Paper Citation Record · LEDGER

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models

As of 16 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2502.00046.

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

pith.paper-citation-record.v1
2502.00046 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:10:20.416886Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

27 of 27 outbound references displayed

verified exact0
verified fuzzy13
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e54d76d6-fe98-409a-9b5b-e31e68daa704 · outbound

This paper cites Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models.

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T20:10:20.309203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c8d9856-4fd9-4392-a263-957bea6556cc · outbound

This paper cites Think you have solved question answering? try arc, the ai2 reasoning challenge, 2018.

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models Think you have solved question answering? try arc, the ai2 reasoning challenge, 2018

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T20:10:20.314235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac8ce482-26be-41c7-a921-3a6c5ca12f31 · outbound

This paper cites LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale.

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T20:10:20.318354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68d31d00-a39f-45f7-9514-84cd3ca516b4 · outbound

This paper cites 8-bit Optimizers via Block-wise Quantization.

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models 8-bit Optimizers via Block-wise Quantization

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T20:10:20.323263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c30ab8fd-a474-44f9-a50f-89abff5dcbef · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models QLoRA: Efficient Finetuning of Quantized LLMs

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T20:10:20.327819Z

Source-reported events for the cited work

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Observation a8809b62-cb6d-49e8-9692-2033fa0605ff · outbound

This paper cites SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression.

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T20:10:20.332494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d37521cb-17f6-405f-abd9-4344399dedfb · outbound

This paper cites The case for 4-bit precision: k-bit inference scaling laws.

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models The case for 4-bit precision: k-bit inference scaling laws

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T20:10:20.337309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 653b8c4f-d744-445b-9eeb-4a1a38f2adcf · outbound

This paper cites Sparsegpt: Massive language models can be accurately pruned in one-shot, 2023.

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models Sparsegpt: Massive language models can be accurately pruned in one-shot, 2023

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T20:10:20.341080Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e66c84f6-9852-48d8-bc7e-2f4eaf6a1e12 · outbound

This paper cites A framework for few-shot language model evaluation, 07 2024.

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models A framework for few-shot language model evaluation, 07 2024

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T20:10:20.345301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1b05359-f367-424f-9ce6-4e2dfd569099 · outbound

This paper cites Minillm: Knowledge distillation of large language models, 2024.

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models Minillm: Knowledge distillation of large language models, 2024

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T20:10:20.349880Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8080725c-f4ca-44d4-9d04-aba5fff62697 · outbound

This paper cites A simple and effective method for removal of hidden units and weights.

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models A simple and effective method for removal of hidden units and weights

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:20.692587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dfb09f6a-f8e0-40f9-a795-069c674d8187 · outbound

This paper cites Measuring massive multitask language understand- ing, 2021.

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models Measuring massive multitask language understand- ing, 2021

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:20.680374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 80a95704-fc63-431c-9daa-2a7700b5b235 · outbound

This paper cites Model compression in practice: Lessons learned from practitioners creating on-device machine learning experiences.

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models Model compression in practice: Lessons learned from practitioners creating on-device machine learning experiences

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:20.668675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 42ab462e-ffc6-43a3-97eb-4600a6947d91 · outbound

This paper cites A study of bfloat16 for deep learning training, 2019.

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models A study of bfloat16 for deep learning training, 2019

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:20.656503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 66ebcab8-3aea-4cbb-86da-30f120dff344 · outbound

This paper cites Openai’s ceo says the age of giant ai models is already over, Apr 2023.

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models Openai’s ceo says the age of giant ai models is already over, Apr 2023

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:20.644786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9e38cdbe-bb44-4dbd-a584-de1fa6f115b0 · outbound

This paper cites Islam, and Shaolei Ren.

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models Islam, and Shaolei Ren

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:20.632038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9d3adadf-ed99-49ce-9c7f-38280762acd4 · outbound

This paper cites Truthfulqa: Measuring how models mimic human falsehoods, 2022.

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models Truthfulqa: Measuring how models mimic human falsehoods, 2022

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T20:10:20.378311Z

Source-reported events for the cited work

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Observation 246ce983-0234-44c9-b0fb-63cb85becaf0 · outbound

This paper cites Pointer sentinel mixture models, 2016.

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models Pointer sentinel mixture models, 2016

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T20:10:20.382807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 391b524a-2f9f-468b-90e6-df17d384f226 · outbound

This paper cites Are sixteen heads really better than one?, 2019.

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models Are sixteen heads really better than one?, 2019

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:20.601335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 24a3387a-59b4-44d7-b0db-faa9cd0e06b8 · outbound

This paper cites Compact language models via pruning and knowledge distillation, 2024.

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models Compact language models via pruning and knowledge distillation, 2024

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:20.588490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e31bc1dc-ab52-4a6e-941c-4827fc0794ae · outbound

This paper cites Wino- grande: An adversarial winograd schema challenge at scale, 2019.

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models Wino- grande: An adversarial winograd schema challenge at scale, 2019

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:20.574683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ef9c82d6-8232-49ab-bd06-cc1e58e6dd90 · outbound

This paper cites Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter, 2020.

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter, 2020

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T20:10:20.398972Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ed8aff7-5ddb-4a9d-aef1-b534180c4c19 · outbound

This paper cites Llm pruning and distillation in practice: The minitron approach, 2024.

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models Llm pruning and distillation in practice: The minitron approach, 2024

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:20.554220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b8d4c228-3951-428c-a631-c6fd9825221b · outbound

This paper cites Gomez, Lukasz Kaiser, and Illia Polosukhin.

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models Gomez, Lukasz Kaiser, and Illia Polosukhin

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T20:10:20.406236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afb37d68-5ecd-4066-9ab9-fd573a0804f9 · outbound

This paper cites Analyz- ing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned.

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models Analyz- ing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:20.533351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 41172bae-8af3-4d38-9b9e-18b7a030aeda · outbound

This paper cites Sheared llama: Accelerating language model pre-training via structured pruning, 2024.

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models Sheared llama: Accelerating language model pre-training via structured pruning, 2024

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:20.521492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fe0db32b-10d6-4dea-ae52-6ae14ab81a54 · outbound

This paper cites Hel- laswag: Can a machine really finish your sentence?, 2019.

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models Hel- laswag: Can a machine really finish your sentence?, 2019

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:20.508445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.