Paper Citation Record · LEDGER

On the Effectiveness of Incremental Training of Large Language Models

As of 13 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2411.18700.

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

pith.paper-citation-record.v1
2411.18700 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:07:17.187777Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

29 of 29 outbound references displayed

verified exact0
verified fuzzy8
unresolved21
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8e94b6cc-9e36-4656-b103-5f0a93eb967b · outbound

This paper cites Language Models are Few-Shot Learners.

On the Effectiveness of Incremental Training of Large Language Models Language Models are Few-Shot Learners

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T11:07:17.072102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c78e031-6c87-441f-bcb1-f6f298f0ad23 · outbound

This paper cites BERT Rediscovers the Classical NLP Pipeline.

On the Effectiveness of Incremental Training of Large Language Models BERT Rediscovers the Classical NLP Pipeline

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T11:07:17.077285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b64c5ac2-0d07-4d4f-8f8b-1ebb53944b65 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

On the Effectiveness of Incremental Training of Large Language Models RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T11:07:17.081467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14598270-c0ff-4c62-ac3d-440bb049dfb9 · outbound

This paper cites Scaling Laws for Neural Language Models.

On the Effectiveness of Incremental Training of Large Language Models Scaling Laws for Neural Language Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T11:07:17.085994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12439348-02dc-434e-ac1b-0217059f06b4 · outbound

This paper cites Training Compute-Optimal Large Language Models.

On the Effectiveness of Incremental Training of Large Language Models Training Compute-Optimal Large Language Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T11:07:17.090479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 980ea5dc-d5f2-4381-a8f7-06924a65c315 · outbound

This paper cites The Llama 3 Herd of Models.

On the Effectiveness of Incremental Training of Large Language Models The Llama 3 Herd of Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T11:07:17.094732Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25ee6e12-78f2-4a44-9998-5e19b622a658 · outbound

This paper cites A fast learning algorithm for deep belief nets,.

On the Effectiveness of Incremental Training of Large Language Models A fast learning algorithm for deep belief nets,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T11:07:17.099131Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8ab18b5-2a3a-4326-a982-14b1dbc99b3d · outbound

This paper cites Greedy layer- wise training of deep networks,.

On the Effectiveness of Incremental Training of Large Language Models Greedy layer- wise training of deep networks,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:07:17.523820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c8871e9c-a191-41c6-bfd5-3736c3830568 · outbound

This paper cites The cascade-correlation learning architec- ture,.

On the Effectiveness of Incremental Training of Large Language Models The cascade-correlation learning architec- ture,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:07:17.510236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ee8b7273-9829-42aa-aa6e-efd1c33d98ab · outbound

This paper cites Improving language models by retrieving from trillions of tokens.

On the Effectiveness of Incremental Training of Large Language Models Improving language models by retrieving from trillions of tokens

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T11:07:17.110794Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac1514ad-4586-4dcf-b5a2-75d08f926f6e · outbound

This paper cites Analyzing hidden representations in end- to-end automatic speech recognition systems,.

On the Effectiveness of Incremental Training of Large Language Models Analyzing hidden representations in end- to-end automatic speech recognition systems,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:07:17.496896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation def03a05-a250-46df-94d8-c585e739b61d · outbound

This paper cites The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives.

On the Effectiveness of Incremental Training of Large Language Models The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T11:07:17.118607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35a86151-da4d-4fd4-a6ed-57636614eb5d · outbound

This paper cites Transformer Feed-Forward Layers Are Key-Value Memories.

On the Effectiveness of Incremental Training of Large Language Models Transformer Feed-Forward Layers Are Key-Value Memories

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T11:07:17.122588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc3fc014-5cb3-4fef-9662-7f08d269cbe8 · outbound

This paper cites Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability,.

On the Effectiveness of Incremental Training of Large Language Models Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:07:17.485310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 254cd807-f22d-46a5-b661-d8cd9626cad5 · outbound

This paper cites Visualizing and understanding convolutional networks,.

On the Effectiveness of Incremental Training of Large Language Models Visualizing and understanding convolutional networks,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:07:17.472343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation fe957c28-c9de-4056-bd4b-9c6c96da2ad2 · outbound

This paper cites Qwen2 Technical Report.

On the Effectiveness of Incremental Training of Large Language Models Qwen2 Technical Report

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T11:07:17.133462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08b23097-01bd-4b1a-8f60-41a3e2c3e273 · outbound

This paper cites Why does unsuper- vised pre-training help deep learning?.

On the Effectiveness of Incremental Training of Large Language Models Why does unsuper- vised pre-training help deep learning?

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:07:17.459738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 53a07be4-1a81-4739-ad62-02493786a540 · outbound

This paper cites The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

On the Effectiveness of Incremental Training of Large Language Models The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T11:07:17.141100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b543ff2-8fd8-4520-affc-87eec956b3e2 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

On the Effectiveness of Incremental Training of Large Language Models Distilling the Knowledge in a Neural Network

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T11:07:17.145022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3b32f80-6923-4e83-a35f-758e6b4c2edc · outbound

This paper cites Mixed Precision Training.

On the Effectiveness of Incremental Training of Large Language Models Mixed Precision Training

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T11:07:17.148936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04acfd1d-844b-4814-b545-67c71f54d203 · outbound

This paper cites Universal Language Model Fine-tuning for Text Classification.

On the Effectiveness of Incremental Training of Large Language Models Universal Language Model Fine-tuning for Text Classification

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T11:07:17.152917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b95f1d8f-9be0-4227-913a-e7951f3a350c · outbound

This paper cites Progressive Neural Networks.

On the Effectiveness of Incremental Training of Large Language Models Progressive Neural Networks

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T11:07:17.157078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2c69e01-9f59-46fd-94e0-52803e39fe34 · outbound

This paper cites An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks.

On the Effectiveness of Incremental Training of Large Language Models An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T11:07:17.161309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd30d1d6-9297-42ec-b819-f5ceff22f5c8 · outbound

This paper cites How transferable are features in deep neural networks?.

On the Effectiveness of Incremental Training of Large Language Models How transferable are features in deep neural networks?

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T11:07:17.166435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5ea9ea4-19ef-46ed-9985-cfc563d25274 · outbound

This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

On the Effectiveness of Incremental Training of Large Language Models Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T11:07:17.170606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a86c7fe5-b006-41f8-a950-cbd05c080d48 · outbound

This paper cites Fineweb- edu,.

On the Effectiveness of Incremental Training of Large Language Models Fineweb- edu,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:07:17.439285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 46459acf-7dc4-488a-8c36-842f54d2ee83 · outbound

This paper cites Decoupled Weight Decay Regularization.

On the Effectiveness of Incremental Training of Large Language Models Decoupled Weight Decay Regularization

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T11:07:17.178636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87a60cc8-8eb3-4df5-8417-d28617c884ca · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

On the Effectiveness of Incremental Training of Large Language Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T11:07:17.183757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc76df30-b020-4876-a902-00052a076e06 · outbound

This paper cites Deep feedforward net- works,.

On the Effectiveness of Incremental Training of Large Language Models Deep feedforward net- works,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:07:17.426541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

No inbound Pith citation observations are available.