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Paper Citation Record · LEDGER

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning

As of 10 August 2026, this Paper Citation Record lists 75 of 75 outbound references and 2 inbound Pith citation observations for arXiv:2505.22355.

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

pith.paper-citation-record.v1
2505.22355 v1

Coverage vector

measured 75 of 75 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:15:55.842329Z

measured 77 of 77 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-21T05:59:59.183023Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T06:03:59.420139Z

Reference resolution

75 of 75 outbound references displayed

  • verified exact1
  • verified fuzzy35
  • unresolved38
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 12f79f23-09ae-4692-aa1b-6486511d8153 · outbound

This paper cites Composable sparse fine-tuning for cross-lingual transfer.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Composable sparse fine-tuning for cross-lingual transfer

Reference 1

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raw_fallback, observed 2026-08-07T13:16:03.836514Z

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.

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Observation dcb45d7c-daf6-460e-93e4-4de3b160d0fa · outbound

This paper cites Fine-Tuning LLMs: LoRA or Full-Parameter? An in-depth Analysis with Llama-2.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Fine-Tuning LLMs: LoRA or Full-Parameter? An in-depth Analysis with Llama-2

Reference 2

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raw_fallback, observed 2026-08-07T13:16:03.609223Z

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=2026-08-07T13:15:36.582348Z digest=sha256:e04c2c3144ee921e31343ab927493c08a75fa5c4cd286a8d66672017b5f59c9e

Observation c5289ade-3c36-4247-820a-1ef255278aa5 · outbound

This paper cites Machine learning theory.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Machine learning theory

Reference 3

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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=2026-08-07T13:15:36.759818Z digest=sha256:7ef5bf8adca0dcf8e5ec67da76919bb50437e2c91341a36095e25321ddb03fca

Observation d4987e2d-c24d-4d54-995e-2c4227742f20 · outbound

This paper cites Attention fusion: a light yet efficient late fusion mechanism for task adaptation in nlu.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Attention fusion: a light yet efficient late fusion mechanism for task adaptation in nlu

Reference 4

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raw_fallback, observed 2026-08-07T13:16:03.097240Z

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=2026-08-07T13:15:36.874038Z digest=sha256:60522fdc17ccb58653dbe272d592530789492848453feb8499771307f975967e

Observation 687c27bb-9ecd-447f-bbda-e96fc3c40e7e · outbound

This paper cites SemEval- 2019 task 3: EmoContext contextual emotion detection in text.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning SemEval- 2019 task 3: EmoContext contextual emotion detection in text

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:37.049804Z digest=sha256:0f3427c63b09d23d599dbae1c117f6cc703b271d95ad247431ed3577b0505ec7

Observation 90353056-37e9-477a-9947-833d833a3134 · outbound

This paper cites Parameter-Efficient Fine-Tuning Design Spaces.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Parameter-Efficient Fine-Tuning Design Spaces

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:37.186380Z digest=sha256:97376538e5f46464047d03b28c0d77f64f79f946fc56a263a0a447e44f61a8b3

Observation cbe02d9e-607d-4414-b9b6-9e8b69548659 · outbound

This paper cites Gonzalez, Ion Stoica, and Eric P.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Gonzalez, Ion Stoica, and Eric P

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:37.363511Z digest=sha256:eb12123e414a2863af60d0c293525efa8097bca2ea99bd164a44a34fe5dea343

Observation 34a45761-fbcd-4bfa-8015-0a75e6451adc · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Training Verifiers to Solve Math Word Problems

Reference 8

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:37.522359Z digest=sha256:c2a7641c2de720104a1f20c92de5e1defb6790f1c5ac8e62b6073f2b007b1d14

Observation 1377627b-25c8-402b-a37b-5835adf133fa · outbound

This paper cites A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:37.660314Z digest=sha256:e2fb5114f6f8544b26feadbf37c71cb9b9e8843351a17d464acef5f1c99d02f5

Observation 419ee7b2-7da3-4f03-9b56-e536371315c7 · outbound

This paper cites Identifying and attacking the saddle point problem in high-dimensional non- convex optimization.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Identifying and attacking the saddle point problem in high-dimensional non- convex optimization

Reference 10

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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=2026-08-07T13:15:37.762392Z digest=sha256:78d18bfd4f3589570ef8e856c2e200d0b9b167a2967b1d16734215f5a2a77438

Observation 26a3b6a5-a3d4-4ad5-aad4-2dea01183bb1 · outbound

This paper cites Parameter- efficient fine-tuning of large-scale pre-trained language models.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Parameter- efficient fine-tuning of large-scale pre-trained language models

Reference 11

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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=2026-08-07T13:15:37.881514Z digest=sha256:bd672060bfddbe8bc420ad5434862b8407e898ff7587da33b27633803f9d571a

Observation 2eac4ad3-6e2f-45f4-8d24-bd325366bb7e · outbound

This paper cites KronA: Parameter Efficient Tuning with Kronecker Adapter.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning KronA: Parameter Efficient Tuning with Kronecker Adapter

Reference 12

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source=pdf_text observed=2026-08-07T13:15:38.030936Z digest=sha256:51863a71e692c1a51f53a64d98c3538190bc3edbbd622536609dcc09a35b2af6

Observation 2b2d3dba-fd6c-4996-a2d2-25bacaa1d1ac · outbound

This paper cites Rank Diminishing in Deep Neural Networks.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Rank Diminishing in Deep Neural Networks

Reference 13

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local_arxiv, observed 2026-08-07T13:15:56.506053Z

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=2026-08-07T13:15:38.199860Z digest=sha256:e91f24157dbf881a6e58b6660dd0c7d3dc2d9a9cd619fb99e9ee2ce0d895cf1a

Observation b98447e0-0554-4cc8-80ae-58f44909fd43 · outbound

This paper cites Sharpness-Aware Minimization for Efficiently Improving Generalization.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Sharpness-Aware Minimization for Efficiently Improving Generalization

Reference 14

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:38.318455Z digest=sha256:15fe8efca6acb6f43ec3d0c7733c3f75badaa1437d57de2c55eab047753ae2ae

Observation 87e43c74-1627-436e-9071-1bfbe63c44aa · outbound

This paper cites Robustness gym: Unifying the NLP evaluation landscape.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Robustness gym: Unifying the NLP evaluation landscape

Reference 15

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raw_fallback, observed 2026-08-07T13:16:02.295718Z

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=2026-08-07T13:15:38.441936Z digest=sha256:7708beedb8c44b7a9d0c39467af6778cff76636871e4ceb860ffdb43b85a6c9b

Observation 6aab1f42-997c-408b-a688-50fde9c37782 · outbound

This paper cites A survey of adversarial defenses and robustness in nlp.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning A survey of adversarial defenses and robustness in nlp

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:16:02.019454Z

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=2026-08-07T13:15:38.574186Z digest=sha256:669d3c1cb9ff2af929e10d77fe3908df248c71fe4b68d2357e88659bf6b5d8af

Observation 33125f48-a545-46ff-b590-3471570c0df3 · outbound

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

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 17

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:38.680064Z digest=sha256:c7f40dd7d26281eec5417f5caae7c52d221f598edea91d6e076926bd8b57e52e

Observation 1452da4b-d04b-4518-be58-6dcca4a6595d · outbound

This paper cites Parameter Efficient Instruction Tuning: An Empirical Study.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Parameter Efficient Instruction Tuning: An Empirical Study

Reference 18

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local_arxiv, observed 2026-08-07T13:15:56.316577Z

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=2026-08-07T13:15:38.811243Z digest=sha256:dfd8198ba4e62961d693e0c16c9bc1958a57f2e84bad7bbdbe814cae40f3218f

Observation 18d56fac-2f96-464b-9b00-0cd59fa76ad6 · outbound

This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 19

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no resolver link, observed 2026-08-07T13:15:38.927860Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:38.927860Z digest=sha256:d3acd69f3d45af2a70a8366f313e05296291b24a2ca97658be9f3d5d522b140d

Observation 5033d86d-2885-44d6-b57f-1e07d368427b · outbound

This paper cites Llm-adapters: An adapter family for parameter-efficient fine- tuning of large language models.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Llm-adapters: An adapter family for parameter-efficient fine- tuning of large language models

Reference 20

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raw_fallback, observed 2026-08-07T13:16:01.805125Z

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=2026-08-07T13:15:39.181520Z digest=sha256:04cb5b87ce94f2f52ace95bb3bf3240e39a0f3366bb1b2678c25f609bd1060c4

Observation 0452cebd-4809-4724-8a9f-5af721ab6b57 · outbound

This paper cites Hira: Parameter-efficient hadamard high-rank adaptation for large language models.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Hira: Parameter-efficient hadamard high-rank adaptation for large language models

Reference 21

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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=2026-08-07T13:15:39.291715Z digest=sha256:82ad033bf306c3bec4d611d2843d9a03ab5c3bbbe5511f0295967e0479b590f3

Observation 8bbbc91b-d652-4aa9-9404-0388fd60c4d2 · outbound

This paper cites Adversarial examples for evaluating reading comprehension systems.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Adversarial examples for evaluating reading comprehension systems

Reference 22

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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=2026-08-07T13:15:39.405947Z digest=sha256:d5cf5ee7863da82477579bc85ef14a76d10e4c1c151ebe6b9b56eb0be4017c5b

Observation 58551801-1c41-410b-abb7-38034034acbc · outbound

This paper cites Adversarial Examples for Evaluating Reading Comprehension Systems.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Adversarial Examples for Evaluating Reading Comprehension Systems

Reference 23

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source=pdf_text observed=2026-08-07T13:15:39.516491Z digest=sha256:171f7ca3442a97a7bf006803c35eb760e495d54ea9f667cd8fc0226008d2ec5b

Observation bd2c7bce-fac4-40ca-9130-b50717598f5a · outbound

This paper cites Mistral 7B.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Mistral 7B

Reference 24

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source=pdf_text observed=2026-08-07T13:15:39.689990Z digest=sha256:386b9615f5c232417e8563fa5626e7ef464868c40769930d98058f756ce50d7c

Observation 595688af-1c8a-43e4-81d0-994c2d73856b · outbound

This paper cites BYOM: Building Your Own Multi-Task Model For Free.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning BYOM: Building Your Own Multi-Task Model For Free

Reference 25

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no resolver link, observed 2026-08-07T13:15:39.754741Z

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source=pdf_text observed=2026-08-07T13:15:39.754741Z digest=sha256:7b466e7e1cab090e76e15ed3f50463183c572460a91ed0b699ee81cf24843a39

Observation 3ca2f388-d13c-4179-899e-2266dbaaa8cc · outbound

This paper cites Viggo: A video game corpus for data-to-text generation in open-domain conversation.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Viggo: A video game corpus for data-to-text generation in open-domain conversation

Reference 26

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raw_fallback, observed 2026-08-07T13:16:01.097881Z

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=2026-08-07T13:15:39.838440Z digest=sha256:4b92c67da1c7b4203fd22a335e8c873bb3475bee769cc9ecbd21a27dbf6da7d0

Observation b67ff8d5-0e9b-45e4-93f7-bb5ee0fb3dab · outbound

This paper cites Scaling Laws for Neural Language Models.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Scaling Laws for Neural Language Models

Reference 27

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:39.921205Z digest=sha256:fbfb3cdf5945b1b220b3024ff2908bab5b85ebc4e88acf60caf534715dd21b61

Observation 4bb41e2c-d6ea-4f2c-be09-e0bd1a049f51 · outbound

This paper cites Compacter: Efficient low-rank hypercomplex adapter layers.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Compacter: Efficient low-rank hypercomplex adapter layers

Reference 28

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no resolver link, observed 2026-08-07T13:15:39.995899Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:39.995899Z digest=sha256:f767a0c3e27da3a39f9c6942e8e5bf2046996db386c31cc17af6218e16c05de9

Observation 208642fc-4995-4ec3-802f-bb0efe11ef5f · outbound

This paper cites Bias plus variance decomposition for zero-one loss functions.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Bias plus variance decomposition for zero-one loss functions

Reference 29

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verified fuzzy
raw_fallback, observed 2026-08-07T13:16:00.907734Z

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=2026-08-07T13:15:40.059767Z digest=sha256:f2d3a02e08cf68703b82e2c24725bb35ee680d1bed772231b397434f55ffb690

Observation c09185b5-02c3-4826-915e-35b2c989352d · outbound

This paper cites Fine-Tuning Llama-2: A Comprehensive Case Study for Tailoring Models to Unique Applications.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Fine-Tuning Llama-2: A Comprehensive Case Study for Tailoring Models to Unique Applications

Reference 30

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verified fuzzy
raw_fallback, observed 2026-08-07T13:16:00.714779Z

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=2026-08-07T13:15:40.139808Z digest=sha256:1f086c191722d0f15f9b2cd280bb7862d8c651e943d8b428c03e803b2cba8927

Observation f5b87aa2-e10b-4844-848f-938c461c6ca1 · outbound

This paper cites The power of scale for parameter-efficient prompt tuning.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning The power of scale for parameter-efficient prompt tuning

Reference 31

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raw_fallback, observed 2026-08-07T13:16:00.531571Z

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=2026-08-07T13:15:40.256605Z digest=sha256:d9bd8528ce87b2ac08740b632a4d5e2929ccb5d6bee57aa4aff10330fa138ec4

Observation ba3a2de4-834a-4ec9-a469-348fd83c155b · outbound

This paper cites BART: denoising sequence-to-sequence pre- training for natural language generation, translation, and comprehension.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning BART: denoising sequence-to-sequence pre- training for natural language generation, translation, and comprehension

Reference 32

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raw_fallback, observed 2026-08-07T13:16:00.327525Z

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=2026-08-07T13:15:40.317550Z digest=sha256:1296073a0fae0a76c12f6e0788b4f8bcd23a72ebca8a362f599eaf1cdcda1c13

Observation 1a364875-df6b-4d88-9fd8-7b135d3afba6 · outbound

This paper cites Measuring the intrinsic dimension of objective landscapes.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Measuring the intrinsic dimension of objective landscapes

Reference 33

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raw_fallback, observed 2026-08-07T13:16:00.146513Z

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=2026-08-07T13:15:40.383650Z digest=sha256:5b5ee507eca2ec7ef8992eb2b0109ba3add6fab87a7e8ef33aa0e54e488dcc68

Observation 14fdf519-0097-4acc-9c5a-36d69afa2748 · outbound

This paper cites Prefix-tuning: Optimizing continuous prompts for generation.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Prefix-tuning: Optimizing continuous prompts for generation

Reference 34

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no resolver link, observed 2026-08-07T13:15:40.444743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:40.444743Z digest=sha256:084669e4d5ff88b5180e7a4bf7a4a35901f3cae89342a2e181dbef5ef020cc06

Observation 851674e6-c71a-4aa2-9dee-5f4cb0250ae5 · outbound

This paper cites Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 35

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source=pdf_text observed=2026-08-07T13:15:40.547680Z digest=sha256:d1d83e4781b8aec2c23965682d8ebaeedd5dc25cf5d3092dfb0a50c62d367d32

Observation 984b72ce-b176-4fba-85fd-b26c46e6685d · outbound

This paper cites MaLA-500: Massive Language Adaptation of Large Language Models.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning MaLA-500: Massive Language Adaptation of Large Language Models

Reference 36

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source=pdf_text observed=2026-08-07T13:15:40.661396Z digest=sha256:75066182fdd7d3eac6f10a44ad11fe8e3e787e9d465f6e953c708213eca57a03

Observation 724c77be-d3f7-4802-bc6d-ab53e4ebf3fe · outbound

This paper cites Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning

Reference 37

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verified fuzzy
raw_fallback, observed 2026-08-07T13:15:59.895873Z

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=2026-08-07T13:15:40.744269Z digest=sha256:f9ce8266b064f32ce63796b82a2d056d7291d32d42572792707ce5a5c3b05446

Observation 0c264f59-2a2a-4ad5-9528-041921dc133b · outbound

This paper cites Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning

Reference 38

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:40.805362Z digest=sha256:a6cc9c74c06cd334a0f083ad859cbd0ccabf059101df74c84f5ed853b5cf7736

Observation 15f10fbe-82e8-4a88-beb6-67e401725a5f · outbound

This paper cites A robust adversarial training approach to machine reading comprehension.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning A robust adversarial training approach to machine reading comprehension

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:15:59.658680Z

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=2026-08-07T13:15:40.885986Z digest=sha256:b761c285664b3340d24436133a2eeafc4f68569c324b7698035105e4a832bda7

Observation 54772026-c1e0-4ffa-82de-c58cc9fd5efd · outbound

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

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 40

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no resolver link, observed 2026-08-07T13:15:40.970414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:40.970414Z digest=sha256:da6cfc49fcbf1c9024b0fa5a36914108253af136135753e05a866120c3741444

Observation 42a32492-fb45-484a-9e38-02d7c81fe6a9 · outbound

This paper cites Multilingual denoising pre-training for neural machine translation.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Multilingual denoising pre-training for neural machine translation

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:15:59.414841Z

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=2026-08-07T13:15:41.034754Z digest=sha256:0b0ec677f4eb33dbb1e63532629cd069cf6478e25e8f8ed4877fecf574e397a9

Observation 2e3b0e7d-0f44-43e3-a0a7-358d6297b950 · outbound

This paper cites HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy

Reference 42

Resolution
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no resolver link, observed 2026-08-07T13:15:41.039555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:41.039555Z digest=sha256:847e946b05878f94f985c5ee6bfaf5eedca886f6a04cfa55e8fa53e45dc28ffc

Observation fa20ead6-55c7-4201-a814-661fafc423df · outbound

This paper cites Hidden factors and hidden topics: understanding rating dimensions with review text.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Hidden factors and hidden topics: understanding rating dimensions with review text

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:15:59.229472Z

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=2026-08-07T13:15:41.044050Z digest=sha256:3be7151e0b3915fee71205db89fe9eef578ff88e869a4eab6b0ae0ca5eccb5a4

Observation 2298b2e2-7bb6-4c8c-a958-45f97552efe8 · outbound

This paper cites an unresolved cited work.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Unresolved cited work

Reference 44

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raw_fallback, observed 2026-08-07T13:15:59.005445Z

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=2026-08-07T13:15:53.878264Z digest=sha256:da2b7bbee41bca0c4d9cdac37a2d78e21a3451f348d902055ac27b5ad231e3ae

Observation 8991882c-3e58-49ce-8dec-15c8029035dd · outbound

This paper cites Morris, Eli Lifland, Jin Yong Yoo, Jake Grigsby, Di Jin, and Yanjun Qi.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Morris, Eli Lifland, Jin Yong Yoo, Jake Grigsby, Di Jin, and Yanjun Qi

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:15:58.829639Z

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=2026-08-07T13:15:53.902121Z digest=sha256:d01ab11fab2632f01c07a6c033a7d932f8b8b66d8766b5d8bd4f3241ff639b26

Observation 0fc136bd-353e-44b3-8d8d-fb711d037128 · outbound

This paper cites A modern take on the bias-variance tradeoff in neural networks.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning A modern take on the bias-variance tradeoff in neural networks

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:15:58.647517Z

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=2026-08-07T13:15:53.921183Z digest=sha256:b495b98576d09ac3560605f2be73a049d824189d4f9aad0324f4a16bd5c6518a

Observation 781a4c6f-717c-46b8-911d-06dda19d32e2 · outbound

This paper cites Learn more, but bother less: parameter efficient continual learning.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Learn more, but bother less: parameter efficient continual learning

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:15:58.528083Z

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=2026-08-07T13:15:53.962429Z digest=sha256:54c4209bcdc8dc48adc4a676034df2bec09a312ad7d334898cb221166b48e739

Observation 0af02282-ef80-4015-a23b-f1685ef01d7a · outbound

This paper cites Know what you don’t know: Unanswerable ques- tions for squad.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Know what you don’t know: Unanswerable ques- tions for squad

Reference 48

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verified fuzzy
raw_fallback, observed 2026-08-07T13:15:58.402648Z

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=2026-08-07T13:15:53.984276Z digest=sha256:4f6e66e0063b1943134be884380aa2910f92e72b85e934aa66e88a02a3accbb8

Observation 6d648650-517d-434a-845a-001ba5d7d64a · outbound

This paper cites Finetuning LLMs with LoRA and QLoRA: Insights from Hundreds of Experiments.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Finetuning LLMs with LoRA and QLoRA: Insights from Hundreds of Experiments

Reference 49

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verified fuzzy
raw_fallback, observed 2026-08-07T13:15:58.263088Z

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=2026-08-07T13:15:54.023521Z digest=sha256:2e394334a6b2ed7f4e5e90d8c8e4b4251f229f4732b4f7a293e2c581a2f742e6

Observation 09fecb0d-2c84-4cb9-841c-53b7264b3c73 · outbound

This paper cites The measure of the critical values of differentiable maps.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning The measure of the critical values of differentiable maps

Reference 50

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no resolver link, observed 2026-08-07T13:15:54.064996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:54.064996Z digest=sha256:eb3e4e6bc6aa51cd29f059764c23ae3a3021a8d585aded0e3807ff93cfe69744

Observation cddc0f6a-6d9e-48ce-b38b-120bba7c3c92 · outbound

This paper cites Recursive deep models for semantic compositionality over a sentiment treebank.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Recursive deep models for semantic compositionality over a sentiment treebank

Reference 51

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:54.097363Z digest=sha256:1de48999b5a9ce9aad72bc6cdc3fa7fd71a4948e736b51ef82b26c33da641873

Observation 38854a62-521c-4abe-b29a-2673f5044541 · outbound

This paper cites Stanford alpaca: An instruction-following llama model, 2023.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Stanford alpaca: An instruction-following llama model, 2023

Reference 52

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no resolver link, observed 2026-08-07T13:15:54.137803Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:54.137803Z digest=sha256:97a17447e3fca520e1cb41bfd87e9a53ff572c659354cb561eff61485c53a6e4

Observation 4f5d9545-870b-4f7e-a2ac-e391ac171ca7 · outbound

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

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 53

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no resolver link, observed 2026-08-07T13:15:54.211557Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:54.211557Z digest=sha256:9728924478b00ab922a2439e8913b7b552c9475a5950dc700ff3a0ee8a468372

Observation 71a6feda-aeac-49f2-8493-bd21401a207c · outbound

This paper cites Attention is all you need.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Attention is all you need

Reference 54

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no resolver link, observed 2026-08-07T13:15:54.284807Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:54.284807Z digest=sha256:9a97172feb15f3b5cb7329fbe0b792acad9786e3e50d6a32eddbce3beaec4ce4

Observation 3ed71f43-c9bd-43ed-8116-f3878f7e44f8 · outbound

This paper cites Building a question answering test collection.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Building a question answering test collection

Reference 55

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:54.350966Z digest=sha256:8ec97dd031d4e9e140eaeb61f79997be682f42ed334bf9b668a2d78ca38f2d6d

Observation d07643eb-e23f-43a1-92f3-b7ee68bb1db9 · outbound

This paper cites Efficient fine-tuning of bert models on the edge.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Efficient fine-tuning of bert models on the edge

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:15:58.080872Z

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=2026-08-07T13:15:54.421944Z digest=sha256:d09348edfc32840b0f88e14717fbb61a534a8baef4cc3bbea3541ef288c73332

Observation f797033a-c2e2-4c82-b445-870a8f2213c8 · outbound

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

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Glue: A multi-task benchmark and analysis platform for natural language understanding

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:15:57.964986Z

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=2026-08-07T13:15:54.539883Z digest=sha256:9ecfa45c13dc07af2e7b5582b407d6cb3e5798c7505f56cfe51149f9c4458194

Observation f507dc7a-600f-45f1-afb5-383a323f12c9 · outbound

This paper cites Adversarial GLUE: A multi-task benchmark for robustness evaluation of language models.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Adversarial GLUE: A multi-task benchmark for robustness evaluation of language models

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:15:57.854289Z

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=2026-08-07T13:15:54.602084Z digest=sha256:75a8fb3cef1eec0132fc2e4b0270ebbf473557c6ac84d743f9b7a807cdb71000

Observation 0623adb6-d5df-4bbc-8bcf-2796c5b81227 · outbound

This paper cites On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective

Reference 59

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:54.670865Z digest=sha256:6bb4a66d3c6be49a91a088114bcc1a0392abf6861a750510cf957011cfe88e57

Observation bd5caff5-17a8-47cf-b8cc-6faa2779ee90 · outbound

This paper cites A Survey on the Robustness of Computer Vision Models against Common Corruptions.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning A Survey on the Robustness of Computer Vision Models against Common Corruptions

Reference 60

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no resolver link, observed 2026-08-07T13:15:54.766783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:54.766783Z digest=sha256:ea545f4dc6991d1b21a406aeff33c46585bd4bce0f818781da27e7a8e8e1e86c

Observation 03d6909f-789f-4c19-ae47-e1aa948e8708 · outbound

This paper cites Textflint: Unified multilingual robustness evaluation toolkit for natural language processing.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Textflint: Unified multilingual robustness evaluation toolkit for natural language processing

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:15:57.718317Z

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=2026-08-07T13:15:54.882757Z digest=sha256:16852d11e42fd498d76c4948d1bbd621d2676730ba6d9cccf31a6ebb721dbcf6

Observation 36d36e32-1957-4131-893d-ca55d2ba6f71 · outbound

This paper cites A broad-coverage challenge corpus for sentence understanding through inference.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning A broad-coverage challenge corpus for sentence understanding through inference

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:15:57.546769Z

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=2026-08-07T13:15:54.929259Z digest=sha256:2d6f4dafcc8aa7a9bb483d1c634b29425e2049246be21af9eebc6e99052f6b6e

Observation 2a3bcc9a-4993-49df-b715-032782d0c94a · outbound

This paper cites Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task

Reference 63

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:54.995510Z digest=sha256:2fe498b86586f8389adbd5702c473c6530af667a387ce8521a226d1569681040

Observation 77b91b32-6a72-45d9-a7bb-820cd4b08436 · outbound

This paper cites Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluations.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluations

Reference 64

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no resolver link, observed 2026-08-07T13:15:55.040614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:55.040614Z digest=sha256:06b7f26078723ef4cabc8e477bd02b330f0df6a3a5e7d693cecfcb8ff2caeec9

Observation 388a7cf4-dd0b-4d55-881a-84f3d9a8f983 · outbound

This paper cites Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:15:57.439796Z

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=2026-08-07T13:15:55.142722Z digest=sha256:9663af05c2b9a4dce721809b2a487b881c3f2edd81607a8553ad058badbbb476

Observation 6bfe6ea7-c4c8-403f-9c3a-43905a0b7358 · outbound

This paper cites GLM-130B: An Open Bilingual Pre-trained Model.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning GLM-130B: An Open Bilingual Pre-trained Model

Reference 66

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unresolved
no resolver link, observed 2026-08-07T13:15:55.244227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:55.244227Z digest=sha256:97a6329435edb7e2d7a7f2deb3edab3092bc4c7dc6a5f4ff183bb4f504c47c54

Observation aee6f738-f1b5-4a74-aee6-9bf9f458524d · outbound

This paper cites Openattack: An open-source textual adversarial attack toolkit.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Openattack: An open-source textual adversarial attack toolkit

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:15:57.324635Z

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=2026-08-07T13:15:55.295070Z digest=sha256:56f7d98075230661dda69298833eb687e81aae61aa572165f18eb6293d9f4ac9

Observation 31379478-576f-4554-9bc8-056bbae3760f · outbound

This paper cites Adaptive budget allocation for parameter-efficient fine-tuning.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Adaptive budget allocation for parameter-efficient fine-tuning

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:15:57.095799Z

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=2026-08-07T13:15:55.360370Z digest=sha256:da2a7a5c7d49bc81ea377063ecf64f2a25cd6262afd5adfb0bbd7876fb290b1d

Observation 1a149608-d37f-42d6-bac8-9f8d702474c8 · outbound

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

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning OPT: Open Pre-trained Transformer Language Models

Reference 69

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unresolved
no resolver link, observed 2026-08-07T13:15:55.462049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:55.462049Z digest=sha256:9c33d2d646a7890cd1969137cf3bfb09175d9c3e0d0ed904634a5211ca2aa33e

Observation 6e772163-ad6c-4050-bad6-2d498d276e61 · outbound

This paper cites Character-level convolutional networks for text classification.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Character-level convolutional networks for text classification

Reference 70

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unresolved
no resolver link, observed 2026-08-07T13:15:55.536142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:55.536142Z digest=sha256:fa16f8a8a20d56ffbe0fbb4fbd87b078fda3b6f33e43fc98f02005f23b6b8c8e

Observation 2d648484-6cee-4fd4-a42e-a8c25732a4a9 · outbound

This paper cites Towards adaptive prefix tuning for parameter-efficient language model fine-tuning.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Towards adaptive prefix tuning for parameter-efficient language model fine-tuning

Reference 71

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verified fuzzy
raw_fallback, observed 2026-08-07T13:15:56.771479Z

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.

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Observation 6d651174-1ff7-40e9-af12-90f93b9bb3e6 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Judging llm-as-a-judge with mt-bench and chatbot arena

Reference 72

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unresolved
no resolver link, observed 2026-08-07T13:15:55.677885Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation f4fd0f2d-302f-4567-8054-18a397a81b32 · outbound

This paper cites Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 73

Resolution
unresolved
no resolver link, observed 2026-08-07T13:15:55.747677Z

Source-reported events for the cited work

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Observation 1f3d2782-3fef-4ef9-acbe-fb7db3bb5404 · outbound

This paper cites PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts

Reference 74

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unresolved
no resolver link, observed 2026-08-07T13:15:55.842329Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 6d3914cc-c6b7-47ae-a059-7d79700c3a30 · outbound

This paper cites URL https://openreview.net/forum?id=nZeVKeeFYf9.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning URL https://openreview.net/forum?id=nZeVKeeFYf9

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-07T13:15:39.056700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Pith citing papers

Observation 1f192a19-ffbd-4c23-b1f0-3c54320dfc57 · inbound

SMoA: Spectrum Modulation Adapter for Parameter-Efficient Fine-Tuning cites this paper.

SMoA: Spectrum Modulation Adapter for Parameter-Efficient Fine-Tuning Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning

Reference 31

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verified exact
arxiv_id, observed 2026-05-21T06:03:59.421593Z

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.

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Observation fc37002a-ba2f-4591-a0a7-15231d2b08c6 · inbound

ChunkFT: Byte-Streamed Optimization for Memory-Efficient Full Fine-Tuning cites this paper.

ChunkFT: Byte-Streamed Optimization for Memory-Efficient Full Fine-Tuning Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-21T05:49:41.053697Z

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.

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