arxiv: 2605.14517 · v1 · submitted 2026-05-14 · 💻 cs.CL · cs.AI

Recognition: no theorem link

Dimension-Level Intent Fidelity Evaluation for Large Language Models: Evidence from Structured Prompt Ablation

Gang Peng

Authors on Pith no claims yet

Pith reviewed 2026-05-15 01:46 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords intent fidelityLLM evaluationprompt ablationdimensional assessmentholistic scoresstructural recoverymultilingual outputsquality deficits

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The pith

Many LLM outputs with perfect holistic scores still miss user intent on specific dimensions.

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

The paper introduces a dimension-level intent fidelity evaluation framework that uses structured prompt ablation to measure structural recovery and intent preservation separately for each semantic dimension. Applied to 2,880 outputs across three languages, three domains, and six models, the approach reveals a structural-fidelity split in which a notable fraction of outputs achieve perfect overall scores yet show measurable deficits in one or more intent dimensions. This split is larger for English outputs than for Chinese ones, and human evaluation confirms that the dimensional scores align more closely with actual quality judgments than holistic scores do. The work also decomposes when models compensate for missing intent information and when they cannot.

Core claim

Holistic evaluation scores capture overall output quality but do not distinguish whether a model reproduced the structural form of a user's request from whether it preserved the user's specific intent. A dimension-level framework applied through structured prompt ablation across 2,880 outputs shows a systematic split: among Chinese outputs with complete paired scores, 25.7 percent received perfect holistic alignment while exhibiting measurable dimensional intent deficits, and among English outputs this proportion rose to 58.6 percent. Human evaluation confirmed these split-zone outputs represent genuine quality deficits, and a public-private decomposition of ablation cells together with a 2,

What carries the argument

Structured prompt ablation that separately measures structural recovery and intent fidelity for each semantic dimension, paired with proxy annotation to distinguish prior inferability from default recoverability.

If this is right

Holistic scores alone are insufficient to assess LLM performance on tasks where users care about specific intent dimensions.
Dimensional fidelity scores track human judgments more reliably than holistic scores across the tested languages and domains.
Models can compensate for missing intent information in some ablation cells but fail to do so in others, as shown by the public-private decomposition.
Moderate dimensional misalignment is typically absorbed while severe inversion consistently harms output quality.

Where Pith is reading between the lines

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

Prompt engineering practices could incorporate explicit checks for each semantic dimension to reduce the frequency of these hidden deficits.
The framework could be extended to additional languages or task types to test whether the English-Chinese difference in split rates generalizes.
Training objectives that directly optimize dimensional fidelity rather than only holistic reward signals might reduce the observed compensation failures.

Load-bearing premise

Structured prompt ablation and proxy annotation reliably isolate prior inferability from default recoverability without introducing selection bias or confounding the human validation of split-zone outputs.

What would settle it

If a replication using a different ablation design or different human annotators finds no reliable difference between dimensional scores and holistic scores on the same split-zone outputs, the claim that dimension-level evaluation is a necessary complement would be falsified.

Figures

Figures reproduced from arXiv: 2605.14517 by Gang Peng.

**Figure 1.** Figure 1: Dimension-level evaluation and human validation of the structural-fidelity split. (a) Experimental design. (b) GA vs. f-ICMw scatter (ZH, N=1,440); split zone (25.7%) highlighted. (c) GA ceiling effect: 84.7% at GA=5. (d) Human validation: split-zone human mean GA=3.12 vs. LLM GA=5.0. 2.2 Structural recovery systematically diverges from intent fidelity The s-ICMw ablation hypothesis (H2s: ablating dimensio… view at source ↗

**Figure 3.** Figure 3: Weight-tolerance plateau. (a) f-ICMw by condition (v2 and v3_clean). (b) Gap ratio: Perturbed– Mismatched vs. Uniform–Perturbed (~15–25×). (c) WAS drop under mismatched: 100% consistent. 2.5 Cross-language and cross-model robustness The domain-level ordering of public rates (travel > business ≈ technical) and dimension-level ordering (How-to-do most public, Who most private) were preserved across ZH, EN, a… view at source ↗

read the original abstract

Holistic evaluation scores capture overall output quality but do not distinguish whether a model reproduced the structural form of a user's request from whether it preserved the user's specific intent. We propose a dimension-level intent fidelity evaluation framework, applied here through a structured prompt ablation study across 2,880 outputs spanning three languages, three task domains, and six LLMs, that separately measures structural recovery and intent fidelity for each semantic dimension. This framework reveals a systematic structural-fidelity split: among Chinese-language outputs with complete paired scores, 25.7% received perfect holistic alignment scores (GA=5) while exhibiting measurable dimensional intent deficits; among English-language outputs, this proportion rose to 58.6%. Human evaluation confirmed that these split-zone outputs represent genuine quality deficits and that dimensional fidelity scores track human judgements more reliably than holistic scores do. A public-private decomposition of 2,520 ablation cells characterises when models successfully compensate for missing intent and when they fail, while proxy annotation distinguishes prior inferability from default recoverability. A weight-perturbation experiment shows that moderate misalignment is typically absorbed, whereas severe dimensional inversion is consistently harmful. These findings demonstrate that dimension-level intent fidelity evaluation is a necessary complement to holistic assessment when evaluating LLM outputs for user-specific tasks.

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.

Desk Editor's Note private letter to a colleague

The paper shows many perfect holistic scores hide real dimensional intent shortfalls in LLMs, with 58.6% of English cases affected, but the numbers rest on a complete-pairs subset that may not be representative.

read the letter

The main point is that holistic alignment scores can look perfect while specific intent dimensions are still off. They report 25.7% of Chinese outputs and 58.6% of English ones fall into this split zone among the complete paired cases, drawn from 2880 outputs across three languages, three domains, and six models. Human raters confirmed these are genuine deficits and that the dimensional scores track judgments better than the overall score does. A weight-perturbation check also shows moderate misalignment gets absorbed but severe inversion does not. The public-private breakdown of the 2520 ablation cells and the proxy annotation for inferability versus recoverability give the framework some extra structure that standard holistic metrics lack. That is the useful part. The soft spot is the conditioning on complete paired scores for the headline percentages. If incompletes occur more often when intent recovery fails, the reported split rates will not reflect the full distribution, and the abstract gives no sensitivity checks or randomness test on the subset. Without the full methods it is hard to judge how much that matters. This work is for people who need tighter evaluation of user-specific intent in LLM outputs rather than broad quality scores. It has enough scale and a practical angle to deserve peer review, though the selection issue will need explicit handling in revision.

Referee Report

1 major / 2 minor

Summary. The paper proposes a dimension-level intent fidelity evaluation framework for LLMs, implemented via structured prompt ablation on 2,880 outputs across three languages, three task domains, and six models. It reports a structural-fidelity split in which 25.7% of Chinese-language and 58.6% of English-language outputs with complete paired scores achieve perfect holistic alignment (GA=5) yet exhibit measurable dimensional intent deficits; human validation, public-private decomposition of 2,520 ablation cells, proxy annotation, and weight-perturbation checks are presented as supporting evidence that dimensional scores track human judgments more reliably than holistic scores.

Significance. If the central split-zone proportions and human-validation results hold after addressing subset conditioning, the work supplies a concrete, scalable complement to holistic metrics for user-specific LLM evaluation. The scale (2,880 outputs), multi-language coverage, and inclusion of perturbation and proxy-annotation controls are strengths that would make the framework useful for both benchmarking and alignment research.

major comments (1)

[Abstract and results reporting complete paired scores] The central quantitative claims (25.7% Chinese, 58.6% English split-zone outputs) are conditioned on the subset of outputs that received complete paired scores. The manuscript must demonstrate that incompleteness is independent of dimensional intent deficit or holistic score, for example by reporting the distribution of incompleteness across GA levels or by sensitivity checks that include or impute incomplete cases. Without such checks, selection bias remains a plausible alternative explanation for the reported proportions.

minor comments (2)

[Methods] Clarify the exact criteria used to define a 'complete paired score' and how proxy annotation distinguishes prior inferability from default recoverability; these definitions are load-bearing for interpreting the ablation cells.
[Human evaluation subsection] Ensure that the human-evaluation protocol (number of annotators, inter-annotator agreement, and exact instructions for rating dimensional deficits) is reported in sufficient detail for replication.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the detailed and constructive review. The major comment raises a valid concern about potential selection bias in our reported proportions, which we address below by committing to additional analyses in the revision.

read point-by-point responses

Referee: [Abstract and results reporting complete paired scores] The central quantitative claims (25.7% Chinese, 58.6% English split-zone outputs) are conditioned on the subset of outputs that received complete paired scores. The manuscript must demonstrate that incompleteness is independent of dimensional intent deficit or holistic score, for example by reporting the distribution of incompleteness across GA levels or by sensitivity checks that include or impute incomplete cases. Without such checks, selection bias remains a plausible alternative explanation for the reported proportions.

Authors: We agree that the central claims are conditioned on the subset of outputs with complete paired scores and that this requires explicit checks for independence from GA levels or dimensional deficits. In the revised manuscript we will add (i) a table or figure showing the distribution of incompleteness rates across all GA levels (1-5) for each language and domain, and (ii) sensitivity analyses that either impute missing dimensional scores under conservative assumptions or re-compute the split-zone proportions after including all available (even partial) cases. These additions will directly test whether incompleteness correlates with the variables of interest and will allow readers to assess robustness. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical framework

full rationale

The paper presents an empirical study based on direct annotation of 2,880 outputs from structured prompt ablation across languages, domains, and models. Reported proportions (25.7% Chinese, 58.6% English split-zone cases) are computed from human-validated complete paired scores without any equations, fitted parameters, or derivations that reduce the fidelity metrics to inputs defined by the same data. The dimension-level framework is introduced independently, with proxy annotation and weight-perturbation checks serving as external validation rather than self-referential definitions. No load-bearing self-citations or ansatz smuggling appear in the provided text; the central claims rest on observable output properties and human judgments.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The framework rests on the assumption that user intent can be decomposed into independent semantic dimensions and that prompt ablation isolates recoverability without external validation of those dimensions.

axioms (2)

domain assumption Intent can be decomposed into measurable semantic dimensions that are independent of overall structure.
Invoked in the definition of the dimension-level evaluation framework and the split-zone analysis.
domain assumption Human judgments reliably distinguish genuine intent deficits from structural issues in split-zone outputs.
Used to confirm that dimensional scores track human judgments better than holistic scores.

pith-pipeline@v0.9.0 · 5514 in / 1237 out tokens · 28283 ms · 2026-05-15T01:46:31.109511+00:00 · methodology

discussion (0)

Reference graph

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