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Paper Citation Record · LEDGER

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models

As of 10 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 1 inbound Pith citation observation for arXiv:2502.01378.

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

pith.paper-citation-record.v1
2502.01378 v1

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T15:36:04.620053Z

measured 62 of 62 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:53:42.543595Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T04:53:45.038200Z

Reference resolution

61 of 61 outbound references displayed

  • verified exact2
  • verified fuzzy11
  • unresolved48
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7a99774b-1535-48d4-aff4-ec432bf0b684 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models On the Opportunities and Risks of Foundation Models

Reference 1

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source=pdf_text observed=2026-08-09T15:36:04.410687Z digest=sha256:2c0dc8110a1b4593204b802f75f9967da4f771a08bc9f2298a55c10808c84238

Observation b374afaf-2a6c-4e7f-b23c-a16a5ca5012c · outbound

This paper cites Instruction tuning for large language models: A survey.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Instruction tuning for large language models: A survey

Reference 2

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source=pdf_text observed=2026-08-09T15:36:04.414410Z digest=sha256:2d1c5c59f163d4d451ea1e5686883f0892751464d8ccbd00dff0e7f915ea12e1

Observation 2d138c26-9b1d-4a67-a0c0-a548582c3d2e · outbound

This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 3

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source=pdf_text observed=2026-08-09T15:36:04.418330Z digest=sha256:0d2173c54576a561df188a8575b31d7429d78d4ccd5f43799c1cb955afdf5d99

Observation 5d6740cb-8757-4397-a187-de45c8499f91 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 4

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source=pdf_text observed=2026-08-09T15:36:04.422233Z digest=sha256:ac96da325f95082ac548350f4498f0ef4eb062d3f46210585f97d147c1885c33

Observation f8cbc10f-3f9a-4eba-a107-4f7af0d997a5 · outbound

This paper cites Fast monte carlo algorithms for matrices i: Approximating matrix multiplication.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Fast monte carlo algorithms for matrices i: Approximating matrix multiplication

Reference 5

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verified fuzzy
raw_fallback, observed 2026-08-09T15:36:05.499023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T15:36:04.426042Z digest=sha256:fd96ee14a35deb2e3e537d04d2e95397fb504f3dd54ac6fe42a2402130fb96a9

Observation 0d736b1c-f510-4699-95a1-8a7ed4df2900 · outbound

This paper cites Accelerating large language models through partially linear feed-forward network.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Accelerating large language models through partially linear feed-forward network

Reference 6

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raw_fallback, observed 2026-08-09T15:36:05.488132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T15:36:04.429576Z digest=sha256:c73ecfdaa85f203ef441b669d571ad0c91fc784bcf215f1b2d454d2941e4a8e8

Observation 42e27d34-5e5c-4151-97a6-a70ab70c01d5 · outbound

This paper cites First Activations Matter: Training-Free Methods for Dynamic Activation in Large Language Models.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models First Activations Matter: Training-Free Methods for Dynamic Activation in Large Language Models

Reference 7

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local_arxiv, observed 2026-08-09T15:36:04.922900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T15:36:04.433269Z digest=sha256:bd3e4266115efa82b5b5ae18cc3cbcae60262ec7dc1bfa28a115110991246d34

Observation 56d9ee7b-bcd1-435f-99cd-ea56ddb9ad75 · outbound

This paper cites From Low Rank Gradient Subspace Stabilization to Low-Rank Weights: Observations, Theories, and Applications.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models From Low Rank Gradient Subspace Stabilization to Low-Rank Weights: Observations, Theories, and Applications

Reference 8

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source=pdf_text observed=2026-08-09T15:36:04.436881Z digest=sha256:65a43fb2e87def4d6f2e3f4572d802ff62c2148cdb7029ff779de566d35ee446

Observation 053bfaed-9abf-489c-bb52-339420b18400 · outbound

This paper cites LSAQ: Layer-Specific Adaptive Quantization for Large Language Model Deployment.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models LSAQ: Layer-Specific Adaptive Quantization for Large Language Model Deployment

Reference 9

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local_arxiv, observed 2026-08-09T15:36:04.897649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T15:36:04.440594Z digest=sha256:0a3b8e797e55a1b6758ce6075785990fd5f09ea80e5cb60474e3383e7ae43278

Observation 26d22641-5be1-453b-bcca-af9996668f5f · outbound

This paper cites Pushing the Limits of Large Language Model Quantization via the Linearity Theorem.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Pushing the Limits of Large Language Model Quantization via the Linearity Theorem

Reference 10

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source=pdf_text observed=2026-08-09T15:36:04.444303Z digest=sha256:da34e915bf752ff614687f7a7d43051f3fdedee9592c5ef25e0e305a698f647f

Observation c11586e7-efe1-4040-8e5d-32a54250d0bc · outbound

This paper cites Q-GaLore: Quantized GaLore with INT4 Projection and Layer-Adaptive Low-Rank Gradients.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Q-GaLore: Quantized GaLore with INT4 Projection and Layer-Adaptive Low-Rank Gradients

Reference 11

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source=pdf_text observed=2026-08-09T15:36:04.448111Z digest=sha256:ee266091ae917a9159a3dffbeffc2eacd150768b4d7a7e22ebfac420609c95de

Observation e86f556c-2ba9-45b0-9480-61098180e33a · outbound

This paper cites Training-Free Activation Sparsity in Large Language Models.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Training-Free Activation Sparsity in Large Language Models

Reference 12

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source=pdf_text observed=2026-08-09T15:36:04.451732Z digest=sha256:b26dacbb33033d75cc4572e753590613641d68f0125f42d4ba355959c6687e5c

Observation 02a9849c-69e5-4728-a0db-a743ccbcbe25 · outbound

This paper cites LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models

Reference 13

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source=pdf_text observed=2026-08-09T15:36:04.455245Z digest=sha256:bd9443383cf722260aec5e97e01cc5299db55181c2ee6f300e4e4ca2b9921428

Observation 3dfa33b9-e66c-46cc-9151-9ec1a845eb0c · outbound

This paper cites an unresolved cited work.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Unresolved cited work

Reference 15

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T15:36:04.458886Z digest=sha256:d4574e8f353f2bbef87bbb0753d01ee070a0d5b1df5744d6cc1c929be98ae561

Observation b5e68911-353a-40b8-91ea-89cef1e505db · outbound

This paper cites Dolan and Chris Brockett.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Dolan and Chris Brockett

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T15:36:04.462384Z digest=sha256:c893e62a5264a217748a1c42a9be5c0390e36c9344ec3f755840f6cf537566a3

Observation 004d61e4-e93d-4ec9-a92d-4a16360dc3a7 · outbound

This paper cites an unresolved cited work.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Unresolved cited work

Reference 17

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T15:36:04.465834Z digest=sha256:2bd9481b47b945b83f8cc135208d7b1519772fa86a8c2110e3b99b24eabe2e98

Observation 49532871-cfae-462d-b631-bc9d06910a5b · outbound

This paper cites BoolQ: Exploring the surprising difficulty of natural yes/no questions.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models BoolQ: Exploring the surprising difficulty of natural yes/no questions

Reference 18

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T15:36:04.469175Z digest=sha256:21f78357790ffa53a2739cc9aa8166c4c2360cb41c6d68b9a03b7ef497ed35d5

Observation ef933af0-e74b-4b49-9c25-d00bd8288461 · outbound

This paper cites Piqa: Reasoning about phys- ical commonsense in natural language.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Piqa: Reasoning about phys- ical commonsense in natural language

Reference 19

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source=pdf_text observed=2026-08-09T15:36:04.472566Z digest=sha256:2558cc6a4b651a4bf9a0140a3b5755c106652edd10f037ca3b112aaacba75858

Observation a7f429e3-185e-47ce-bae0-732a356b76ac · outbound

This paper cites SocialIQA: Commonsense Reasoning about Social Interactions.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models SocialIQA: Commonsense Reasoning about Social Interactions

Reference 20

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source=pdf_text observed=2026-08-09T15:36:04.476090Z digest=sha256:1b13d940411338199c8cc28b1dc3cf8d7e6a0ea826bff2c2ef22f5569f12a870

Observation a487be32-e9df-4b2f-bdf2-21825767e103 · outbound

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

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 21

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source=pdf_text observed=2026-08-09T15:36:04.480293Z digest=sha256:c95e4308903a46a0591b9ee406c93853458188049f0f019544cf3b06ce39179b

Observation 88eae423-85ee-4608-8743-d23f73943131 · outbound

This paper cites Winogrande: An adversarial winograd schema challenge at scale.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Winogrande: An adversarial winograd schema challenge at scale

Reference 22

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source=pdf_text observed=2026-08-09T15:36:04.484204Z digest=sha256:833753c46877e410a3cef8b9a477679f9d1ba73b8febc02379824f0b6e00825e

Observation ce8b0b48-48b6-4c80-a99d-838a19683567 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 23

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source=pdf_text observed=2026-08-09T15:36:04.487020Z digest=sha256:54cbb6f58926bc349e6bf07734b068dbf932f48986e4433dcc2bf8f4242c10af

Observation ecc1dff6-be7a-4d7b-aa89-880a23302ca2 · outbound

This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 24

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source=pdf_text observed=2026-08-09T15:36:04.490182Z digest=sha256:067149f6d2286acc9aebb8ab863d74f729293708bcdc0f8d1086bd2d31d83f40

Observation 7e52f85e-e1a6-4a72-a7fd-810aeb40f650 · outbound

This paper cites Solving General Arithmetic Word Problems.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Solving General Arithmetic Word Problems

Reference 25

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source=pdf_text observed=2026-08-09T15:36:04.493340Z digest=sha256:19d69dc6083dd91aac6c14ae5b2bdbbbdaea0121d9657a804375dc9215e093cb

Observation dcd6a529-f118-489c-897e-dc45e5114cc1 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Training Verifiers to Solve Math Word Problems

Reference 26

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source=pdf_text observed=2026-08-09T15:36:04.496474Z digest=sha256:ab9b207449bfdccaf90efbdd1f5eaef8ceb4cf233dfa823efcc94e8307722837

Observation 7cd19fc0-b7a5-452d-b5ff-9015eacd5ca8 · outbound

This paper cites Learning to solve arithmetic word problems with verb categorization.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Learning to solve arithmetic word problems with verb categorization

Reference 27

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verified fuzzy
raw_fallback, observed 2026-08-09T15:36:05.420344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T15:36:04.499709Z digest=sha256:66c0e05873ee391387286ccb652537d7fb9a502749687c7d9a45cc3371f91c68

Observation 5120e42f-836b-4cf5-bc13-e0379b6e6fda · outbound

This paper cites Program induction by rationale generation: Learning to solve and explain algebraic word problems.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Program induction by rationale generation: Learning to solve and explain algebraic word problems

Reference 28

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verified fuzzy
raw_fallback, observed 2026-08-09T15:36:05.408255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T15:36:04.503299Z digest=sha256:faf559bdd3b371f16a8f674c363658ab97778bf7d655166f7dc9a68f0956cba5

Observation 8a3a4dcc-5bf0-4a2f-9943-24926d6da7ba · outbound

This paper cites Parsing algebraic word problems into equations.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Parsing algebraic word problems into equations

Reference 29

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source=pdf_text observed=2026-08-09T15:36:04.510195Z digest=sha256:2c3a503ef0eafef9c286c40b81dfd560a73f2c8ba6435bfdb3b0254c63d820a6

Observation d4aab167-4347-4a18-b0d1-6567589f69c2 · outbound

This paper cites an unresolved cited work.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Unresolved cited work

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T15:36:04.513740Z digest=sha256:c3a2c9c1d131d0fa4fd44dd6f55d5c2486776431a721d636978c5618b1c646ca

Observation 8a8982f4-86c7-4029-b7eb-153b53ec0bc4 · outbound

This paper cites MAWPS: A math word problem repository.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models MAWPS: A math word problem repository

Reference 31

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raw_fallback, observed 2026-08-09T15:36:05.373645Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T15:36:04.517167Z digest=sha256:aa57676b720770efdf5d919c92e889f1a560db06e1e4cb12318b9f1817a97c43

Observation 796d26dd-92fb-445d-b2e1-e52056049789 · outbound

This paper cites Llama 2: Open foundation and fine-tuned chat models, 2023.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Llama 2: Open foundation and fine-tuned chat models, 2023

Reference 32

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source=pdf_text observed=2026-08-09T15:36:04.520269Z digest=sha256:73383414d50fd1256f6c0b88e94869abfb2b654fdd494b5529bbef1803e265ad

Observation 71479d94-dfef-42d1-9708-ab453b7b9174 · outbound

This paper cites Llama 3 model card.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Llama 3 model card

Reference 33

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source=pdf_text observed=2026-08-09T15:36:04.523564Z digest=sha256:5070b4881269983caa05a52f859555e7d9340598c607f3ca2da85be9355c0442

Observation 11b5314d-38b4-46a4-b6ab-eb045cb51925 · outbound

This paper cites Attention is all you need.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Attention is all you need

Reference 34

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source=pdf_text observed=2026-08-09T15:36:04.526711Z digest=sha256:3e213f28432106edae1d334fb1d1374bddf60f7e2a271c0fe5010186649ecc8a

Observation 906cc04e-9410-4e80-a887-8eb1e57b0711 · outbound

This paper cites Improving language understanding by generative pre-training.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Improving language understanding by generative pre-training

Reference 35

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source=pdf_text observed=2026-08-09T15:36:04.530091Z digest=sha256:c378d7208fc760beb9fd92da012405f42116b91cf7d0d164936a84e32bd75899

Observation cde9661f-7e2a-4b15-af4d-7b67c1b20c2b · outbound

This paper cites Language models are unsupervised multitask learners.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Language models are unsupervised multitask learners

Reference 36

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source=pdf_text observed=2026-08-09T15:36:04.533313Z digest=sha256:b625ca513a9e663d5d1c75767026955a3e756bc4b0baeff4cd9f943d97a838ad

Observation c86edbe3-7a32-4c3a-840c-d3f60995661d · outbound

This paper cites Language models are few-shot learners.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Language models are few-shot learners

Reference 37

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source=pdf_text observed=2026-08-09T15:36:04.536602Z digest=sha256:22b133009796e8332835d9275b9f585cccfdcde070bc8ee19a7ba368dc5d2156

Observation c1dd38b1-9c3a-4c41-874c-9696f866578e · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models OPT: Open Pre-trained Transformer Language Models

Reference 38

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source=pdf_text observed=2026-08-09T15:36:04.539969Z digest=sha256:f87abbf064b84aff8b35f05a4e76e3e39080a38ac68ea6393340ba6af3bcaad9

Observation 19309529-df6a-4457-bcd3-bea4486e426e · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 39

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source=pdf_text observed=2026-08-09T15:36:04.543675Z digest=sha256:5ec10c671dffb221fe4dcdb19915bfdb76e1c9524f5efd024260a8d7107dd52d

Observation 4f1c1232-f414-4e3e-bed3-74c31b37b8f5 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 40

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source=pdf_text observed=2026-08-09T15:36:04.547121Z digest=sha256:ff515c9f3fc7e7e0133be811ad992155f854dd03b44a2f4bb5117a6c2e733aa4

Observation b791e550-9fce-42cd-8ef9-33c354a64def · outbound

This paper cites The Llama 3 Herd of Models.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models The Llama 3 Herd of Models

Reference 41

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source=pdf_text observed=2026-08-09T15:36:04.550728Z digest=sha256:3d2fb6043e0b9387066d7d53817f93ac5543951a9bb10d083fdee046ff0ba5a8

Observation cb9227cc-62e5-438a-bfdc-6b29ac800c0b · outbound

This paper cites Bloom: A 176b-parameter open-access multilingual language model.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Bloom: A 176b-parameter open-access multilingual language model

Reference 42

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raw_fallback, observed 2026-08-09T15:36:05.324192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T15:36:04.554248Z digest=sha256:78b9c0fde5b805981e9e23e85fba8bd90143d5ab44018d0728ed5be5c03e5c7f

Observation 2a648d4e-cc30-483e-8642-ede31c079dff · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 43

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source=pdf_text observed=2026-08-09T15:36:04.558116Z digest=sha256:3b075917facc5611d813db39c450c2dfc192d3a4367288e88a07225b3a452ec6

Observation f3ae2700-0bca-4105-8dfa-cc8ac20764d1 · outbound

This paper cites The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only

Reference 44

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source=pdf_text observed=2026-08-09T15:36:04.561844Z digest=sha256:f857bb5ceff1102d58d2d9d66231e0881be97967cb8a8299fd4f9cf79505a08b

Observation 9ab0943b-1e86-4ac2-894c-369b9ddf5a22 · outbound

This paper cites Parameter-efficient transfer learning for nlp.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Parameter-efficient transfer learning for nlp

Reference 45

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source=pdf_text observed=2026-08-09T15:36:04.565822Z digest=sha256:65a817b10b0a03b73d1d5b6a71e517185a6fe605cb22bedc4405ab45e88cbcc9

Observation 3a80791f-8fa5-4756-8f50-68105d016cfb · outbound

This paper cites AdapterHub: A Framework for Adapting Transformers.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models AdapterHub: A Framework for Adapting Transformers

Reference 46

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source=pdf_text observed=2026-08-09T15:36:04.569028Z digest=sha256:a5f34cf13f681cdbb5bf597a912676c1d904df090b3b5d3f4626674a58cba11a

Observation f6f0e3fd-d2c0-4beb-bd59-181a6cf11535 · outbound

This paper cites Relora: High- rank training through low-rank updates.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Relora: High- rank training through low-rank updates

Reference 47

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source=pdf_text observed=2026-08-09T15:36:04.572728Z digest=sha256:243d1cff7ed307e8fe1ae74f32cb11182305ad5278532bb2c4b8cfb6f9904c6d

Observation abc781bc-f05c-4772-a1f8-6438a374a6ed · outbound

This paper cites S$^{2}$FT: Efficient, scalable and generalizable LLM fine-tuning by structured sparsity.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models S$^{2}$FT: Efficient, scalable and generalizable LLM fine-tuning by structured sparsity

Reference 48

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raw_fallback, observed 2026-08-09T15:36:05.297632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T15:36:04.576349Z digest=sha256:d6997c8a245a30ff282b59bd92f2c763bd9395cc65ade15c1ccf7c4a7c8652aa

Observation 828a1067-31be-409d-bf22-9cdd11ee32d0 · outbound

This paper cites SLTrain: a sparse plus low-rank approach for parameter and memory efficient pretraining.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models SLTrain: a sparse plus low-rank approach for parameter and memory efficient pretraining

Reference 49

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source=pdf_text observed=2026-08-09T15:36:04.579549Z digest=sha256:b561eda8c8c556cde0757458a41da86d942f90c8b5fcec1b270bf41720064b25

Observation 5b35d931-cb0a-4ea5-ae22-bd8a186a8584 · outbound

This paper cites LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning

Reference 50

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source=pdf_text observed=2026-08-09T15:36:04.583127Z digest=sha256:bf163dccd1749138966517f024bf8211bc5c5ca8342c1ba170cd8036db8ce1ea

Observation 8163b80d-d668-4393-ac9d-ae4bab343c4a · outbound

This paper cites GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

Reference 51

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source=pdf_text observed=2026-08-09T15:36:04.586608Z digest=sha256:aec489085026f94f40a5cde8af94ffd957e4e67a87061d2e5ab63e48708383bd

Observation dfac57be-e5e6-4ba9-8383-5e67cd2b3517 · outbound

This paper cites Subspace Optimization for Large Language Models with Convergence Guarantees.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Subspace Optimization for Large Language Models with Convergence Guarantees

Reference 52

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source=pdf_text observed=2026-08-09T15:36:04.589680Z digest=sha256:9bf76029cb757e5e2800248d016571e2c6c5d63dfebf2dc3e9f63802b7158fc1

Observation 90c3e789-de35-4d12-ba7f-00a8d4aed1e0 · outbound

This paper cites Flora: Low-Rank Adapters Are Secretly Gradient Compressors.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Flora: Low-Rank Adapters Are Secretly Gradient Compressors

Reference 53

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source=pdf_text observed=2026-08-09T15:36:04.592683Z digest=sha256:0e255cf73df217ef7c85097e82ba83cd01acb6e4954235b5ef47c9191471dbab

Observation e0758e33-bda0-4aca-9cb0-f0d89f52189c · outbound

This paper cites Back razor: Memory-efficient transfer learning by self-sparsified backpropagation.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Back razor: Memory-efficient transfer learning by self-sparsified backpropagation

Reference 54

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raw_fallback, observed 2026-08-09T15:36:05.182835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T15:36:04.595492Z digest=sha256:d5aca759d652ca3bbeabf03d85f209cafe9773e76710dd8a2eb2ea0846d17f2f

Observation a1e9e345-4ecc-47db-8603-01d3f18c8a71 · outbound

This paper cites Sheared back- propagation for fine-tuning foundation models.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Sheared back- propagation for fine-tuning foundation models

Reference 55

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raw_fallback, observed 2026-08-09T15:36:05.172074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T15:36:04.598687Z digest=sha256:52a4bd6d65d7e0e815d97aacded8c6f1968a375ab5567d85fa49b417531a7b4f

Observation 634db3ce-5d63-410d-98fd-a21824293f93 · outbound

This paper cites Mixed Precision Training.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Mixed Precision Training

Reference 56

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source=pdf_text observed=2026-08-09T15:36:04.601816Z digest=sha256:2c9739b5f6cb81a6a436699bf449da0a75bb5eb1f5227b9622f5801e6b10fdec

Observation 4f345764-70c8-4294-9e7e-b086a8e8e989 · outbound

This paper cites Qlora: Efficient finetuning of quantized llms.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Qlora: Efficient finetuning of quantized llms

Reference 57

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source=pdf_text observed=2026-08-09T15:36:04.605737Z digest=sha256:fe313e7d73cb25af55a74d6664955514355b3224af555ccb1d2edb85d48f1ee5

Observation 5f0b7288-dee8-4114-b32d-914e834b639b · outbound

This paper cites APOLLO: SGD-like Memory, AdamW-level Performance.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models APOLLO: SGD-like Memory, AdamW-level Performance

Reference 58

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source=pdf_text observed=2026-08-09T15:36:04.609015Z digest=sha256:7dfb47a232145f369d5963569f9155bccebdf4e5d9e98536ad7a817913438f43

Observation dde316be-93f3-4f55-bfb5-36f4c2dd0173 · outbound

This paper cites DropBP: Accelerating Fine-Tuning of Large Language Models by Dropping Backward Propagation.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models DropBP: Accelerating Fine-Tuning of Large Language Models by Dropping Backward Propagation

Reference 59

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source=pdf_text observed=2026-08-09T15:36:04.612958Z digest=sha256:1538d5efa44d3c713fe5db4c799dcc9b6dddc0324028bf77f619646591bb53ec

Observation 9faff1bb-5271-4124-88c3-f2164ea7bcb8 · outbound

This paper cites an unresolved cited work.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Unresolved cited work

Reference 60

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T15:36:04.616640Z digest=sha256:db2b203065a8a59000f72e41f53f8407ef9fc0cc9d4bcd9da40aa10abbcff68a

Observation 7b4b0cc1-937e-4c1b-ba6a-274839a4ecba · outbound

This paper cites an unresolved cited work.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Unresolved cited work

Reference 61

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T15:36:04.620053Z digest=sha256:af4dcad8b92471bdf82ae354c08626a5f2323ec56d336012b91a87c9501f6401

Observation edce4c6c-da2f-4de1-94a8-67175cca20e2 · outbound

This paper cites an unresolved cited work.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Unresolved cited work

Reference 2017

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source=pdf_text observed=2026-08-09T15:36:04.506781Z digest=sha256:b19191368271d98bd2fe9ae56957bc02d4e05042355884462c94d22c1152336a

Pith citing papers

Observation f666ed42-bca9-4958-aeaa-5aa3a8b2ff96 · inbound

From over-reliance to smart integration: using Large-Language Models as translators between specialized modeling and simulation tools cites this paper.

From over-reliance to smart integration: using Large-Language Models as translators between specialized modeling and simulation tools CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models

Reference 2025

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local_arxiv, observed 2026-08-07T04:53:45.137947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T04:53:42.543595Z digest=sha256:662a3661dd9ed8fda256e315d53dd71d298dfd842e24dabf82e481709fead33b