NCL-UoR at SemEval-2026 Task 5: Embedding-Based Methods, Fine-Tuning, and LLMs for Word Sense Plausibility Rating

Huizhi Liang; Thanet Markchom; Tong Wu

arxiv: 2603.08256 · v2 · submitted 2026-03-09 · 💻 cs.CL

NCL-UoR at SemEval-2026 Task 5: Embedding-Based Methods, Fine-Tuning, and LLMs for Word Sense Plausibility Rating

Tong Wu , Thanet Markchom , Huizhi Liang This is my paper

Pith reviewed 2026-05-15 14:43 UTC · model grok-4.3

classification 💻 cs.CL

keywords word sense plausibilityLLM promptingstructured reasoningSemEvalembedding methodsfine-tuningnarrative analysishomonym disambiguation

0 comments

The pith

Structured prompting with narrative decomposition and decision rules outperforms fine-tuning and embeddings for word sense plausibility rating.

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

The paper evaluates three strategies for scoring how plausible a word sense feels within short ambiguous stories on a 1-5 scale. Embedding methods pair sentence vectors with regressors, fine-tuning adapts transformers efficiently, and LLM prompting uses a breakdown of the story plus fixed scoring rules. The prompting method that splits each narrative into precontext, target sentence, and ending, then applies calibration rules, achieves the best results. This matters because it shows that explicit reasoning structures in prompts can surpass both traditional machine learning and model adaptation for judging contextual meaning. If correct, it implies that careful prompt engineering offers a more efficient path than scaling or retraining for tasks involving human perception of ambiguity.

Core claim

The central claim is that large language model prompting, when structured to decompose the input narrative into precontext, target sentence, and ending components and equipped with explicit decision rules for rating calibration, delivers superior performance in predicting human-perceived word sense plausibility compared to embedding-based regressors and parameter-efficient fine-tuning of transformers, with the analysis indicating that prompt design outweighs model scale in effectiveness for this task.

What carries the argument

Structured prompting strategy that decomposes evaluation into narrative components (precontext, target sentence, ending) and applies explicit decision rules for rating calibration.

Load-bearing premise

That the observed performance gains stem primarily from the prompting structure and decision rules rather than from differences in implementation details, tuning effort, or the particular characteristics of the evaluation dataset.

What would settle it

Running the same comparison on a fresh set of narratives with different ambiguous words, while controlling for total engineering effort across methods, and finding that a fine-tuned model matches or exceeds the structured prompt would falsify the superiority claim.

read the original abstract

Word sense plausibility rating requires predicting the human-perceived plausibility of a given word sense on a 1-5 scale in the context of short narrative stories containing ambiguous homonyms. This paper systematically compares three approaches: (1) embedding-based methods pairing sentence embeddings with standard regressors, (2) transformer fine-tuning with parameter-efficient adaptation, and (3) large language model (LLM) prompting with structured reasoning and explicit decision rules. The best-performing system employs a structured prompting strategy that decomposes evaluation into narrative components (precontext, target sentence, ending) and applies explicit decision rules for rating calibration. The analysis reveals that structured prompting with decision rules outperforms both fine-tuned models and embedding-based approaches, and that prompt design matters more than model scale for this task.

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

Structured prompting with decision rules comes out on top in this comparison, but without ablations it's tough to credit the structure itself.

read the letter

The main thing to know is that this shared-task paper shows structured prompting with explicit decision rules outperforming fine-tuned transformers and embedding-based regressors on rating word sense plausibility in short stories. They break the narrative into precontext, target sentence, and ending, then use rules to calibrate the 1-5 rating. The result is that prompt design seems to matter more than model size for this specific task. That's a practical takeaway worth noting for anyone doing similar plausibility judgments. The comparison itself is systematic and covers the expected baselines. It's helpful to see the three paradigms lined up directly on the same data. The soft spot is the missing controls. There's no ablation stripping out the decision rules, no statistical tests on the score differences, and no accounting for how much effort went into tuning each approach. The gains could easily come from extra iteration on the prompts rather than the decomposition method. For a shared-task system paper this is common, but it weakens the attribution. This is for people in computational linguistics working on SemEval tasks or word sense issues. A reader interested in applied prompting techniques will find it useful. It deserves peer review because the setup is straightforward and the task is fresh, even if the novelty is limited to the application. I'd recommend sending it to referees with a note to add ablations and significance checks.

Referee Report

3 major / 2 minor

Summary. The manuscript describes the NCL-UoR submission to SemEval-2026 Task 5 on word sense plausibility rating. It compares three families of methods—embedding-based regressors on sentence embeddings, parameter-efficient fine-tuning of transformers, and LLM prompting that decomposes each narrative into precontext/target-sentence/ending components and applies explicit decision rules for 1-5 scale calibration—reporting that the structured-prompting system achieves the highest scores.

Significance. If the reported gains are shown to be robust, the work would indicate that explicit narrative decomposition plus decision rules can outperform both embedding baselines and fine-tuned models on subjective plausibility judgments, with possible relevance to low-data or interpretability-sensitive rating tasks. The current evidence, however, rests on a single shared-task test set without ablations or significance testing, so the result remains suggestive rather than conclusive.

major comments (3)

[§4.2] §4.2 and Table 2: the central claim that structured prompting with decision rules outperforms fine-tuning lacks an ablation that removes the explicit decision rules while retaining the narrative decomposition; without this control the attribution of gains to the prompting strategy versus other factors cannot be established.
[Table 2] Table 2: no statistical significance tests, confidence intervals, or variance estimates across runs are provided for the 1-5 rating differences; given typical SemEval test-set sizes, this leaves open whether the observed margins are reliable.
[§3.3] §3.3: the fine-tuning experiments omit any description of hyperparameter search budget, number of trials, or total compute allocated to the baselines, preventing assessment of whether equivalent optimization effort on the embedding or fine-tuning systems would close the gap.

minor comments (2)

[Abstract] Abstract: the phrase 'systematically compares' would be clearer if the primary evaluation metric (e.g., Pearson correlation or mean absolute error) were named explicitly.
[§5] §5: error analysis is limited to qualitative examples; quantitative breakdown by sense type or narrative length would strengthen the discussion.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments, which help clarify the strength of our claims. We address each major point below and will revise the manuscript accordingly.

read point-by-point responses

Referee: [§4.2] §4.2 and Table 2: the central claim that structured prompting with decision rules outperforms fine-tuning lacks an ablation that removes the explicit decision rules while retaining the narrative decomposition; without this control the attribution of gains to the prompting strategy versus other factors cannot be established.

Authors: We agree that an ablation isolating the explicit decision rules (while retaining narrative decomposition into precontext/target-sentence/ending) is needed to attribute gains precisely. In the revised manuscript we will add this control experiment and report the resulting scores alongside the full system. revision: yes
Referee: [Table 2] Table 2: no statistical significance tests, confidence intervals, or variance estimates across runs are provided for the 1-5 rating differences; given typical SemEval test-set sizes, this leaves open whether the observed margins are reliable.

Authors: We acknowledge the absence of statistical analysis. Because the shared-task test set is fixed, we will add bootstrap confidence intervals (resampling test instances) and paired significance tests to the revised Table 2. revision: yes
Referee: [§3.3] §3.3: the fine-tuning experiments omit any description of hyperparameter search budget, number of trials, or total compute allocated to the baselines, preventing assessment of whether equivalent optimization effort on the embedding or fine-tuning systems would close the gap.

Authors: The fine-tuning runs used a modest fixed budget (learning rates from {1e-5, 5e-5, 1e-4}, 3 epochs, early stopping on validation) selected from prior PEFT literature rather than exhaustive search. We will expand §3.3 to document this procedure and compute allocation explicitly, while noting the limited tuning budget as a limitation that could be explored in future work. revision: partial

Circularity Check

0 steps flagged

No significant circularity in empirical evaluation

full rationale

The manuscript describes an empirical comparison of embedding-based methods, fine-tuning, and LLM prompting strategies on the SemEval-2026 Task 5 dataset. The central claim that structured prompting outperforms other approaches is supported by experimental results on a shared task test set using standard metrics. No mathematical derivations, self-definitional constructs, fitted inputs presented as predictions, or load-bearing self-citations are present. The derivation chain is self-contained against external benchmarks, consisting of direct performance measurements rather than reductions to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work relies on standard NLP assumptions about human plausibility annotations as ground truth and the validity of the 1-5 scale; no free parameters, new entities, or ad-hoc axioms are introduced beyond routine model choices.

axioms (1)

domain assumption Human annotations on the 1-5 plausibility scale constitute reliable ground truth for the task.
Invoked implicitly when treating the SemEval labels as the evaluation target.

pith-pipeline@v0.9.0 · 5449 in / 1148 out tokens · 33026 ms · 2026-05-15T14:43:52.555000+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.

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

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

The best-performing system employs a structured prompting strategy that decomposes evaluation into narrative components (precontext, target sentence, ending) and applies explicit decision rules for rating calibration.
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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

GPT-4o with structured prompting achieves ρ=0.731 and Acc.=0.794

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