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arxiv: 2412.14368 · v6 · submitted 2024-12-18 · 💻 cs.CL

Beyond Math: Stories as a Testbed for Memorization-Constrained Reasoning in LLMs

Yuxuan Jiang , Francis Ferraro This is my paper

Pith reviewed 2026-05-23 06:16 UTC · model grok-4.3

classification 💻 cs.CL
keywords LLMsmemorizationcharacter understandingdata contaminationfictional charactersgist memoryverbatim memorybenchmarks
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0 comments X

The pith

LLMs achieve high accuracy on character understanding by memorizing popular fiction rather than reasoning from essential meaning.

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

The paper argues that LLMs' strong performance on tasks involving fictional characters stems from verbatim memorization of training data instead of genuine comprehension using gist memory. It presents a method designed to reduce this memorization while retaining the implicit cues necessary for understanding. Results show performance on popular works falling from 96% to 72% accuracy, with drops of up to 18% on various tasks. This suggests that current benchmarks are contaminated and measure recall more than reasoning ability.

Core claim

The authors claim that by mitigating mechanized memorization in evaluations of character understanding, accuracy on popular fictional works drops from 96% to 72%, revealing that existing benchmarks primarily test verbatim memory rather than the intended gist-based comprehension and reasoning.

What carries the argument

A simple method to mitigate mechanized memorization in character understanding evaluations while preserving essential implicit cues.

If this is right

  • Existing benchmarks for character understanding in LLMs are contaminated by data overlap with training corpora.
  • Performance on these tasks often reflects memorization rather than true understanding.
  • New evaluation methods are needed that better isolate reasoning from recall.
  • LLMs may require different training approaches to prioritize gist memory over verbatim storage.

Where Pith is reading between the lines

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

  • Similar contamination issues likely affect other reasoning benchmarks involving popular culture or well-known texts.
  • Applying the method to non-fiction or original stories could test whether drops occur only on memorized content.
  • Future models might be trained with techniques to encourage gist extraction over rote memorization.

Load-bearing premise

The proposed method successfully isolates gist memory from verbatim memory without inadvertently removing cues required for genuine comprehension and reasoning, such that observed accuracy drops reflect reduced memorization rather than impaired task capability.

What would settle it

Testing the method on character understanding tasks using entirely new, original fictional stories never seen in training data, where no accuracy drop should occur if the claim holds.

Figures

Figures reproduced from arXiv: 2412.14368 by Francis Ferraro, Yuxuan Jiang.

Figure 1
Figure 1. Figure 1: Gist vs. verbatim memorization in speaker identification under original and anonymized scripts. This [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The bar chart compares the performance of [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: The line chart shows the decline in mem [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Performance degradation across LLMs due to GIST+NR. Action prediction tasks (e.g., Guessing, CSI) show greater drops than motivation prediction tasks (e.g., PERSONET, FriendsQA), reflecting their differen￾tial reliance on memorization. novel, graded intervention framework combining Gist Prompting and Cross-Cultural Name Replace￾ment. Our experimental results across six diverse character-centric benchmarks … view at source ↗
read the original abstract

Recently, Large Language Models (LLMs) have shown impressive performance in character understanding tasks, such as analyzing the roles, personalities, and relationships of fictional characters. However, the extensive pre-training corpora used by LLMs raise concerns that they may rely on memorizing popular fictional works rather than genuinely understanding and reasoning about them. In this work, we argue that 'gist memory'-capturing essential meaning - should be the primary mechanism for character understanding tasks, as opposed to 'verbatim memory' - exact match of a string. We introduce a simple yet effective method to mitigate mechanized memorization in character understanding evaluations while preserving the essential implicit cues needed for comprehension and reasoning. Our approach reduces memorization-driven performance on popular fictional works from 96% accuracy to 72% and results in up to an 18% drop in accuracy across various character understanding tasks. These findings underscore the issue of data contamination in existing benchmarks, which often measure memorization rather than true character understanding.

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

1 major / 0 minor

Summary. The paper claims that LLMs achieve high performance on character understanding tasks (roles, personalities, relationships) for fictional works primarily through verbatim memorization of popular texts rather than gist-based reasoning. It introduces an unspecified method to mitigate mechanized memorization while preserving implicit cues for comprehension, reporting a reduction in memorization-driven accuracy from 96% to 72% on popular fictional works and drops of up to 18% across character understanding tasks. The findings are presented as evidence of data contamination in existing benchmarks.

Significance. If the method can be shown to selectively target verbatim recall without degrading the signals needed for genuine character reasoning, the work would offer a useful diagnostic for distinguishing memorization from comprehension in narrative benchmarks. This addresses a timely concern about training-data overlap with widely read fiction and could inform more robust evaluation protocols, though the absence of any methodological detail prevents assessment of whether the reported drops support that distinction.

major comments (1)
  1. [Abstract] Abstract: The central empirical claims rest on an undescribed intervention that is said to 'mitigate mechanized memorization' while 'preserving the essential implicit cues needed for comprehension and reasoning.' No mechanism, inputs, hyperparameters, or control experiments are provided, so it is impossible to determine whether the observed drops (96%→72% and up to 18%) reflect removal of contamination or unintended degradation of task-relevant information. This is load-bearing for the paper's interpretation.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for highlighting the lack of methodological detail in the abstract. We agree this is a critical issue for interpreting the empirical claims and will revise the manuscript to include a full description of the intervention.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central empirical claims rest on an undescribed intervention that is said to 'mitigate mechanized memorization' while 'preserving the essential implicit cues needed for comprehension and reasoning.' No mechanism, inputs, hyperparameters, or control experiments are provided, so it is impossible to determine whether the observed drops (96%→72% and up to 18%) reflect removal of contamination or unintended degradation of task-relevant information. This is load-bearing for the paper's interpretation.

    Authors: We agree that the abstract provides no description of the intervention and that this prevents evaluation of whether the reported accuracy drops distinguish memorization from comprehension. The current manuscript version contains only the abstract, which states the method is 'simple yet effective' but supplies no further details. In the revised version we will expand the abstract and add a dedicated methods section describing the mechanism, inputs, hyperparameters, and control experiments used to produce the 96%→72% and up-to-18% drops. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical claims rest on reported performance changes with no derivations or self-referential definitions

full rationale

The provided abstract and text contain no equations, derivations, fitted parameters, or self-citations. The central claim concerns observed accuracy drops (96% to 72%, up to 18% on tasks) after applying an unspecified method to reduce memorization. This is presented as an empirical finding rather than a quantity derived from or equivalent to its own inputs by construction. No load-bearing step matches any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities; the claim depends on the unstated validity of the mitigation technique preserving comprehension cues.

pith-pipeline@v0.9.0 · 5671 in / 1067 out tokens · 34733 ms · 2026-05-23T06:16:15.510344+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

18 extracted references · 18 canonical work pages

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    He often acts as the voice of reason for his friends, attempting to mediate, clarify, and console in various situations

    Bojing: Bojing comes across as practical, level-headed and caring. He often acts as the voice of reason for his friends, attempting to mediate, clarify, and console in various situations. His attempts to play down his date suggest he is a private person who doesn’t enjoy sharing intimate details of his life

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    Lacks the sensitivity of others’ feelings at times but genuinely care about friends

    Joey: Energetic, extroverted, and casual. Lacks the sensitivity of others’ feelings at times but genuinely care about friends

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    However, she sometimes shows a more cynical side, quick to suspect something might be wrong with Bojing’s date and suggesting a strip joint as a solution to Meilin’s woes

    Cuixia: Cuixia seems like a lively, fun-loving character. However, she sometimes shows a more cynical side, quick to suspect something might be wrong with Bojing’s date and suggesting a strip joint as a solution to Meilin’s woes

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    Exhibits insecurity and anxiety in his dialogues, making references to uncomfortable situations and questioning his own actions

    Chandler: Witty and self-deprecating with an approachable sense of humor. Exhibits insecurity and anxiety in his dialogues, making references to uncomfortable situations and questioning his own actions

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    He’s also humorously self-aware, under- cutting his moments of honesty with reminders that he might be oversharing

    Jingjing: Jingjing can be open and candid about his thoughts, even if they seem inappropriate or unusual. He’s also humorously self-aware, under- cutting his moments of honesty with reminders that he might be oversharing

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    Yunsheng: Appears offbeat and unusual, suggesting the eating chalk anecdote about his past relationship. He also believes in new-age concepts like auras, showing a more spiritual side

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    Ross: Insecure and somewhat neurotic and vulnerable. His behavior is indicative of someone who is going through emotional turmoil

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    He seems to be fluctuating between hurt, anger, and longing for his past relationship

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    She is also reliant on her relationships with others, signifying her dependency and need for support

    Rachel: Spontaneous and open to change, she takes risks and is adaptable. She is also reliant on her relationships with others, signifying her dependency and need for support

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