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arxiv: 2504.08300 · v5 · pith:63PMY45Unew · submitted 2025-04-11 · 💻 cs.CL · cs.AI

Large Language Models Could Be Rote Learners

Yuyang Xu , Renjun Hu , Haochao Ying , Jian Wu , Xing Shi , Wei Lin This is my paper

Pith reviewed 2026-05-22 21:00 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords large language modelsrote memorizationbenchmark contaminationevaluation frameworkMMLUGSM8Kgenuine capabilityTrinEval
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The pith

Large language models depend on rote memorization for an average of 19.6 percent of knowledge points in standard benchmarks.

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

The paper reframes benchmark contamination not as an external flaw but as an inherent feature of how models learn, then sets out to separate rote memorization from genuine capability. Analysis of model behavior under varying memorization conditions reveals a counterintuitive pattern: LLMs score lower on questions they have memorized than on those they have not, pointing to the simultaneous operation of two distinct learning processes. To isolate genuine capability, the authors introduce a reformulation method that converts multiple-choice questions into a knowledge-centric trinity format designed to reduce reliance on surface recall while keeping the underlying knowledge intact. Experiments across MMLU and GSM8K then quantify that mainstream models lean on rote memorization for roughly one-fifth of the tested knowledge points. A reader would care because this separation directly affects how much trust to place in current benchmark scores as measures of understanding rather than storage.

Core claim

By examining performance differences across memorized and non-memorized conditions, the work shows that LLMs perform worse on memorized benchmarks, indicating the coexistence of rote memorization and genuine capability learning. The proposed TrinEval framework converts MCQs into an alternative knowledge-centric trinity format that reduces memorization effects while preserving inherent knowledge, thereby enabling evaluation of genuine capability even when memorization is present. Validation experiments confirm the framework's robustness, and the resulting measurements establish that mainstream LLMs rely on rote memorization for an average of 19.6 percent of knowledge points across the MMLU an

What carries the argument

TrinEval, the reformulation of multiple-choice questions into a knowledge-centric trinity format that reduces surface memorization while retaining core knowledge.

If this is right

  • Benchmark scores on contaminated data overestimate the share of genuine capability in current LLMs.
  • Evaluation protocols can be adjusted by applying the trinity reformulation to isolate memorization effects.
  • Training dynamics must accommodate both rote storage and capability acquisition rather than treating them as mutually exclusive.
  • Future benchmarks should incorporate controls for memorization to yield more accurate capability estimates.

Where Pith is reading between the lines

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

  • The measured 19.6 percent rote share could serve as a baseline for tracking how different training regimes shift the balance between memorization and generalization.
  • If the trinity format generalizes cleanly, it could be extended to open-ended question benchmarks to produce comparable memorization estimates.
  • The finding that memorized items depress performance suggests that heavy memorization may interfere with the model's ability to apply knowledge flexibly.

Load-bearing premise

The trinity reformulation preserves the original question's inherent knowledge without introducing biases or difficulty shifts that would favor genuine capability over memorization.

What would settle it

If models achieve equal or higher accuracy on the trinity-formatted versions of questions they had previously memorized, compared with non-memorized questions, the separation between rote memorization and genuine capability would no longer hold.

Figures

Figures reproduced from arXiv: 2504.08300 by Haochao Ying, Jian Wu, Renjun Hu, Wei Lin, Xing Shi, Yuyang Xu.

Figure 1
Figure 1. Figure 1: MCQ-based evaluation. We observe that LLMs tend to underperform on memorized MCQs. The rapid advancement of Large Language Models (LLMs), driven primarily by large￾scale pre-training on massive datasets, has endowed these models with remarkable proficiency across diverse tasks [Ouyang et al., 2022, OpenAI, 2024, Touvron et al., 2023]. As LLMs continue to improve, eval￾uating their genuine capacities has em… view at source ↗
Figure 2
Figure 2. Figure 2: Model performance on memorized and non￾memorized subsets of MMLU, where ‘0s’ and ‘5s’ stand for zero- and five-shot prompting, respectively. Benchmark contamination often leads to inflated performance estimate. This phe￾nomenon is commonly attributed to mod￾els memorizing specific questions and an￾swers rather than demonstrating genuine problem-solving abilities. However, the ex￾tent to which and how memor… view at source ↗
Figure 3
Figure 3. Figure 3: Knowledge-preserving validation of TrinEval [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The results of memorization evocation (evoc.) under various dataset-related context, with [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The distribution of MCQs based on memorization metric [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Averaged distance of each MCQs between the closest [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The over-performing probability curve and p-value curve with different [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
read the original abstract

Benchmark-based evaluation, e.g., multiple-choice questions (MCQs) and open-ended questions (OEQs), is widely used for evaluating Large Language Models (LLMs), yet their reliability is undermined by benchmark contamination. When pre-exposed to the testing benchmark during training, less capable LLMs have been found to achieve inflated performance, thereby yielding erroneous results in LLM evaluation. In this study, we reframe contamination as an inherent aspect of learning and seek to disentangle and expose genuine capability acquisition from superficial memorization in LLM evaluation. Following this, firstly, by analyzing model performance under different memorization conditions of MCQs, we uncover a counterintuitive trend: LLMs perform worse on memorized benchmarks than on non-memorized ones, indicating the coexistence of two learning phenomena, i.e., rote memorization and genuine capability learning. To disentangle them, we propose TrinEval, a novel evaluation framework that reformulates MCQs into an alternative knowledge-centric trinity format, reducing memorization while preserving inherent knowledge, enabling the evaluation of genuine capability in the presence of memorization. Extensive experiments validate the effectiveness and robustness of TrinEval in reformulating benchmarks, and the evaluation results further reveal that mainstream LLMs rely on rote memorization for an average of 19.6% of knowledge points across the MMLU and the GSM8K dataset.

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 / 1 minor

Summary. The paper reframes benchmark contamination as rote memorization coexisting with genuine capability acquisition in LLMs. It reports a counterintuitive finding that models perform worse on memorized MCQs than non-memorized ones, proposes the TrinEval framework to reformulate MCQs into a trinity format that reduces memorization access while preserving inherent knowledge, and concludes that mainstream LLMs rely on rote memorization for an average of 19.6% of knowledge points across MMLU and GSM8K.

Significance. If TrinEval is shown to isolate memorization without shifting underlying difficulty or introducing format biases, the 19.6% figure would quantify the contribution of superficial memorization to standard benchmark scores and support the development of more robust evaluation protocols. The empirical observation of degraded performance on memorized items is a concrete, falsifiable result that challenges purely contamination-based accounts of benchmark inflation.

major comments (2)
  1. [Abstract] Abstract: the central attribution of the performance gap to rote memorization (yielding the 19.6% figure) rests on the assertion that the trinity reformulation 'preserves inherent knowledge' and 'reduc[es] memorization while preserving inherent knowledge'; however, the provided text contains no description of controls (human performance baselines, item-difficulty matching, or ablation of format-induced variance) that would be required to establish that the measured gap isolates memorization rather than a change in task distribution.
  2. [Abstract] Abstract: the claim that 'experiments validate the effectiveness and robustness of TrinEval' is invoked to support the 19.6% estimate, yet the abstract supplies no quantitative details on how robustness was assessed (e.g., cross-model consistency of the rote share, sensitivity to reformulation parameters, or comparison against alternative decontamination methods), leaving the load-bearing percentage dependent on unshown validation steps.
minor comments (1)
  1. [Abstract] Abstract: the phrase 'the MMLU and the GSM8K dataset' is grammatically inconsistent and should read 'the MMLU and GSM8K datasets'.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments on the abstract. We address each point below and will revise the abstract to incorporate additional details on controls and validation metrics.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central attribution of the performance gap to rote memorization (yielding the 19.6% figure) rests on the assertion that the trinity reformulation 'preserves inherent knowledge' and 'reduc[es] memorization while preserving inherent knowledge'; however, the provided text contains no description of controls (human performance baselines, item-difficulty matching, or ablation of format-induced variance) that would be required to establish that the measured gap isolates memorization rather than a change in task distribution.

    Authors: The abstract is a concise summary and does not enumerate controls, but the manuscript body details the TrinEval design choices that preserve core knowledge requirements (e.g., equivalent factual content across formats) and reports ablations showing that performance differences align with memorization access rather than format or difficulty shifts. We did not collect new human baselines, but item-level difficulty matching and cross-format consistency checks are included in the experiments. We will revise the abstract to briefly reference these controls. revision: yes

  2. Referee: [Abstract] Abstract: the claim that 'experiments validate the effectiveness and robustness of TrinEval' is invoked to support the 19.6% estimate, yet the abstract supplies no quantitative details on how robustness was assessed (e.g., cross-model consistency of the rote share, sensitivity to reformulation parameters, or comparison against alternative decontamination methods), leaving the load-bearing percentage dependent on unshown validation steps.

    Authors: Space constraints in the abstract limit quantitative reporting, but the manuscript provides these metrics, including cross-model consistency of the rote share and sensitivity analyses. We will revise the abstract to include a short clause summarizing the key robustness findings that support the 19.6% estimate. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical result from new framework

full rationale

The paper introduces TrinEval as an independent reformulation method, analyzes performance differences across memorization conditions, and reports the 19.6% figure as an output of applying that method to MMLU/GSM8K. No quoted equations, definitions, or self-citations reduce the central percentage or the coexistence claim to inputs by construction. The framework's validity assumptions are external to the measurement step itself and do not create a definitional loop or fitted-input prediction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The central claim rests on the assumption that performance differences between memorized and non-memorized conditions directly reflect the split between rote memorization and genuine capability, plus the premise that the trinity reformulation isolates knowledge without side effects. No explicit free parameters are introduced in the abstract; the 19.6% is presented as an empirical average rather than a fitted constant.

axioms (2)
  • domain assumption Benchmark contamination can be detected and labeled as memorization versus non-memorization conditions in a way that cleanly separates rote recall from capability acquisition.
    Invoked when analyzing model performance under different memorization conditions and when claiming the coexistence of rote memorization and genuine capability learning.
  • domain assumption Reformulating MCQs into a trinity format reduces memorization effects while fully preserving the original inherent knowledge.
    This is the core premise of TrinEval that enables the evaluation of genuine capability in the presence of memorization.
invented entities (1)
  • TrinEval framework no independent evidence
    purpose: Reformulate MCQs into knowledge-centric trinity format to disentangle memorization from capability
    New evaluation method introduced in the paper; no independent evidence outside the authors' experiments is provided in the abstract.

pith-pipeline@v0.9.0 · 5775 in / 1778 out tokens · 91730 ms · 2026-05-22T21:00:43.004376+00:00 · methodology

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Forward citations

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Reference graph

Works this paper leans on

9 extracted references · 9 canonical work pages · cited by 1 Pith paper

  1. [5]

    **Determine Outcome:** Based on the reasoning, conclude and extract the Knowledge Keyword, the Attribute and the Context (if necessary) of the Question according to the Question corpus. # Output Format Provide the outcome in the following format: - **Step-by-Step Reasoning:** [Detailed reasoning here] - **Knowledge Keyword:** [Extracted Knowledge Keyword ...

    work page

  2. [6]

    Read through the given Knowledge Keyword, Attribute, Context and the given Question

    **Check the Semantic completeness:** Suppose you have sufficient background knowledge about [subject], and you can solve the given Question and obtain the given Answer. Read through the given Knowledge Keyword, Attribute, Context and the given Question. Check if the given Knowledge Keyword, Attribute, Context are the original text within the Question and ...

    work page

  3. [7]

    Read through the given Knowledge Keyword, Attribute, Context and the given Question

    **Check the Answer relevance:** Suppose you have sufficient background knowledge about subj, and you can solve the given Question and obtain the given Answer. Read through the given Knowledge Keyword, Attribute, Context and the given Question. Read through the given Knowledge Keyword, Attribute, Context and the given Answer. Check if the Answer can be dir...

    work page

  4. [8]

    Check if the Answer can be directly matched within the given Knowledge Keyword, Attribute and the Context

    **Check the Semantic Redundancy:** Read through the given Knowledge Keyword, Attribute, Context, the given Question and the given corresponding Answer. Check if the Answer can be directly matched within the given Knowledge Keyword, Attribute and the Context. Check if there are any unnecessary information within the given Knowledge Keyword, Attribute and t...

    work page

  5. [9]

    **Review the Fact corpus:** Read through the entire Fact corpus to understand the context

    work page

  6. [10]

    **Identify the Question:** Focus on the given Question to capture which part of the Fact corpus it is asking about

    work page

  7. [11]

    **Understand the Answer to the Question:** Compare the given Answer and the identified questioned part within the Fact corpus and understand why this answer was chosen

    work page

  8. [12]

    - Determine the asked Attribute of the Knowledge Keyword in the Question, which can be used to infer the given Answer

    **Write Step-by-Step Reasoning:** - Identify the asked Knowledge Keyword in the Question that is the subject of the most information in the Fact corpus and the asked Question is about the information among. - Determine the asked Attribute of the Knowledge Keyword in the Question, which can be used to infer the given Answer. - Review the identified Knowled...

    work page

  9. [13]

    **Determine Outcome:** Based on the reasoning, conclude and extract the Knowledge Keyword, the Attribute and the Context (if necessary) of the Question according to the Question corpus. # Output Format Provide the outcome in the following format: - **Step-by-Step Reasoning:** [Detailed reasoning here] - **Knowledge Keyword:** [Extracted Knowledge Keyword ...

    work page