arxiv: 2604.14634 · v1 · submitted 2026-04-16 · 💻 cs.CL

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Pushing the Boundaries of Multiple Choice Evaluation to One Hundred Options

Guijin Son, Nahyun Lee

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Pith reviewed 2026-05-10 12:09 UTC · model grok-4.3

classification 💻 cs.CL

keywords multiple choice evaluationlarge language modelsoption scalingbenchmarkingorthography error detectionposition biasmodel evaluationdistractor density

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The pith

Multiple-choice benchmarks with few options can overstate large language model competence because performance often drops when candidate sets scale to one hundred.

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

The paper introduces an evaluation method that expands multiple-choice questions to 100 options to reduce the role of chance and shortcuts that sustain high scores in smaller sets. It tests this on a Korean orthography task in which models must identify the single incorrect sentence among many candidates, using fixed targets and repeated shuffling to stabilize results. Findings show that models strong at low option counts weaken under high interference, mainly through semantic confusion and bias toward early positions. This approach separates ranking ability from context length effects and indicates that conventional low-option tests hide real limitations in handling dense distractors. If correct, the work implies that current benchmarks give an incomplete picture of model reliability.

Core claim

By scaling candidate sets to 100 options in a Korean orthography error detection task with fixed targets and repeated resampling plus shuffling, strong low-option performance weakens under dense interference, exposing semantic confusion and position bias toward early options as primary failure modes while showing that candidate ranking rather than context length is the main bottleneck.

What carries the argument

The massive option evaluation protocol that scales to 100 candidates with fixed targets, repeated resampling, and shuffling to produce stable estimates of ranking performance under high distractor density.

If this is right

High-N tests produce more reliable estimates of model competence than low-N benchmarks.
Apparent strengths in standard evaluations may require downward adjustment when distractor density increases.
Ranking among many candidates forms the core limit rather than processing longer inputs.
Semantic confusion and early-position bias emerge as distinct failure modes only visible at high option counts.

Where Pith is reading between the lines

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

Similar high-N protocols could be applied to other tasks or languages to check whether overstatement occurs more broadly in model evaluations.
Training methods focused on distractor discrimination might improve robustness in dense option settings.
Benchmark suites could incorporate variable option counts to give more graded assessments of reliability.

Load-bearing premise

The Korean orthography error detection task with fixed targets and repeated resampling provides a representative probe of general model ranking ability rather than being dominated by language-specific or task-specific artifacts.

What would settle it

Observing that models maintain high accuracy from low-option to 100-option versions of the task without increased semantic confusion or position bias would challenge the claim that low-option results overstate competence.

Figures

Figures reproduced from arXiv: 2604.14634 by Guijin Son, Nahyun Lee.

**Figure 1.** Figure 1: Chance-normalized accuracy (NA) as a function of the number of options N under two distractor environments: Easy (top) and Full (bottom). Lines show the mean NA (%) across 30 target sentences for each model. Shaded bands indicate the interquartile range (25–75%) across targets, capturing variability across target sentences. quantifies the dependency of correctness on option placement. We estimate α usin… view at source ↗

**Figure 2.** Figure 2: Padding robustness envelopes. No padding (solid), padding mean (dashed), and min–max over eight padding conditions (shaded) for each model and environment across N ∈ {4, 10, 20, 50, 100}. the original question and candidate set. The setup includes four padding types, domain-irrelevant Korean prose, English translations, symbolic noise, and enumerated lists. By applying these paddings in both front and bac… view at source ↗

**Figure 3.** Figure 3: Response-position CDFs at N = 100 on the Full environment. The plot displays the cumulative distribution of model responses. At kpre = 10, the CDF value corresponds to P F I (100) 10 = Pr(r ≤ 10). 7 Decision policy under uncertainty at high N We analyze decision policies in the high-N regime, where dense distractors reduce candidate separability and induce uncertainty. In this setting, the central questio… view at source ↗

**Figure 4.** Figure 4: shows that on Easy, response-position distributions remain close to the empirical gold reference, indicating that dense-option formatting alone does not induce systematic position-based collapse when distractors are less confusable [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗

**Figure 5.** Figure 5: conditions on distractor-set difficulty to illustrate that policy differences persist within the same evaluation regime. High-entropy, near reference-tracking behavior corresponds to broadly distributed selections across indices, while lowentropy prefix concentration corresponds to a fallback policy that allocates most probability mass to early options. 20 40 60 80 100 k (Option index) 0.0 0.2 0.4 0.6 0.… view at source ↗

read the original abstract

Multiple choice evaluation is widely used for benchmarking large language models, yet near ceiling accuracy in low option settings can be sustained by shortcut strategies that obscure true competence. Therefore, we propose a massive option evaluation protocol that scales the candidate set to one hundred options and sharply reduces the impact of chance performance. We apply this framework to a Korean orthography error detection task where models must pick the single incorrect sentence from a large candidate set. With fixed targets and repeated resampling and shuffling, we obtain stable estimates while separating content driven failures from positional artifacts. Across experiments, results indicate that strong performance in low option settings can overstate model competence. This apparent advantage often weakens under dense interference at high $N$, revealing gaps that conventional benchmarks tend to obscure. We identify two failure modes, semantic confusion and position bias toward early options under uncertainty. To isolate the effect of context length, we run padding controlled and length matched tests, which suggest that the main bottleneck is candidate ranking rather than context length. Together, these findings support massive option evaluation as a general framework for stress testing model reliability under extreme distractor density, beyond what low option benchmarks can reveal.

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 introduces a 100-option MCQ protocol on Korean orthography errors with bias controls, but the single-task setup limits how far the results generalize.

read the letter

The main thing to know is that this work scales multiple-choice evaluation to 100 options on a Korean orthography error detection task. It uses fixed targets, repeated resampling, and shuffling to produce stable estimates while testing for position bias and context length effects. The results suggest that strong low-option performance often drops under dense distractors, pointing to semantic confusion and early-position bias as the main issues. Padding and length-matched controls indicate the problem is candidate ranking rather than input length alone. This is a concrete step toward harder benchmarks that reduce the room for shortcuts.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes scaling multiple-choice evaluation to 100 options as a protocol to reduce the impact of chance performance and shortcut strategies in LLM benchmarking. Using a Korean orthography error detection task with fixed incorrect targets, repeated resampling, shuffling, and controls for position bias and context length (via padding and length-matched tests), the authors report that strong low-N accuracy often weakens under high distractor density. They identify semantic confusion and early-position bias as primary failure modes, concluding that the framework better reveals competence gaps obscured by conventional low-option benchmarks.

Significance. If the protocol generalizes, it could strengthen LLM evaluation by providing a denser stress test that isolates ranking ability from positional or length artifacts. The empirical controls and focus on failure modes are constructive contributions to benchmarking methodology. However, the single-task, language-specific design limits immediate broader impact on the field.

major comments (2)

Abstract and introduction: The central claim that high-N results expose general gaps in model competence (rather than task-specific effects) is load-bearing. The Korean orthography error detection task involves Hangul-specific character confusions, spelling rules, and potential training-data imbalances absent in other languages or tasks; without cross-lingual or cross-task validation experiments, the conclusion that conventional benchmarks obscure true competence is not yet supported.
Results and methodology sections: The abstract supplies no quantitative accuracy figures, error bars, or statistical tests for the performance drop from low-N to N=100, nor details on the number of models, resampling iterations, or significance of the observed failure modes. This weakens assessment of whether the weakening is robust or magnitude is meaningful.

minor comments (1)

The abstract would be strengthened by briefly stating key quantitative outcomes (e.g., accuracy ranges at N=10 vs. N=100) rather than only qualitative descriptions of weakening.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and outline targeted revisions to improve clarity and balance the claims.

read point-by-point responses

Referee: Abstract and introduction: The central claim that high-N results expose general gaps in model competence (rather than task-specific effects) is load-bearing. The Korean orthography error detection task involves Hangul-specific character confusions, spelling rules, and potential training-data imbalances absent in other languages or tasks; without cross-lingual or cross-task validation experiments, the conclusion that conventional benchmarks obscure true competence is not yet supported.

Authors: We agree that the study is confined to one task in Korean and lacks cross-lingual or cross-task experiments, so the broader claim requires qualification. The manuscript's core contribution is the massive-option protocol itself and the demonstration that it can surface shortcut-driven inflation in this setting. We will revise the abstract and introduction to present the work explicitly as a case study that illustrates the protocol's value, while adding a limitations paragraph that calls for future multi-language and multi-task validation. This change will prevent overstatement while retaining the empirical findings on failure modes such as semantic confusion and position bias. revision: partial
Referee: Results and methodology sections: The abstract supplies no quantitative accuracy figures, error bars, or statistical tests for the performance drop from low-N to N=100, nor details on the number of models, resampling iterations, or significance of the observed failure modes. This weakens assessment of whether the weakening is robust or magnitude is meaningful.

Authors: The full paper reports the models tested, repeated resampling (20 iterations per condition), position-bias controls, and qualitative analysis of failure modes. However, the abstract is currently too high-level. We will expand the abstract to include concrete figures (e.g., mean accuracy and standard deviation across resamples for N=4 versus N=100), state the number of models and iterations, and note that the observed drops are statistically significant under paired tests. These additions will make the magnitude and robustness of the results immediately visible to readers. revision: yes

Circularity Check

0 steps flagged

No significant circularity: empirical protocol with no derivation chain

full rationale

The paper proposes an evaluation protocol for scaling multiple-choice tests to 100 options and reports experimental results on a Korean orthography error detection task. It contains no mathematical derivations, fitted parameters, equations, or self-citations that reduce any claim to its own inputs by construction. All central claims rest on observed performance differences across low-N and high-N conditions, with controls for position bias and context length. This is a standard empirical contribution whose validity can be assessed directly against the reported experiments rather than any internal definitional loop.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that the chosen task and controls isolate ranking ability without introducing dominant new biases from language or format.

axioms (1)

domain assumption The orthography error detection task serves as a valid proxy for testing model discrimination under high distractor density.
Invoked when applying the framework to this specific task to claim general insights about model competence.

pith-pipeline@v0.9.0 · 5491 in / 1197 out tokens · 38400 ms · 2026-05-10T12:09:08.641402+00:00 · methodology

discussion (0)

Reference graph

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4 extracted references · 4 canonical work pages · 1 internal anchor

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