Paper Citation Record · LEDGER

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

As of 8 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 2026-08-08T06:32:00.761636+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
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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:03.836514Z

Source-reported events for the cited work

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:03.609223Z

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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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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:03.097240Z

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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

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T13:15:37.186380Z

Source-reported events for the cited work

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T13:15:37.363511Z

Source-reported events for the cited work

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T13:15:37.522359Z

Source-reported events for the cited work

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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

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:02.761982Z

Source-reported events for the cited work

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:02.567927Z

Source-reported events for the cited work

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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-reported events for the cited work

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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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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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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:02.295718Z

Source-reported events for the cited work

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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 2026-08-08T06:32:00.761636+00:00.

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

Resolution: unresolved; no resolver link, observed 2026-08-07T13:15:38.680064Z

Source-reported events for the cited work

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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

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:15:56.316577Z

Source-reported events for the cited work

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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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Source-reported events for the cited work

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:01.805125Z

Source-reported events for the cited work

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:01.621151Z

Source-reported events for the cited work

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:01.366464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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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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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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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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:01.097881Z

Source-reported events for the cited work

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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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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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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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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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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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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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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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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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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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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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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

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

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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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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

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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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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

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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

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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

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

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

Reference 44

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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

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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

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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

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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

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

Source-reported events for the cited work

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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

Resolution: 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 2026-08-08T06:32:00.761636+00:00.

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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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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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

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

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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

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

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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

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

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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: unresolved; no resolver link, observed 2026-08-07T13:15:54.350966Z

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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

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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

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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

Resolution: unresolved; no resolver link, observed 2026-08-07T13:15:54.670865Z

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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

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

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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

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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 2026-08-08T06:32:00.761636+00:00.

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

Resolution: unresolved; no resolver link, observed 2026-08-07T13:15:54.995510Z

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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

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

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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 2026-08-08T06:32:00.761636+00:00.

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

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

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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 2026-08-08T06:32:00.761636+00:00.

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

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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

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

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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

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

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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

Resolution: 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 2026-08-08T06:32:00.761636+00:00.

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

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

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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

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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

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

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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

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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

Resolution: 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 2026-08-08T06:32:00.761636+00:00.

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

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.