arxiv: 2604.26460 · v1 · submitted 2026-04-29 · 💻 cs.CL

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Theory-Grounded Evaluation Exposes the Authorship Gap in LLM Personalization

Yash Ganpat Sawant

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Pith reviewed 2026-05-07 10:46 UTC · model grok-4.3

classification 💻 cs.CL

keywords authorship verificationLLM personalizationstylistic evaluationevaluation metricscalibrated baselinesinference-time methodsLUAR

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The pith

All LLM personalization methods score below the cross-author floor on calibrated authorship verification, revealing a gap invisible to standard metrics.

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

The paper establishes that stylistic personalization of LLMs requires evaluation grounded in authorship verification science to produce meaningful results. Drawing on the LUAR model for calibrated scoring alongside two other metrics, the authors test four inference-time methods across 50 authors and 1000 generations. All methods fall short of even the cross-author baseline, while the three metrics show near-zero correlations and reach opposing conclusions about which approach works. This demonstrates that ad hoc benchmarks cannot distinguish real stylistic success from failure.

Core claim

Grounding evaluation in authorship verification theory exposes that no current inference-time personalization method makes LLMs write in a specific individual's style. On the LUAR metric, which supplies absolute baselines with a human ceiling of 0.756 and cross-author floor of 0.626, all four methods score between 0.484 and 0.508. The same generations produce contradictory rankings when evaluated by an LLM judge or function-word stylometrics, with pairwise correlations below 0.07 in absolute value.

What carries the argument

The LUAR trained authorship verification model, which supplies calibrated baselines that turn raw similarity scores into interpretable measures of whether text matches a target author's style.

If this is right

Without theoretical grounding, evaluation conclusions about personalization methods are determined by arbitrary metric choice.
Current inference-time personalization approaches produce text that is statistically closer to other authors than to the target author.
The three tested metrics measure largely independent aspects of style, so no single one can serve as a complete success signal.
The theory-benchmark cycle can identify evaluation failures that ad hoc methods overlook.

Where Pith is reading between the lines

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

Closing the authorship gap may require methods beyond inference-time prompting, such as targeted fine-tuning on author-specific data.
Future benchmarks could combine multiple grounded metrics to avoid the single-metric dependency shown here.
The low correlations suggest that stylistic personalization success may need to be defined separately for different dimensions of writing.

Load-bearing premise

That the LUAR authorship verification model supplies valid calibrated baselines that meaningfully apply to LLM-generated text as a measure of stylistic personalization success.

What would settle it

A direct test of whether LUAR-assigned scores for LLM outputs predict human ability to attribute the text to the correct author versus a different one.

Figures

Figures reproduced from arXiv: 2604.26460 by Yash Ganpat Sawant.

**Figure 1.** Figure 1: LUAR authorship similarity by method with calibration baselines. All methods score below the cross-author human floor (0.626). The authorship gap between generated and human text is a measurable, calibrated quantity. 4 Results 4.1 The Human–LLM Authorship Gap view at source ↗

**Figure 2.** Figure 2: LUAR similarity vs. TMR for 1,000 generations (r=0.013). Profile extraction’s apparent advantage on TMR has no corresponding signal on LUAR. generate text optimized for exactly the kind of features the judge checks. The real author’s own text confirms this: TMR=0.427, lower than Profile Extraction’s 0.542. If TMR measured genuine authorship fidelity, the real author would set the ceiling. Instead, the m… view at source ↗

read the original abstract

Stylistic personalization - making LLMs write in a specific individual's style, rather than merely adapting to task preferences - lacks evaluation grounded in authorship science. We show that grounding evaluation in authorship verification theory transforms what benchmarks can measure. Drawing on three measurement traditions - LUAR, a trained authorship verification model; an LLM-as-judge with decoupled trait matching; and classical function-word stylometrics - we evaluate four inference-time personalization methods across 50 authors and 1,000 generations. The theory-grounded metric, LUAR, provides what ad hoc alternatives cannot: calibrated baselines, with a human ceiling of 0.756 and a cross-author floor of 0.626, that give scores absolute meaning. All methods score below this floor, from 0.484 to 0.508, exposing an authorship gap invisible to uncalibrated metrics. The three metrics produce near-zero pairwise correlations, with absolute r less than 0.07, confirming that without theoretical grounding, metric choice determines conclusions: an LLM judge declares a clear winner while LUAR finds no meaningful differentiation. These findings demonstrate the theory-benchmark cycle in action: authorship theory exposes evaluation failures that ad hoc benchmarks miss.

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 claims that current LLM stylistic personalization evaluations lack grounding in authorship verification theory. Using LUAR (a trained authorship verification model with human ceiling 0.756 and cross-author floor 0.626), an LLM-as-judge for decoupled trait matching, and classical function-word stylometrics, it evaluates four inference-time methods on 50 authors and 1,000 generations. All methods score 0.484–0.508 on LUAR (below the floor), exposing an 'authorship gap' invisible to uncalibrated metrics; the three metrics show near-zero pairwise correlations (|r| < 0.07), so metric choice dictates conclusions.

Significance. If the central results hold after addressing calibration concerns, the work is significant for demonstrating how authorship theory can supply absolute, calibrated baselines that ad-hoc metrics lack, and for exposing that inference-time personalization methods fail to achieve even cross-author stylistic similarity. A strength is the explicit comparison across three independent measurement traditions that reveals metric inconsistency, which could guide future benchmark design in LLM personalization.

major comments (2)

[§3] §3 (LUAR baselines): The central claim that scores of 0.484–0.508 indicate an authorship gap below the cross-author floor of 0.626 assumes LUAR's human-trained thresholds transfer directly to LLM-generated text. No control experiments establish an LLM-specific same-author ceiling or cross-author floor, leaving open that domain shift (e.g., repetition patterns, coherence artifacts) depresses scores independently of personalization quality. This assumption is load-bearing for interpreting the numerical gap as evidence of insufficient personalization rather than metric mismatch.
[§4] §4 (Experimental setup): The manuscript reports results across 50 authors and 1,000 generations but provides insufficient detail on author selection criteria, prompt construction, and generation parameters. Without these, it is difficult to assess whether the authorship gap finding generalizes or is sensitive to the specific data distribution, which directly affects the strength of the theory-benchmark cycle argument.

minor comments (2)

[Abstract] The abstract and §4.3 could include a brief table summarizing the four personalization methods (e.g., their core mechanisms) to help readers map the reported scores to specific techniques.
[§5] The reported correlations (|r| < 0.07) would benefit from explicit p-values or confidence intervals to substantiate the claim of 'near-zero' differentiation.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which help clarify the interpretation of our results and strengthen the experimental reporting. We address each major comment below and indicate the revisions made to the manuscript.

read point-by-point responses

Referee: [§3] §3 (LUAR baselines): The central claim that scores of 0.484–0.508 indicate an authorship gap below the cross-author floor of 0.626 assumes LUAR's human-trained thresholds transfer directly to LLM-generated text. No control experiments establish an LLM-specific same-author ceiling or cross-author floor, leaving open that domain shift (e.g., repetition patterns, coherence artifacts) depresses scores independently of personalization quality. This assumption is load-bearing for interpreting the numerical gap as evidence of insufficient personalization rather than metric mismatch.

Authors: We acknowledge that LUAR was trained exclusively on human text and that domain shift to LLM outputs could influence absolute scores. Nevertheless, the cross-author floor of 0.626 remains a theoretically grounded reference: it quantifies the minimum similarity between texts known to have different authors. Finding that all personalization methods fall below this threshold demonstrates that the generated text is less stylistically aligned with the target author than unrelated human authors are with one another. To directly address the referee's concern, we will add control experiments in the revised manuscript that compute LUAR scores on LLM-generated text (both personalized and non-personalized) against the same and different authors, thereby establishing LLM-specific baselines and isolating any domain-shift effects from the personalization gap. revision: yes
Referee: [§4] §4 (Experimental setup): The manuscript reports results across 50 authors and 1,000 generations but provides insufficient detail on author selection criteria, prompt construction, and generation parameters. Without these, it is difficult to assess whether the authorship gap finding generalizes or is sensitive to the specific data distribution, which directly affects the strength of the theory-benchmark cycle argument.

Authors: We agree that greater transparency in the experimental setup is required for reproducibility and to support claims about generalizability. The current manuscript provides these details primarily in the appendix; we will move and expand the relevant information into the main text of Section 4. Specifically, we will describe the author selection criteria (authors drawn from a public corpus with a minimum of ten writing samples each), the exact prompt templates and few-shot examples used for each of the four inference-time methods, and all generation hyperparameters (temperature, top-p, maximum length, and decoding strategy). These additions will allow readers to evaluate sensitivity to data distribution and reinforce the theory-benchmark cycle argument. revision: yes

Circularity Check

0 steps flagged

No circularity: baselines and scores are computed from external pre-trained model and independent human data

full rationale

The paper's central result—all personalization methods scoring 0.484–0.508 below the LUAR cross-author floor of 0.626—follows directly from applying a pre-existing trained authorship verification model (LUAR) to new LLM generations and comparing against separately established human-derived baselines (ceiling 0.756, floor 0.626). These baselines are not fitted or redefined within the paper; they are imported from the LUAR model's prior training on human authorship data. The near-zero correlations between LUAR, LLM-as-judge, and stylometric metrics are empirical observations across the 1,000 generations, not tautological re-labelings. No self-definitional equations, fitted-input predictions, or load-bearing self-citations appear in the derivation chain. The evaluation remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the assumption that authorship verification models provide transferable calibrated measures for LLM stylistic output; no free parameters or invented entities are introduced.

axioms (1)

domain assumption LUAR and function-word stylometrics supply reliable, calibrated proxies for individual stylistic authorship that extend to LLM-generated text
Invoked to interpret scores below the cross-author floor as evidence of an authorship gap.

pith-pipeline@v0.9.0 · 5503 in / 1293 out tokens · 46326 ms · 2026-05-07T10:46:09.077458+00:00 · methodology

discussion (0)

Reference graph

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