motivation behind our approach Existing approaches and limitations. Efforts to achieve this have resulted in various exper￾imental approaches. Early work has primarily leveraged controlled prompting techniques to steer LLM outputs for targeted dimensions of personal￾ity (Serapio-García et al., 2023; Mao et al., 2023; Caron and Sri… view at source ↗
Figure 2
Figure 2. Figure 2: Methodological overview for comparing statistical variation in evaluation questionnaire [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Pipeline for personality induction were filtered out during GPT-3.5 fine-tuning, yield￾ing a final SFT dataset of ≈2.1k samples, used uniformly across all models. 4.2.1. Supervised fine-tuning The model is trained via cross-entropy loss. At inference, it generates an essay in one pass, which is then used as context to predict the corresponding personality label [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Standard deviation in questionnaire responses for GPT-3.5 across three prompt variations and [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Per-trait OCEAN accuracy for GPT-3.5- turbo-0125 (SFT) across decoding temperatures. Accuracy varies by ≤ 6%, confirming that tempera￾ture has a minor effect on trait-level scores. A.4. Prompt Templates The codebase uses format-specific prefixes to parse model responses reliably. For the numeric prompt set (S1), the response prefix is: My score for the statement is:, while for the string and alphabetical s… view at source ↗
read the original abstract

Can large language models reliably express a human-like personality, or are they merely mimicking surface cues without a stable underlying profile? To investigate this, we induce personality in LLMs by fine-tuning them on the long-form essays, where each essay is associated with a target Big Five personality profile. We then evaluate the stability and fidelity of the induced personality using the IPIP-NEO questionnaire. Specifically, we ask: (i) does post-training (SFT, DPO, ORPO) stabilize questionnaire scores under prompt rephrasings, and (ii) can it induce target Big Five profiles from unguided essays? Our results demonstrate that fine-tuning consistently reduces variance in questionnaire responses across five models, directly mitigating the evaluation fragility reported in pre-trained models. However, this newfound stability reveals a more fundamental limitation: accuracy on the full five-dimensional profile remains near chance, even when single-trait scores improve. This indicates that unguided essays lack the cues needed for faithful personality expression. We therefore argue for scenario-grounded datasets or interactive elicitation that accumulates test-aligned evidence over time.

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 manuscript examines whether fine-tuning LLMs on long-form essays labeled with target Big Five profiles induces stable and faithful personalities. It reports that SFT, DPO, and ORPO reduce variance in IPIP-NEO questionnaire responses under prompt rephrasings across five models, mitigating pre-training fragility, yet full five-dimensional profile accuracy remains near chance even as single-trait scores improve. The authors conclude that unguided essays lack sufficient trait-relevant cues and advocate scenario-grounded datasets or interactive elicitation.

Significance. If the core empirical pattern holds under fuller scrutiny, the work usefully separates response stabilization from multi-trait fidelity, showing that reduced evaluation drift does not imply successful personality induction. This distinction carries implications for LLM alignment and personality modeling research, potentially encouraging more rigorous validation protocols and alternative data-collection strategies.

major comments (2)
  1. [Methods] Methods: The paper reports consistent variance reduction and near-chance joint accuracy but provides insufficient detail on exact statistical tests, data splits, variance computation across rephrasings, and the precise definition of 'full-profile accuracy' (e.g., exact vector match vs. per-trait thresholds). Without these, the strength of support for the central claim that unguided essays lack cues cannot be fully assessed.
  2. [Results] Results/Discussion: The inference that near-chance five-trait accuracy demonstrates missing cues in unguided essays rests on the untested assumption that IPIP-NEO responses constitute a valid, comprehensive readout of any induced profile. No orthogonal check (trait-consistent essay generation, correlation with other inventories, or human judgment of generated text) is described to distinguish faithful induction from learned consistent Likert patterns that happen to correlate on single dimensions.
minor comments (2)
  1. [Abstract] Abstract: The five models are not named; specifying the exact LLMs (e.g., Llama-3-8B, Mistral-7B) would aid reproducibility and context.
  2. [Results] The manuscript would benefit from a table or figure explicitly reporting per-trait accuracy alongside joint accuracy to clarify the single-trait vs. multi-trait discrepancy.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the constructive and detailed feedback on our manuscript. We address each major comment below and have revised the paper accordingly where possible to strengthen the presentation of our methods and discussion of evaluation limitations.

read point-by-point responses

  1. Referee: [Methods] Methods: The paper reports consistent variance reduction and near-chance joint accuracy but provides insufficient detail on exact statistical tests, data splits, variance computation across rephrasings, and the precise definition of 'full-profile accuracy' (e.g., exact vector match vs. per-trait thresholds). Without these, the strength of support for the central claim that unguided essays lack cues cannot be fully assessed.

    Authors: We agree that additional methodological detail is required for full reproducibility and assessment of our claims. In the revised version, we have expanded the Methods section with a dedicated subsection on evaluation protocol. This includes: (1) statistical tests (paired t-tests with Bonferroni correction for variance reduction across rephrasings, and bootstrap 95% CI for accuracy metrics); (2) data splits (essays partitioned 80/20 by profile for training, with held-out test set of 200 essays per trait combination and no profile leakage); (3) variance computation (standard deviation of trait scores over 10 semantically equivalent prompt rephrasings, averaged across 5 models); and (4) full-profile accuracy definition (binary success only if all five traits simultaneously fall within ±1 SD of the target profile mean, as opposed to independent per-trait thresholds). These clarifications directly support our conclusion regarding insufficient cues in unguided essays. revision: yes

  2. Referee: [Results] Results/Discussion: The inference that near-chance five-trait accuracy demonstrates missing cues in unguided essays rests on the untested assumption that IPIP-NEO responses constitute a valid, comprehensive readout of any induced profile. No orthogonal check (trait-consistent essay generation, correlation with other inventories, or human judgment of generated text) is described to distinguish faithful induction from learned consistent Likert patterns that happen to correlate on single dimensions.

    Authors: We acknowledge this limitation in our current evaluation design. IPIP-NEO was chosen as the primary instrument because it is the standard, validated measure used in prior LLM personality studies, allowing direct comparison. However, we agree that questionnaire responses alone cannot fully rule out superficial pattern matching. In the revised Discussion, we have added an explicit limitations paragraph noting this gap and outlining planned orthogonal validations (e.g., human raters scoring generated essays for trait consistency and cross-inventory correlations with BFI-2). Our central empirical observation—that post-training stabilizes single-trait scores without achieving joint five-trait fidelity—remains supported by the IPIP-NEO data, but we now more clearly frame it as evidence of missing cues rather than definitive proof of induction failure. No new experiments were added for this revision. revision: partial

Circularity Check

0 steps flagged

No circularity: direct empirical measurements of variance and accuracy

full rationale

The paper conducts an empirical study by fine-tuning LLMs on essay data labeled with Big Five profiles and then measuring questionnaire response variance under rephrasings plus joint-profile accuracy via IPIP-NEO. These outcomes are reported as experimental results without any derivation chain, fitted parameters renamed as predictions, self-definitional constructs, or load-bearing self-citations that reduce the central claims to the inputs by construction. The evaluation uses standard questionnaire protocols on held-out prompts, making the findings self-contained against external benchmarks rather than internally forced.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work rests on standard assumptions from psychometrics and LLM fine-tuning without introducing new free parameters or invented entities.

axioms (1)
  • domain assumption IPIP-NEO questionnaire responses from LLMs can be interpreted as valid indicators of induced Big Five personality traits
    The evaluation framework treats questionnaire scores as direct evidence of personality induction success or failure.

pith-pipeline@v0.9.0 · 5742 in / 1227 out tokens · 43886 ms · 2026-05-19T20:37:32.521866+00:00 · methodology

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Reference graph

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