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Paper Citation Record · LEDGER

Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 53 inbound Pith citation observations for arXiv:2312.12148.

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

pith.paper-citation-record.v1
2312.12148 v1

Coverage vector

measured 0 of 0 reference resolution

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Source: paper_references, paper_reference_links

measured 53 of 53 standing notices

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

measured 53 of 53 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T22:54:24.630078Z

measured 0 of 1 external citation measurements

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Source: arxiv_reference, observed 2026-07-03T03:47:35.347672Z

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Outbound references

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Pith citing papers

Observation 93c8af22-69e1-4255-adba-6fdbe8f089a0 · inbound

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

Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 26

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arxiv_id, observed 2026-05-13T11:32:36.915841Z

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

source=pdf_text observed=2026-05-13T11:32:36.738536Z digest=sha256:f1ebb393dcb38050979ce54f7babd11694463d6a203d42a3aa1219d7c20d6f28

Observation 7f30d6cc-cde2-4c91-a720-68251970c128 · 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 Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 29

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arxiv_id, observed 2026-05-15T07:21:39.762520Z

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

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Observation 8b4c0152-f866-4537-9dee-12eac021bffc · inbound

Task Prompt Vectors: Effective Initialization through Multi-Task Soft-Prompt Transfer cites this paper.

Task Prompt Vectors: Effective Initialization through Multi-Task Soft-Prompt Transfer Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 51

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arxiv_id, observed 2026-05-23T22:13:30.164342Z

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

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Observation 5303f824-054c-48ed-b6ed-206fd98aeb9d · inbound

Foundational Models for 3D Point Clouds: A Survey and Outlook cites this paper.

Foundational Models for 3D Point Clouds: A Survey and Outlook Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 92

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source=pdf_text observed=2026-08-09T22:54:24.630078Z digest=sha256:f5761366de268264de0cc8e3cec08be279eb61651d71003eb7d6e7b5c3a08f13

Observation b9aa4819-6fd6-4ff6-ac46-ef946d75e354 · inbound

Parameter Efficient Fine-Tuning of Segment Anything Model for Biomedical Imaging cites this paper.

Parameter Efficient Fine-Tuning of Segment Anything Model for Biomedical Imaging Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 36

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Observation 647b4678-9837-4bb3-947a-1a1e691f536e · inbound

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective cites this paper.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 15

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Observation f313d2bb-3924-4cd5-9de0-7416731d7456 · inbound

Verbalized Bayesian Persuasion cites this paper.

Verbalized Bayesian Persuasion Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 118

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Observation f4bb937e-9f79-4d3f-a555-88da28b9a294 · inbound

LoRA-TTT: Low-Rank Test-Time Training for Vision-Language Models cites this paper.

LoRA-TTT: Low-Rank Test-Time Training for Vision-Language Models Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 64

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Observation 4873e568-70d5-42e2-87b6-52d6c4767a59 · inbound

Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation cites this paper.

Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 112

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arxiv_id, observed 2026-05-22T21:42:11.390393Z

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

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Observation 56890d57-b668-459e-b6a2-80df874c47dc · inbound

MSA at BEA 2025 Shared Task: Disagreement-Aware Instruction Tuning for Multi-Dimensional Evaluation of LLMs as Math Tutors cites this paper.

MSA at BEA 2025 Shared Task: Disagreement-Aware Instruction Tuning for Multi-Dimensional Evaluation of LLMs as Math Tutors Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 23

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Observation 40ab8fc6-b8fd-4281-a614-6c42bf010126 · inbound

Cuff-KT: Tackling Learners' Real-time Learning Pattern Adjustment via Tuning-Free Knowledge State Guided Model Updating cites this paper.

Cuff-KT: Tackling Learners' Real-time Learning Pattern Adjustment via Tuning-Free Knowledge State Guided Model Updating Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 52

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Observation 763f883e-1e18-4684-b96c-74b4d5644a19 · inbound

Schema as Parameterized Tools for Universal Information Extraction cites this paper.

Schema as Parameterized Tools for Universal Information Extraction Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 45

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Observation 2fca282f-14e3-4cdd-adba-239998c7078a · inbound

Gradient-Based Model Fingerprinting for LLM Similarity Detection and Family Classification cites this paper.

Gradient-Based Model Fingerprinting for LLM Similarity Detection and Family Classification Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 19

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Observation f8a668b0-9d71-4fde-8766-903060a4af11 · inbound

Continual Learning in Vision-Language Models via Aligned Model Merging cites this paper.

Continual Learning in Vision-Language Models via Aligned Model Merging Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 18

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Observation dcbea057-522d-4b65-b94b-1ac9157c776c · inbound

Zero-Shot Adaptation of Parameter-Efficient Fine-Tuning in Diffusion Models cites this paper.

Zero-Shot Adaptation of Parameter-Efficient Fine-Tuning in Diffusion Models Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 12

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Observation 29f40be2-71cb-4a0b-99b4-04b7d9aa85db · inbound

The impact of fine tuning in LLaMA on hallucinations for named entity extraction in legal documentation cites this paper.

The impact of fine tuning in LLaMA on hallucinations for named entity extraction in legal documentation Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 18

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Observation e22cee96-9e5c-420f-825a-ebdb89da6b06 · inbound

Kvasir-VQA-x1: A Multimodal Dataset for Medical Reasoning and Robust MedVQA in Gastrointestinal Endoscopy cites this paper.

Kvasir-VQA-x1: A Multimodal Dataset for Medical Reasoning and Robust MedVQA in Gastrointestinal Endoscopy Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 48

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Observation 29533b44-0798-43b0-b81c-52bb5f91ebf1 · inbound

SOFT: Selective Data Obfuscation for Protecting LLM Fine-tuning against Membership Inference Attacks cites this paper.

SOFT: Selective Data Obfuscation for Protecting LLM Fine-tuning against Membership Inference Attacks Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 88

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Observation d7e353ef-eb8f-4b70-93ff-5c596118c4bd · inbound

Meta-Learning and Synthetic Data for Automated Pretraining and Finetuning cites this paper.

Meta-Learning and Synthetic Data for Automated Pretraining and Finetuning Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 7317

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Observation 5bb92f7f-cf35-491a-a69c-cf8f49e6a57c · inbound

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence cites this paper.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 46

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Observation b71990e6-68f0-4e20-9c57-5b69d7972036 · inbound

LoX: Low-Rank Extrapolation Robustifies LLM Safety Against Fine-tuning cites this paper.

LoX: Low-Rank Extrapolation Robustifies LLM Safety Against Fine-tuning Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 2017

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Observation aefcb902-e75e-490c-b9a3-fc737ce03fa1 · inbound

Reconstructing Biological Pathways by Applying Selective Incremental Learning to (Very) Small Language Models cites this paper.

Reconstructing Biological Pathways by Applying Selective Incremental Learning to (Very) Small Language Models Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 36

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Observation bc3982f3-a902-4b3d-a212-b6231ec366f9 · inbound

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation cites this paper.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 64

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Observation 23f6ae84-93c2-48c6-bc61-4786f43fd5f9 · inbound

Fine-tuning on simulated data outperforms prompting for agent tone of voice cites this paper.

Fine-tuning on simulated data outperforms prompting for agent tone of voice Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 19

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Observation 4cc98494-dc94-4d14-93b6-57ebe08457f8 · inbound

Defending Against Prompt Injection With a Few DefensiveTokens cites this paper.

Defending Against Prompt Injection With a Few DefensiveTokens Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 44

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Observation 17def1db-fb88-4ee0-b33e-bf52a3515e17 · inbound

Enhancing RLHF with Human Gaze Modeling cites this paper.

Enhancing RLHF with Human Gaze Modeling Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 39

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Observation 0f2ced8f-88ea-4c83-9a8a-2b6a08e53a1b · inbound

Implementing Adaptations for Vision AutoRegressive Model cites this paper.

Implementing Adaptations for Vision AutoRegressive Model Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 34

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Observation 3d14656f-e141-433c-aef2-9f753803ecbb · inbound

GRID: Scaling Task-Agnostic Inference in Continual Prompt Tuning cites this paper.

GRID: Scaling Task-Agnostic Inference in Continual Prompt Tuning Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 48

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source=arxiv_source observed=2026-08-06T15:55:32.848941Z digest=sha256:d1fd2a34dd903b903000125180a55a43def87ebd475e7734e9347087bf6d8c59

Observation aaedcf3a-5e26-46f5-82e8-5d6e911c0355 · inbound

The Impact of Fine-tuning Large Language Models on Automated Program Repair cites this paper.

The Impact of Fine-tuning Large Language Models on Automated Program Repair Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 12

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Observation 3f4f5ea5-b5d7-41e0-b717-22cd8973819b · inbound

CIgrate: Automating CI Service Migration with Large Language Models cites this paper.

CIgrate: Automating CI Service Migration with Large Language Models Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 38

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Observation f97ba744-af0d-4f37-8d7b-63281d83f1be · inbound

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments cites this paper.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 32

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Observation dc2c7e43-7dc3-4870-86cb-93a10894c670 · inbound

Sealing The Backdoor: Unlearning Adversarial Text Triggers In Diffusion Models Using Knowledge Distillation cites this paper.

Sealing The Backdoor: Unlearning Adversarial Text Triggers In Diffusion Models Using Knowledge Distillation Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 110

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Observation b9820517-4e69-4ed2-8b76-c3d73ac9ccbe · inbound

Why Do MLLMs Struggle with Spatial Understanding? A Systematic Analysis from Data to Architecture cites this paper.

Why Do MLLMs Struggle with Spatial Understanding? A Systematic Analysis from Data to Architecture Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 41

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source=arxiv_source observed=2026-08-05T11:40:24.298029Z digest=sha256:d6e7fba7b679b29871eea689060a85e715ce3c35872524258a204668bf409f25

Observation e7f9361a-f08e-412b-9f5c-04356b28e9cc · inbound

DP-FedLoRA: Privacy-Enhanced Federated Fine-Tuning for On-Device Large Language Models cites this paper.

DP-FedLoRA: Privacy-Enhanced Federated Fine-Tuning for On-Device Large Language Models Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 13

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source=pdf_text observed=2026-08-04T19:48:47.406634Z digest=sha256:4622fc337c4e3cf310c97905d8751706cf3d9ded0d86431fd7e516f51563de9a

Observation 88bc9182-1261-47d2-9425-e25802af6ac5 · inbound

Echoes of Human Malice in Agents: Benchmarking LLMs for Multi-Turn Online Harassment Attacks cites this paper.

Echoes of Human Malice in Agents: Benchmarking LLMs for Multi-Turn Online Harassment Attacks Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 42

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unresolved
no resolver link, observed 2026-08-04T09:40:47.045864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T09:40:47.045864Z digest=sha256:0f3f53b2ee57e0889615b625d7df10afe8b73936b496e9d64155b0dd35c983cd

Observation eac65aa3-18dc-44ae-a7ef-316ff559716c · inbound

Key and Value Weights Are Probably All You Need: On the Necessity of the Query, Key, Value weight Triplet in Self-Attention Transformers cites this paper.

Key and Value Weights Are Probably All You Need: On the Necessity of the Query, Key, Value weight Triplet in Self-Attention Transformers Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-18T03:40:50.535093Z

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=2026-05-18T03:38:36.932424Z digest=sha256:02ab440dda641273340e9650cce4ad64890a9c791eb5c2d6b3cb525bbbe8778f

Observation 253ea811-83e6-444b-bd18-6660f55f87cc · inbound

Point of Order: Action-Aware LLM Persona Modeling for Data-Grounded Civic Deliberation cites this paper.

Point of Order: Action-Aware LLM Persona Modeling for Data-Grounded Civic Deliberation Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-03T20:57:37.579226Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T20:57:37.579226Z digest=sha256:91fe2d980fe15863f6d48bad7b4e7882bad7e47359359c8415b29731cefb765e

Observation a47bdbe2-464f-4b70-9b1a-23a9b8142b76 · inbound

PEFT-Bench: A Parameter-Efficient Fine-Tuning Methods Benchmark cites this paper.

PEFT-Bench: A Parameter-Efficient Fine-Tuning Methods Benchmark Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 62

Resolution
verified exact
arxiv_id, observed 2026-05-17T05:09:03.680829Z

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=arxiv_source observed=2026-05-17T05:08:42.031800Z digest=sha256:70b4f9929bf1cb1b85174718ac8d7fc10ac1b45710afb23107df867cca6dda5e

Observation 683ff4eb-0f1b-4c42-9b2c-6d1ee48d1535 · inbound

PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models cites this paper.

PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 77

Resolution
verified exact
arxiv_id, observed 2026-05-17T02:38:53.872835Z

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=arxiv_source observed=2026-05-17T02:38:11.118057Z digest=sha256:63acfd6cdd3851c47846168db13fec58d1d98794fc910a1f43fd0db5098bdb91

Observation 38861806-a42b-4c26-b22b-b6f5fe5a7c1d · inbound

Reconstructing Item Characteristic Curves using Fine-Tuned Large Language Models cites this paper.

Reconstructing Item Characteristic Curves using Fine-Tuned Large Language Models Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-03T12:36:13.304483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T12:36:13.304483Z digest=sha256:d9a8fef3110d054e84a0112b4bcddf87e44d0ad04d2de413b9ede295a6cab1be

Observation 6ce0219b-aece-4c36-9dd3-67acac433928 · inbound

Do Fine-Tuned LLMs Understand Vulnerabilities? An Investigation into the Semantic Trap cites this paper.

Do Fine-Tuned LLMs Understand Vulnerabilities? An Investigation into the Semantic Trap Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 47

Resolution
verified exact
arxiv_id, observed 2026-05-22T11:41:30.156233Z

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=2026-05-22T11:38:16.149523Z digest=sha256:5ed287e6c95b62c021c2adf7a33a46ffec741c07a420bbb01c5b2700a23b1633

Observation a07ded3c-92f2-4997-824f-05dec0eebfa2 · inbound

When pre-training hurts LoRA fine-tuning: a dynamical analysis via single-index models cites this paper.

When pre-training hurts LoRA fine-tuning: a dynamical analysis via single-index models Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 2025

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malformed identifier
no resolver link, observed 2026-08-03T05:21:47.873597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T05:21:47.873597Z digest=sha256:6217cb2324d2e788da52864a0c8c31a525e3d0eafbc1d1b1c861b5d5fe7b93ee

Observation 6e439f72-6072-4904-b199-8d9aba5a3d91 · inbound

HeiSD: Hybrid Speculative Decoding for Embodied Vision-Language-Action Models with Kinematic Awareness cites this paper.

HeiSD: Hybrid Speculative Decoding for Embodied Vision-Language-Action Models with Kinematic Awareness Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 36

Resolution
verified exact
arxiv_id, observed 2026-05-15T09:19:54.382687Z

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=2026-05-15T09:15:50.963123Z digest=sha256:bc052d1a5457aefc62218c55aaa2dad9e4da8ad74ffed2e5476330f8d6c47ae4

Observation de62f91a-9433-4d3f-8308-28f3741884b0 · inbound

Task-Centric Personalized Federated Fine-Tuning of Language Models cites this paper.

Task-Centric Personalized Federated Fine-Tuning of Language Models Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 3

Resolution
metadata mismatch
arxiv_id, observed 2026-05-14T21:27:59.623525Z

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=2026-05-14T21:26:17.264779Z digest=sha256:8c8b4e6662d73ba203c851b019588bfa3769896f84581ae3cfc6d2450a2ba41a

Observation eb76bd4d-9344-4f3f-8403-c6a281a4a64d · inbound

Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates cites this paper.

Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 40

Resolution
verified exact
arxiv_id, observed 2026-05-10T22:35:52.828961Z

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=2026-05-10T19:40:25.318694Z digest=sha256:ddead5d360f5aebedc0e10af8d7d7aaa1bdb9c11e8b6f89dd9c86a485f1e243f

Observation 7a465ce2-b623-45b4-b059-cb8e19570d1c · inbound

Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates cites this paper.

Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 40

Resolution
unresolved
no resolver link, observed 2026-07-13T09:41:56.414542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T09:41:56.414542Z digest=sha256:7ee09355f1ba067672d114662b2d5c13adc0adfaf5676f48a4f3e593da47120e

Observation c6d07920-33fb-4066-bfb5-f3cf1eed1349 · inbound

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

TLoRA: Task-aware Low Rank Adaptation of Large Language Models Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-05-10T09:48:48.145168Z

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=arxiv_source observed=2026-05-10T05:07:10.885133Z digest=sha256:c1fc16c1f2207f86ec4270bd9439b46c1ca485aa8d6228dcc73995e97b862f8f

Observation b271ee76-df26-4ee8-a13e-e79509bb87fa · inbound

SAMoRA: Semantic-Aware Mixture of LoRA Experts for Task-Adaptive Learning cites this paper.

SAMoRA: Semantic-Aware Mixture of LoRA Experts for Task-Adaptive Learning Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 46

Resolution
verified exact
arxiv_id, observed 2026-05-11T13:01:24.964905Z

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=arxiv_source observed=2026-05-10T02:26:55.896058Z digest=sha256:011f62b29482dac82177d51f1b5360b0ebd860411a9191b219c10e2b3651d493

Observation 131785b0-4dcf-4813-b30d-279969a4eaac · inbound

Leveraging LLMs for Multi-File DSL Code Generation: An Industrial Case Study cites this paper.

Leveraging LLMs for Multi-File DSL Code Generation: An Industrial Case Study Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 46

Resolution
verified exact
arxiv_id, observed 2026-05-11T22:26:13.399730Z

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=2026-05-08T02:49:14.533263Z digest=sha256:51b2e4d04141c7fe743f5c41659d45f0a69a28930abe497bb4d110f69b3f3a6c

Observation c8aed598-4e12-4176-9032-19560dcdf8fd · inbound

World2VLM: Distilling World Model Imagination into VLMs for Dynamic Spatial Reasoning cites this paper.

World2VLM: Distilling World Model Imagination into VLMs for Dynamic Spatial Reasoning Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 54

Resolution
verified exact
arxiv_id, observed 2026-05-12T09:46:27.001122Z

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=2026-05-07T09:20:36.243949Z digest=sha256:431fe03c071a58f51c811c8fdfa7d75fa28d2641093335c0187472bfec80bfe3

Observation a431cc78-1c6c-45f1-a8ea-3e9f61487f07 · 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 Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 99

Resolution
verified exact
arxiv_id, observed 2026-06-29T15:23:32.957858Z

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=2026-06-29T15:14:28.128331Z digest=sha256:0b108eb903c793e195cb9b13adc632d49cd2d43329db26f9eef6c7479f9d10b2

Observation 59d5ab82-3023-4ea7-a101-a9a01fcaaf8f · inbound

FMplex: Model Virtualization for Serving Extensible Foundation Models cites this paper.

FMplex: Model Virtualization for Serving Extensible Foundation Models Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 80

Resolution
verified exact
arxiv_id, observed 2026-07-03T03:47:35.349218Z

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=2026-06-27T14:50:35.584259Z digest=sha256:994fa5784e990b60c4c93854872647877837d0f33a92548427cec33e38fd73e2

Observation d2eea844-ff0e-42e8-9699-5ff8fa9d8a90 · inbound

Exploring Post-Training Alignment of Small Language Models for Biomedical Data-to-Text Generation: A Case Study of Medication Leaflet cites this paper.

Exploring Post-Training Alignment of Small Language Models for Biomedical Data-to-Text Generation: A Case Study of Medication Leaflet Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-02T05:16:31.177775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T05:16:31.177775Z digest=sha256:09a3cab4048a9e1abd02c530e1c9e4b8392665ce8dc02e60f1559cd49fb87071