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Paper Citation Record · LEDGER

Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 27 inbound Pith citation observations for arXiv:2203.06904.

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

pith.paper-citation-record.v1
2203.06904 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 27 of 27 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:59:41.615381Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T04:07:36.843758Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 3d2d8b35-1dff-40bc-8d22-718cad24271a · inbound

LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model cites this paper.

LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 12

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verified exact
arxiv_id, observed 2026-05-15T08:41:04.794104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-15T08:41:04.743886Z digest=sha256:642248a0e2ade2344edf28fb4f2eab8cb4792b7f3289db0a66d94bf3478df402

Observation 9c58cbcd-becb-45ba-9000-8b79233b7f35 · inbound

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

GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 11

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arxiv_id, observed 2026-05-16T23:51:50.295166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-16T23:51:50.163520Z digest=sha256:6da76eb98727b6f5b70291d19801f6fbffb1eed13c691c2a72daede097b7c66f

Observation 3d4d83ba-78bb-467c-8921-669f0845d63e · inbound

A Survey on the Memory Mechanism of Large Language Model based Agents cites this paper.

A Survey on the Memory Mechanism of Large Language Model based Agents Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 81

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arxiv_id, observed 2026-05-15T07:21:39.949513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-15T07:21:39.440092Z digest=sha256:e50521ecb1c29b533979ed2d3af6388ff01e6282f70323d628730d86ba4f1be5

Observation 33ad3137-b31c-4fa1-898f-fbada4110ecf · inbound

A Survey on Large Language Models for Code Generation cites this paper.

A Survey on Large Language Models for Code Generation Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 67

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verified exact
arxiv_id, observed 2026-05-13T20:18:06.720369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-13T20:18:06.304134Z digest=sha256:7804b06f4dd221d1910626233b5b12c572998887d41f4f2dc57ca40d6f34c601

Observation dffb9ab2-0397-4a0a-a3f2-46e14e130260 · inbound

LLaVA-Video: Video Instruction Tuning With Synthetic Data cites this paper.

LLaVA-Video: Video Instruction Tuning With Synthetic Data Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 133

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arxiv_id, observed 2026-05-10T23:20:32.777912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-10T23:20:32.330351Z digest=sha256:6e07d97712816d8e1712ba17579f81bcb51c83dc71a045544edf02ac5d6baf7b

Observation fb5490ed-9f6c-4e42-9580-738bc5d063d4 · inbound

KBAlign: Efficient Self Adaptation on Specific Knowledge Bases cites this paper.

KBAlign: Efficient Self Adaptation on Specific Knowledge Bases Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 5

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no resolver link, observed 2026-08-12T14:59:24.081593Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T14:59:24.081593Z digest=sha256:6467543259e62ab6d050aaf729714036d56243cf3f6e3ac940c8eedae498d9f2

Observation e721d176-9bc2-4b5e-b41d-f7747bd6b2be · inbound

Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency cites this paper.

Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 13

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no resolver link, observed 2026-08-12T13:07:04.168998Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T13:07:04.168998Z digest=sha256:51a4728e2ca2552ccb03774aede1317d78cc1f5a4e48c87801b13aa42c3d1218

Observation fce0980d-f86b-42a7-9cf3-6ef7a5ba5a3f · inbound

KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language Models cites this paper.

KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language Models Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 76

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no resolver link, observed 2026-08-11T20:08:27.915161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T20:08:27.915161Z digest=sha256:57e4c5a0d5a120d1f3fb10d8a498856f8c49cef2742b5a2af3a6aa09528a2c8d

Observation fbfbe76d-d5e2-499e-8d6c-6e3bcf9faf50 · inbound

IGC: Integrating a Gated Calculator into an LLM to Solve Arithmetic Tasks Reliably and Efficiently cites this paper.

IGC: Integrating a Gated Calculator into an LLM to Solve Arithmetic Tasks Reliably and Efficiently Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 10

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no resolver link, observed 2026-08-10T22:49:57.603533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:49:57.603533Z digest=sha256:46559cac84d303803b4d293abcb164c8bb65c85e20ea7744397d627e6bd3b4e7

Observation 7b6c4ea3-6c0f-41f7-83b5-76ff94731a89 · inbound

PARA: Parameter-Efficient Fine-tuning with Prompt Aware Representation Adjustment cites this paper.

PARA: Parameter-Efficient Fine-tuning with Prompt Aware Representation Adjustment Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 9

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no resolver link, observed 2026-08-09T16:56:09.381937Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T16:56:09.381937Z digest=sha256:aeb6abbc322e1c2c77bd5a5a2d86c69eadcb18659279a82ce67df8a1396dfcf4

Observation d13b770b-0a2d-47ca-9ada-77c6ee0c4afe · inbound

IRepair: An Intent-Aware Approach to Repair Data-Driven Errors in Large Language Models cites this paper.

IRepair: An Intent-Aware Approach to Repair Data-Driven Errors in Large Language Models Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 2020

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no resolver link, observed 2026-08-08T13:57:16.436527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T13:57:16.436527Z digest=sha256:d91d12e6adc1593c43b265436b1e2523bbc50894ee52192bbdbc817bca3a7df0

Observation e99468f7-e6a1-4d65-9731-cc93f17ad700 · inbound

RepCali: High Efficient Fine-tuning Via Representation Calibration in Latent Space for Pre-trained Language Models cites this paper.

RepCali: High Efficient Fine-tuning Via Representation Calibration in Latent Space for Pre-trained Language Models Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 14

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no resolver link, observed 2026-08-15T21:59:41.615381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:59:41.615381Z digest=sha256:2e53a62a8f37d813c941a82ca7eb4ceda3c9885b23c6ef74e9fe127db76a45d0

Observation 2cd2324b-9b75-41ba-868f-85bd6457a73c · inbound

PMA: Towards Parameter-Efficient Point Cloud Understanding via Point Mamba Adapter cites this paper.

PMA: Towards Parameter-Efficient Point Cloud Understanding via Point Mamba Adapter Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 4

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no resolver link, observed 2026-08-07T13:47:16.628672Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:47:16.628672Z digest=sha256:4985514fa49d2dd7506ce9bde312bea86f6fdffd45a0b7541f53f893be4478e3

Observation 1f2f099d-e45e-498f-978d-0f5dd9861fbe · inbound

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation cites this paper.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 15

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no resolver link, observed 2026-08-07T01:03:38.175326Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T01:03:38.175326Z digest=sha256:c5f914b76433d921fd1b496233ede3535727c5d94de3a1dc3ab4b236da03f01d

Observation abeddf7b-7e53-4746-9c12-1baa38dae98f · inbound

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters cites this paper.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 5

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no resolver link, observed 2026-08-07T00:28:39.344281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:28:39.344281Z digest=sha256:a5061eb08c3c8c38389a442063b67a6dbaeb12dfa97d048cdcc335cf5de10b42

Observation 67e36b4f-4bfb-4796-94a9-3423ad939ee1 · inbound

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs cites this paper.

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 2020

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no resolver link, observed 2026-08-06T20:53:28.445295Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:53:28.445295Z digest=sha256:3f5200df1d0ed47dfac119914a361efc3019a2bdb3e804a101387af38216d568

Observation 2cffd4b2-6780-4103-aa7f-85eca601f8e6 · inbound

Mono-InternVL-1.5: Towards Cheaper and Faster Monolithic Multimodal Large Language Models cites this paper.

Mono-InternVL-1.5: Towards Cheaper and Faster Monolithic Multimodal Large Language Models Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 14

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unresolved
no resolver link, observed 2026-08-06T16:49:56.785510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:49:56.785510Z digest=sha256:2b4957eefb99cb7bf222cbd7ce7657541401d6983e274e7123145c3e19877f0b

Observation 2a7c29da-8892-4103-afb3-afefb434dbef · inbound

HydraOpt: Navigating the Efficiency-Performance Trade-off of Adapter Merging cites this paper.

HydraOpt: Navigating the Efficiency-Performance Trade-off of Adapter Merging Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 13

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no resolver link, observed 2026-08-15T18:22:34.608560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:22:34.608560Z digest=sha256:b493a4817613b5223b29de8a46cc9d42be94307500297029df90cf64e3eb539a

Observation 3044a8f2-eb0e-4ce3-8c28-46d32b9645e0 · inbound

Consiglieres in the Shadow: Understanding the Use of Uncensored Large Language Models in Cybercrimes cites this paper.

Consiglieres in the Shadow: Understanding the Use of Uncensored Large Language Models in Cybercrimes Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 124

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no resolver link, observed 2026-08-15T17:24:59.507458Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T17:24:59.507458Z digest=sha256:67326d00d4422c3d89c34081821c163e7494e1274c071ec3d13344fa22559729

Observation 36b48c1e-5e44-40a9-99ee-1f69131f7d3e · inbound

K-Merge: Online Continual Merging of Adapters for On-device Large Language Models cites this paper.

K-Merge: Online Continual Merging of Adapters for On-device Large Language Models Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 11

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no resolver link, observed 2026-08-04T09:47:23.181611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T09:47:23.181611Z digest=sha256:18f326e97932c7a54d2b29c57de032e05e9cdc471f2334c48f99761dbfecab79

Observation 94e8d8f0-c119-4162-8047-6c05e020b722 · inbound

Visual prompting reimagined: The power of the Activation Prompts cites this paper.

Visual prompting reimagined: The power of the Activation Prompts Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 33

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verified exact
arxiv_id, observed 2026-05-10T23:45:53.688717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-10T18:52:10.770345Z digest=sha256:d2e3f460c6c7566fd912de52586c37dc98380b0a39d6224825ee66a4c0dda9a9

Observation 9fa8380c-cf61-44ce-a8c9-915998e5724b · inbound

TLoRA: Task-aware Low Rank Adaptation of Large Language Models cites this paper.

TLoRA: Task-aware Low Rank Adaptation of Large Language Models Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 52

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arxiv_id, observed 2026-05-10T09:48:48.118759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-10T05:07:10.885133Z digest=sha256:bbd347341bfc344b388de5a87059aec437816d89a24ca057ff0ec8232dfc1a22

Observation 3e53810a-03a4-46cd-8ceb-505435c7e206 · inbound

Beyond LoRA vs. Full Fine-Tuning: Gradient-Guided Optimizer Routing for LLM Adaptation cites this paper.

Beyond LoRA vs. Full Fine-Tuning: Gradient-Guided Optimizer Routing for LLM Adaptation Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 7

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verified exact
arxiv_id, observed 2026-05-11T04:40:58.663635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-11T01:10:16.768269Z digest=sha256:0b5761e182e0ac93180597b2ba78c2d3dd0dcd2592f0829498d5e7cbc5345979

Observation cdb7376a-4ea3-4307-9d07-fdce6a8a9b3a · inbound

Beyond LoRA vs. Full Fine-Tuning: Gradient-Guided Optimizer Routing for LLM Adaptation cites this paper.

Beyond LoRA vs. Full Fine-Tuning: Gradient-Guided Optimizer Routing for LLM Adaptation Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 7

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verified exact
arxiv_id, observed 2026-05-20T23:53:51.976530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-20T23:49:29.023187Z digest=sha256:3abaaf106fe70af6393fb98814b777feee1f9d96007b9b90da595b9bef733a46

Observation 212b0dda-f5c0-45a6-9b4b-2be0b3596628 · inbound

Soft Specialists: $\alpha$-R\'enyi Ensembles for Uncertainty-Aware LLM Post-Training cites this paper.

Soft Specialists: $\alpha$-R\'enyi Ensembles for Uncertainty-Aware LLM Post-Training Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 25

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verified exact
arxiv_id, observed 2026-06-29T15:23:32.966360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-29T15:14:28.128331Z digest=sha256:682318777ed491abcf1bedaf4ef792f4912773fd36a7446d2ae22139c5ad62a1

Observation ee65e143-de76-4677-877d-1d7f50175b41 · inbound

Unifying Data, Memory, and Compute Efficiency in LLM training: A Survey cites this paper.

Unifying Data, Memory, and Compute Efficiency in LLM training: A Survey Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 49

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verified exact
arxiv_id, observed 2026-07-03T04:07:36.845177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-27T14:12:14.785572Z digest=sha256:aef20162b752d7025b23ee54e6ebfce20d00b0a2a5e36906fe15a7cdd020db03

Observation 26adb54c-cd6b-459f-9d31-3e65b6c3fd3a · inbound

Z-PEFT: Zero-shot Backdoor Detection in Parameter-Efficient Fine-Tuning via Canonical Spectral Signatures cites this paper.

Z-PEFT: Zero-shot Backdoor Detection in Parameter-Efficient Fine-Tuning via Canonical Spectral Signatures Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 4

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no resolver link, observed 2026-08-04T10:14:32.903421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T10:14:32.903421Z digest=sha256:0c3f86cc3f35e1bd3ebbade21d95c111e4b2fd60aa521ce0f0a8ac3d84c31e27