Paper Citation Record · LEDGER

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation

As of 23 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 5 inbound Pith citation observations for arXiv:2412.04318.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.04318 v2

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T21:37:19.907107Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T21:03:04.021627Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-30T19:05:00.327994Z

Reference resolution

36 of 36 outbound references displayed

verified exact0
verified fuzzy10
unresolved26
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7edaab7e-f345-40d9-b401-0fb1e5f3f1c7 · outbound

This paper cites The pitfalls of next-token prediction.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation The pitfalls of next-token prediction

Reference 1

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Observation feb83713-020e-41cc-a4c4-6e01d94ed2a3 · outbound

This paper cites Reconciling modern machine-learning practice and the classical bias–variance trade-off.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation Reconciling modern machine-learning practice and the classical bias–variance trade-off

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T21:37:19.780875Z

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Observation c87fc84a-c4ec-4424-9137-3dd8f87f9107 · outbound

This paper cites DeepSeek LLM: Scaling Open-Source Language Models with Longtermism.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

Reference 3

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Observation 860e0920-f258-48a3-b74c-e237a8f9bb23 · outbound

This paper cites Language Models are Few-Shot Learners.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation Language Models are Few-Shot Learners

Reference 4

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Observation 6781914f-6edc-43c0-8aef-e382ecd9c6d1 · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T21:37:19.795377Z

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Observation 1aa015df-3b6c-40ab-84a0-863f22b61312 · outbound

This paper cites Branch- GAN : Improving text generation with (not so) large language models.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation Branch- GAN : Improving text generation with (not so) large language models

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:37:20.225676Z

Source-reported events for the cited work

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Observation 0d97de0d-2c21-4d03-9e58-bc0912345134 · outbound

This paper cites Generative pretraining from pixels.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation Generative pretraining from pixels

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:37:20.215681Z

Source-reported events for the cited work

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Observation caf1e9ad-415e-4084-85e4-5a9788962f03 · outbound

This paper cites The Llama 3 Herd of Models.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation The Llama 3 Herd of Models

Reference 8

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Observation 1353a1e0-6733-4e60-871c-a911bbbbbbd0 · outbound

This paper cites Text english code fiction nonfiction dataset, 2024.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation Text english code fiction nonfiction dataset, 2024

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:37:20.204929Z

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Observation 18224769-5e18-4e41-847a-391c036047dc · outbound

This paper cites A theoretical analysis of the repetition problem in text generation.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation A theoretical analysis of the repetition problem in text generation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:37:20.195452Z

Source-reported events for the cited work

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Observation b02208bb-a1a4-4a93-881c-5014f7bda999 · outbound

This paper cites DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence

Reference 11

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Observation 763c2465-9a78-4bd2-8277-e3d5082d71d5 · outbound

This paper cites Identity mappings in deep residual networks.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation Identity mappings in deep residual networks

Reference 12

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Source-reported events for the cited work

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Observation 331e2470-14f8-4581-b207-c7a4c61cf81a · outbound

This paper cites Measuring massive multitask language understanding.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation Measuring massive multitask language understanding

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T21:37:19.824936Z

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Observation 1e12c8e2-0ac0-4369-a592-674bea271d6d · outbound

This paper cites The curious case of neural text degeneration.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation The curious case of neural text degeneration

Reference 14

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Observation c39d0dba-9300-4333-b5a1-1086d716b4cd · outbound

This paper cites CTRL: A Conditional Transformer Language Model for Controllable Generation.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation CTRL: A Conditional Transformer Language Model for Controllable Generation

Reference 15

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Observation 3779295d-5e45-45ca-914b-b9572599ee1b · outbound

This paper cites Latesteval: Addressing data contamination in language model evaluation through dynamic and time-sensitive test construction.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation Latesteval: Addressing data contamination in language model evaluation through dynamic and time-sensitive test construction

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:37:20.163560Z

Source-reported events for the cited work

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Observation 13da8c61-1f2b-4ba5-afb2-41106915d9b6 · outbound

This paper cites Omnigrok: Grokking Beyond Algorithmic Data.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation Omnigrok: Grokking Beyond Algorithmic Data

Reference 17

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Observation 165c6517-a448-46ce-a5c1-f93b929565d8 · outbound

This paper cites StarCoder 2 and The Stack v2: The Next Generation.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation StarCoder 2 and The Stack v2: The Next Generation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T21:37:19.843483Z

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Observation e5f97074-d2d2-4afd-a8e8-589c23d002a7 · outbound

This paper cites Language model evaluation beyond perplexity.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation Language model evaluation beyond perplexity

Reference 19

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Observation 2a4f120f-ce2c-4a57-8c11-f1018483f16e · outbound

This paper cites Pointer sentinel mixture models.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation Pointer sentinel mixture models

Reference 20

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Observation 581ed746-e73f-4a17-a68d-ceb280e05281 · outbound

This paper cites Deep double descent: Where bigger models and more data hurt.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation Deep double descent: Where bigger models and more data hurt

Reference 21

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Observation b06f706f-3775-445a-9ec9-346889cd88cb · outbound

This paper cites Training language models to follow instructions with human feedback.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation Training language models to follow instructions with human feedback

Reference 22

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Observation 159bdb1a-6223-4bdc-9ef9-88e985f7d85b · outbound

This paper cites Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

Reference 23

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This paper cites Brown, Adam Santoro, Aditya Gupta, Adri \`a Garriga-Alonso, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation Brown, Adam Santoro, Aditya Gupta, Adri \`a Garriga-Alonso, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W

Reference 24

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Observation 88bdb013-3c09-41b7-8963-71d4a1f885e6 · outbound

This paper cites Glue: A multi-task benchmark and analysis platform for natural language understanding.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation Glue: A multi-task benchmark and analysis platform for natural language understanding

Reference 25

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Observation ea8d3438-0cad-470c-8064-5294dfd38bcf · outbound

This paper cites Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:37:20.116077Z

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Observation b79f896f-137e-4942-9f1a-81cb85d21203 · outbound

This paper cites Emergent abilities of large language models.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation Emergent abilities of large language models

Reference 27

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Observation 8e7908ff-3a34-4f41-b1b7-9c24926ba89e · outbound

This paper cites Neural Text Generation with Unlikelihood Training.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation Neural Text Generation with Unlikelihood Training

Reference 28

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Observation 7e1136d3-d4a9-40e8-9119-a9264cafa1f9 · outbound

This paper cites Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation

Reference 29

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The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation Understanding deep learning requires rethinking generalization

Reference 30

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This paper cites Understanding deep learning (still) requires rethinking generalization.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation Understanding deep learning (still) requires rethinking generalization

Reference 31

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The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation TinyLlama: An Open-Source Small Language Model

Reference 32

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The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation write newline

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The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation @esa (Ref

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The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation Unresolved cited work

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The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation Unresolved cited work

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Pith citing papers

Observation b3a9a617-327c-41fa-9f44-b0de61023470 · inbound

Rethinking Early Stopping: Refine, Then Calibrate cites this paper.

Rethinking Early Stopping: Refine, Then Calibrate The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation

Reference 2022

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Towards Revealing the Effectiveness of Small-Scale Fine-tuning in R1-style Reinforcement Learning cites this paper.

Towards Revealing the Effectiveness of Small-Scale Fine-tuning in R1-style Reinforcement Learning The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation

Reference 4

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Observation 14262ad3-f793-40f6-82a3-2fdc91bf01c6 · inbound

Quality-constrained Entropy Maximization Policy Optimization for LLM Diversity cites this paper.

Quality-constrained Entropy Maximization Policy Optimization for LLM Diversity The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation

Reference 1

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Observation 82bd4675-3582-4a2b-811f-94ea0201274d · inbound

Towards Human-Level Book-Writing Capability cites this paper.

Towards Human-Level Book-Writing Capability The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-20T15:43:27.022818Z

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Observation 7e68a4d7-7776-4d71-ae0c-4fcb0d80565a · inbound

Towards Human-Level Book-Writing Capability cites this paper.

Towards Human-Level Book-Writing Capability The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-06-30T19:05:00.330321Z

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