arxiv: 2605.02069 · v1 · submitted 2026-05-03 · 💻 cs.CL · cs.AI· cs.LG

Recognition: 2 theorem links

· Lean Theorem

Pair2Score: Pairwise-to-Absolute Transfer for LLM-Based Essay Scoring

\.Ibrahim R{\i}za Halla\c{c} , Hasan O\u{g}ul

Authors on Pith no claims yet

Pith reviewed 2026-05-08 19:15 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.LG

keywords pairwise-to-absolute transferautomated essay scoringSiamese rankerLLM adaptationtrait scoringquadratic weighted kappatransfer learning

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The pith

A two-stage transfer from pairwise rankings to absolute scores improves essay trait prediction with adapted LLMs.

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

The paper introduces Pair2Score as a framework that converts absolute trait labels into pairwise comparisons for an initial training stage, then transfers that knowledge to train an absolute scorer. This setup uses a directional Siamese ranker followed by configurable strategies such as warm-start initialization or embedding fusion during parameter-efficient adaptation. A sympathetic reader would care because absolute scoring is required for most real applications yet pairwise objectives can be simpler to optimize and may yield better calibrated predictions. Evaluation on grammar, vocabulary, and syntax traits under five-fold cross-validation shows that the strongest transfer variant raises quadratic weighted kappa over a direct absolute baseline for every trait. The work also demonstrates that transfer success depends on specific design choices rather than the mere presence of a pairwise stage.

Core claim

Pair2Score establishes that training a directional Siamese ranker on pairwise comparisons derived from absolute trait labels, followed by transfer via warm-start or embedding-fusion into an absolute predictor, produces higher quadratic weighted kappa scores than training the absolute scorer alone on the same LLM backbone.

What carries the argument

The directional Siamese ranker in stage one that learns from pairwise comparisons converted from absolute labels, combined with the configurable transfer mechanisms (warm-start and embedding-fusion) in stage two that adapt the absolute scorer.

If this is right

The best transfer configuration raises quadratic weighted kappa over the absolute-only baseline for grammar, vocabulary, and syntax.
A single-epoch pairwise stage transfers more reliably than extended pairwise training.
Transfer configuration, rather than the inclusion of pairwise training alone, determines whether absolute scoring benefits.
The five-fold protocol that co-rotates held-out folds with random seeds provides a robust test of the transfer gains.

Where Pith is reading between the lines

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

The method could lower the cost of collecting large absolute-labeled datasets by substituting easier pairwise judgments.
Similar pairwise-to-absolute transfer might apply to other regression-style NLP tasks such as readability assessment or sentiment intensity prediction.
Tuning the duration of the pairwise stage offers a practical lever for further performance gains without changing the overall architecture.

Load-bearing premise

Pairwise comparisons obtained by converting absolute trait labels carry transferable signal that improves absolute prediction without introducing systematic bias from the conversion step.

What would settle it

A controlled run in which every transfer variant yields equal or lower quadratic weighted kappa than the absolute-only baseline on grammar, vocabulary, and syntax would falsify the central claim.

Figures

Figures reproduced from arXiv: 2605.02069 by Hasan O\u{g}ul, \.Ibrahim R{\i}za Halla\c{c}.

**Figure 1.** Figure 1: Combined conceptual-and-protocol overview of Pair2Score. The top panel situates Pair2Score in the view at source ↗

**Figure 2.** Figure 2: Stage 1 directional Siamese objective. A shared LLaMA+LoRA backbone processes both documents; view at source ↗

read the original abstract

Many scoring applications require absolute predictions, while pairwise comparisons can provide a simpler learning objective. We present Pair2Score, a two-stage learning framework that transfers pairwise comparisons into absolute scoring with parameter-efficient LLaMA adaptation. Stage 1 trains a directional Siamese ranker on pairwise comparisons derived from absolute trait labels; Stage 2 trains an absolute predictor using configurable transfer strategies (warm-start and embedding-fusion variants). We evaluate on rubric-aligned Automated Essay Scoring (AES) traits (grammar, vocabulary, syntax) under a five-fold protocol that co-rotates held-out fold and random seed. At the trait level, the best-performing transfer variant improves quadratic weighted kappa (QWK) over an absolute-only baseline for all three traits. However, not all transfer configurations help: a one-epoch pairwise stage transfers more reliably than extended pairwise training, and transfer configuration -- not just the inclusion of a pairwise stage -- determines whether downstream scoring benefits.

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

Pair2Score gets modest QWK lifts on AES traits via a two-stage Siamese-to-absolute transfer, but the gains may trace to training schedule rather than the pairwise signal itself.

read the letter

The core takeaway is that this two-stage setup trains a directional Siamese ranker on pairs built from absolute trait labels, then transfers via warm-start or embedding fusion into an absolute LLaMA predictor, and the best configuration beats a plain absolute baseline on grammar, vocabulary, and syntax. The abstract notes that many transfer choices fail and that one-epoch pairwise training transfers more reliably than longer runs. That pattern already suggests the benefit is fragile and tied to schedule details rather than a robust transfer effect. What looks new is the concrete pairing of configurable warm-start and embedding-fusion variants with Siamese ranking for rubric-aligned AES traits; prior work has used Siamese models and parameter-efficient adaptation separately, but the specific pipeline and its sensitivity analysis appear fresh in this narrow setting. The evaluation protocol with five-fold co-rotation of held-out folds and seeds is a solid choice for this task and gives some protection against seed-specific artifacts. The paper earns credit for flagging that not every transfer configuration helps, which keeps the claims grounded. The soft spots are more substantial. Because the pairs are deterministically derived from the same absolute labels used in stage two, the ranker objective carries redundant information; any QWK improvement could come from extra gradient steps, the adaptation mechanics, or implicit regularization instead of genuine pairwise-to-absolute transfer. The abstract supplies no error bars, no significance tests, and no compute-matched baseline that runs the same total steps without the pairwise stage, so the central claim stays provisional. Full verification of the label-conversion process and potential bias would be needed. This paper is for people already working on automated essay scoring or parameter-efficient LLM adaptation who need small practical gains on rubric traits. A reader in that niche could extract the transfer variants and test them, but the work does not open new capabilities or resolve deeper questions in ranking or scoring. It deserves a serious referee because the method is explicit, the evaluation protocol is reasonable, and the authors already surface the configuration sensitivity. I would send it for review with instructions to add statistical checks and a stronger control that isolates the pairwise contribution from extra training.

Referee Report

3 major / 2 minor

Summary. The paper proposes Pair2Score, a two-stage framework for transferring pairwise ranking signals to absolute LLM-based essay scoring. Stage 1 trains a directional Siamese ranker on pairs constructed deterministically from absolute trait labels (grammar, vocabulary, syntax); Stage 2 applies configurable transfer (warm-start, embedding fusion) to train an absolute predictor. Under a five-fold co-rotating protocol, the best transfer variant is claimed to raise quadratic weighted kappa (QWK) over an absolute-only baseline for all three traits, though the authors note that not all configurations succeed and that one-epoch pairwise pre-training transfers more reliably.

Significance. If the reported QWK gains are shown to arise from genuine pairwise-to-absolute transfer rather than extra gradient steps or schedule effects, the work would supply a practical, parameter-efficient route for improving rubric-aligned AES with LLMs. The emphasis on transfer-configuration ablations and the explicit caveat about unreliable configurations are useful for practitioners.

major comments (3)

[§4, Table 2] §4 (Experiments) and Table 2: No error bars, standard deviations, or statistical significance tests (e.g., paired t-tests or Wilcoxon) are reported for the trait-level QWK differences. Without these, the claim that the best variant “improves … for all three traits” cannot be evaluated for reliability.
[§3.1–3.2] §3.1–3.2: Pairwise labels are generated by direct comparison of the same absolute trait scores later used in Stage 2 (A > B iff score_A > score_B). This makes the Stage-1 ranking objective informationally redundant with the absolute labels. A compute-matched baseline that simply continues absolute training for the same number of additional epochs is required to isolate any transfer benefit from multi-stage training dynamics.
[§4.3] §4.3 (Ablations): The manuscript states that “one-epoch pairwise stage transfers more reliably” and that “transfer configuration—not just the inclusion of a pairwise stage—determines” success, yet provides no ablation that holds total compute fixed while varying only the presence of the pairwise objective. This leaves open the possibility that observed gains are schedule artifacts.

minor comments (2)

[§4.1] The five-fold protocol description should explicitly state whether the same random seed is used across all folds or whether seeds are re-sampled, as this affects reproducibility of the reported QWK values.
[§3.3] Notation for the embedding-fusion variants (e.g., how the Siamese encoder output is injected into the absolute predictor) is introduced only in prose; a small diagram or equation would improve clarity.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments, which highlight key areas for improving the rigor of our experimental claims. We address each major comment below and will incorporate the necessary revisions and additional analyses into the manuscript.

read point-by-point responses

Referee: [§4, Table 2] §4 (Experiments) and Table 2: No error bars, standard deviations, or statistical significance tests (e.g., paired t-tests or Wilcoxon) are reported for the trait-level QWK differences. Without these, the claim that the best variant “improves … for all three traits” cannot be evaluated for reliability.

Authors: We agree that variability measures and significance testing are essential for reliable interpretation of the QWK gains. In the revised manuscript, we will augment Table 2 to report both the mean QWK and the standard deviation across the five folds for every trait and configuration. We will also add paired Wilcoxon signed-rank tests comparing the best transfer variant against the absolute baseline for each trait, including test statistics and p-values. revision: yes
Referee: [§3.1–3.2] §3.1–3.2: Pairwise labels are generated by direct comparison of the same absolute trait scores later used in Stage 2 (A > B iff score_A > score_B). This makes the Stage-1 ranking objective informationally redundant with the absolute labels. A compute-matched baseline that simply continues absolute training for the same number of additional epochs is required to isolate any transfer benefit from multi-stage training dynamics.

Authors: Although the pairwise labels derive from the same absolute scores, the Stage-1 directional Siamese ranking loss operates on a different objective than absolute regression, potentially yielding comparative representations that benefit transfer. To isolate transfer effects from multi-stage training dynamics, we will add a compute-matched absolute baseline in the revised experiments: the absolute-only model will be trained for a total epoch count equal to the sum of epochs used in the two-stage Pair2Score pipeline. Updated results and comparisons will appear in §4. revision: yes
Referee: [§4.3] §4.3 (Ablations): The manuscript states that “one-epoch pairwise stage transfers more reliably” and that “transfer configuration—not just the inclusion of a pairwise stage—determines” success, yet provides no ablation that holds total compute fixed while varying only the presence of the pairwise objective. This leaves open the possibility that observed gains are schedule artifacts.

Authors: We acknowledge that the existing ablations do not explicitly hold total compute constant when varying the pairwise stage. In the revised §4.3 we will introduce a fixed-compute ablation that compares (i) absolute-only training for the full epoch budget against (ii) 1-epoch pairwise followed by the remaining epochs of absolute training (and other allocations) while keeping total steps identical. This will clarify whether gains arise from the pairwise objective itself or from schedule differences. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical two-stage pipeline with held-out evaluation

full rationale

The paper describes an empirical two-stage training procedure (directional Siamese ranker on label-derived pairs followed by absolute predictor with warm-start or embedding fusion) and reports QWK improvements on held-out folds against an absolute-only baseline. No equations, derivations, or self-citations reduce the claimed improvements to a fitted parameter, self-definition, or prior result by construction. The evaluation protocol (five-fold co-rotation of held-out data and seed) keeps the central claim externally falsifiable and independent of the method's internal label conversion mechanics.

Axiom & Free-Parameter Ledger

2 free parameters · 0 axioms · 0 invented entities

The framework rests on standard supervised fine-tuning assumptions for LLMs and the premise that absolute labels can be losslessly converted to pairwise comparisons; no new mathematical axioms or invented physical entities are introduced.

free parameters (2)

pairwise training duration
Abstract states one-epoch pairwise stage transfers more reliably than extended training, implying this hyperparameter is tuned or selected post-hoc.
transfer configuration choice
Warm-start versus embedding-fusion variants are configurable and determine whether benefit occurs.

pith-pipeline@v0.9.0 · 5474 in / 1206 out tokens · 62057 ms · 2026-05-08T19:15:28.226345+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

Cost.FunctionalEquation (J(x)=½(x+x⁻¹)−1) washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Δ(a,b) = s(h_a) − s(h_b); Δ(b,a) = −Δ(a,b); pairwise logistic loss L_rel = log(1+exp(−Δ(a,b)))

What do these tags mean?

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supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
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uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

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