P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks | Paper Citation Record

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LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention cites this paper.

LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 54

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Enhancing Chat Language Models by Scaling High-quality Instructional Conversations cites this paper.

Enhancing Chat Language Models by Scaling High-quality Instructional Conversations P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 166

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T17:25:08.152440Z

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EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers cites this paper.

EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 117

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

Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-13T11:32:37.143142Z

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Observation 330dfcd8-7bee-4f6f-a7f0-a9cd139e6242 · inbound

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs cites this paper.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 15

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Observation bbe2c28e-3da7-43d1-a2e4-44a5d9ccbbb7 · inbound

PyGen: A Collaborative Human-AI Approach to Python Package Creation cites this paper.

PyGen: A Collaborative Human-AI Approach to Python Package Creation P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 30

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Observation 3e9008cb-2de5-4ca0-be2f-4e07d8f0f15a · inbound

On the Privacy Risk of In-context Learning cites this paper.

On the Privacy Risk of In-context Learning P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 27

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Observation f76e3399-e5be-474c-901c-99d8e0571c13 · inbound

IterIS: Iterative Inference-Solving Alignment for LoRA Merging cites this paper.

IterIS: Iterative Inference-Solving Alignment for LoRA Merging P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 26

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Observation d6325a51-89a6-43cc-b7c2-608341e63c45 · inbound

An Empirical Study of Vulnerability Detection using Federated Learning cites this paper.

An Empirical Study of Vulnerability Detection using Federated Learning P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 45

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Observation bc76cada-7ad7-4bca-ae0f-5e5f7bfea498 · inbound

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

Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 41

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Sensitive Content Classification in Social Media: A Holistic Resource and Evaluation cites this paper.

Sensitive Content Classification in Social Media: A Holistic Resource and Evaluation P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-12T05:56:44.262943Z

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Observation 86e0f205-3195-415c-aca0-f85ce71a7c58 · inbound

Safety Alignment Backfires: Preventing the Re-emergence of Suppressed Concepts in Fine-tuned Text-to-Image Diffusion Models cites this paper.

Safety Alignment Backfires: Preventing the Re-emergence of Suppressed Concepts in Fine-tuned Text-to-Image Diffusion Models P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 44

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Observation 26df6d92-1de6-4b82-85ae-5d5980b361d3 · inbound

Unified Parameter-Efficient Unlearning for LLMs cites this paper.

Unified Parameter-Efficient Unlearning for LLMs P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 54

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Observation 31109cda-9f8b-4f53-8577-ec11de77d57b · inbound

Prompt as Free Lunch: Enhancing Diversity in Source-Free Cross-domain Few-shot Learning through Semantic-Guided Prompting cites this paper.

Prompt as Free Lunch: Enhancing Diversity in Source-Free Cross-domain Few-shot Learning through Semantic-Guided Prompting P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-12T05:05:12.165770Z

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Observation 5bf7905d-ab5c-4343-9dc3-7e676b797cdd · inbound

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations cites this paper.

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 142

Resolution: unresolved; no resolver link, observed 2026-08-12T04:51:56.238114Z

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Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges cites this paper.

Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-11T22:41:16.599307Z

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Observation c05a419f-5f39-483f-9232-156f1ada8828 · inbound

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization cites this paper.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 22

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Observation 16827e5e-e568-4569-a1bc-bcd382c89441 · inbound

LLMs are Also Effective Embedding Models: An In-depth Overview cites this paper.

LLMs are Also Effective Embedding Models: An In-depth Overview P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-11T13:59:01.760619Z

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Observation d40a94bb-efce-433d-8b19-ed7f2215c323 · inbound

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs cites this paper.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T12:17:56.732578Z

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Observation 986378c6-4c94-4366-b2fb-219c43dc95e7 · inbound

Differentiable Prompt Learning for Vision Language Models cites this paper.

Differentiable Prompt Learning for Vision Language Models P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.088349Z

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Observation 9b32496b-d7be-40f4-be0f-f7c99e42b244 · inbound

Exploring Information Processing in Large Language Models: Insights from Information Bottleneck Theory cites this paper.

Exploring Information Processing in Large Language Models: Insights from Information Bottleneck Theory P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-10T22:43:11.702334Z

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A Survey on Large Language Models with some Insights on their Capabilities and Limitations cites this paper.

A Survey on Large Language Models with some Insights on their Capabilities and Limitations P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 193

Resolution: unresolved; no resolver link, observed 2026-08-10T22:17:56.127452Z

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Observation 069e3b93-c481-46ef-9e89-64ee2033c155 · inbound

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono cites this paper.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-10T21:51:12.157702Z

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Observation 8d8e37cb-3444-42da-bee4-3646679f7ea2 · inbound

KGIF: Optimizing Relation-Aware Recommendations with Knowledge Graph Information Fusion cites this paper.

KGIF: Optimizing Relation-Aware Recommendations with Knowledge Graph Information Fusion P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T21:44:33.906829Z

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Observation da9dc81e-7d42-42cb-abcf-e872de62fe1b · inbound

Enhancing Generalization in Chain of Thought Reasoning for Smaller Models cites this paper.

Enhancing Generalization in Chain of Thought Reasoning for Smaller Models P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 22

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Observation 650efc14-e2ab-44dc-b0f0-b6a3ac978b46 · inbound

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning cites this paper.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 30

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Observation da22df74-cfb0-4e61-813e-2e8ec64339d6 · inbound

Parameter-Efficient Fine-Tuning for Foundation Models cites this paper.

Parameter-Efficient Fine-Tuning for Foundation Models P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 37

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Observation b0ff9be9-71d9-456f-a40a-8d06e0fb6c8e · inbound

FlexiGPT: Pruning and Extending Large Language Models with Low-Rank Weight Sharing cites this paper.

FlexiGPT: Pruning and Extending Large Language Models with Low-Rank Weight Sharing P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 16

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Observation b90e963b-3618-44b8-9275-5b7cd3163ca9 · inbound

Sparse Gradient Compression for Fine-Tuning Large Language Models cites this paper.

Sparse Gradient Compression for Fine-Tuning Large Language Models P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 25

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Observation ed318721-8664-4c3f-be97-4da3dd11195f · inbound

Task-Specific Adaptation with Restricted Model Access cites this paper.

Task-Specific Adaptation with Restricted Model Access P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 24

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Observation 5648458b-0a06-434e-b974-042e1960c98d · inbound

LAST SToP For Modeling Asynchronous Time Series cites this paper.

LAST SToP For Modeling Asynchronous Time Series P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-09T14:02:48.432870Z

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Observation 622509aa-0c86-482b-91bf-20d1cac6ab3d · inbound

On Zero-Initialized Attention: Optimal Prompt and Gating Factor Estimation cites this paper.

On Zero-Initialized Attention: Optimal Prompt and Gating Factor Estimation P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 32

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Observation 72361f5c-6d6c-45d2-8789-4822f0e44581 · inbound

Vision-Language Models for Edge Networks: A Comprehensive Survey cites this paper.

Vision-Language Models for Edge Networks: A Comprehensive Survey P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 69

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Observation 96b9ea31-28f3-49fb-ab1f-a90b353d5728 · inbound

PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore cites this paper.

PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 10

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Observation 6afa0470-86ed-43e0-9d89-d52df7cd75bc · inbound

OSoRA: Output-Dimension and Singular-Value Initialized Low-Rank Adaptation cites this paper.

OSoRA: Output-Dimension and Singular-Value Initialized Low-Rank Adaptation P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 11

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Observation 9c338ff4-93e0-4a9a-86cf-127d4475a1a2 · inbound

Quaff: Quantized Parameter-Efficient Fine-Tuning under Outlier Spatial Stability Hypothesis cites this paper.

Quaff: Quantized Parameter-Efficient Fine-Tuning under Outlier Spatial Stability Hypothesis P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 46

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Observation 8fbdc9b1-e232-4dd3-807e-8d2afa261895 · inbound

Can Multimodal Large Language Models Understand Spatial Relations? cites this paper.

Can Multimodal Large Language Models Understand Spatial Relations? P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 26

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Observation d879fceb-3210-431b-a7d2-189c9cb0e932 · 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 P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 27

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Observation 1dd4391f-a4f8-4116-8c05-ab7c0efc029b · inbound

Privacy-preserving Prompt Personalization in Federated Learning for Multimodal Large Language Models cites this paper.

Privacy-preserving Prompt Personalization in Federated Learning for Multimodal Large Language Models P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 34

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Observation 59ffbfd6-90e6-462a-ac1c-d60ac9294238 · inbound

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers cites this paper.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:56.805456Z

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Observation 63c044d9-1594-43df-a25e-72eaea3b12f8 · inbound

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts cites this paper.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 30

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Observation 2c06e49b-2c25-4601-ae23-3d070352ab43 · inbound

Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection cites this paper.

Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 13

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Observation cf273c16-e974-4c52-8d50-b417d4ebbcf9 · inbound

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations cites this paper.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 77

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Observation 98c13187-fbe6-45f1-a899-603fbd6808fd · inbound

CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the Edge cites this paper.

CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the Edge P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 89

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Observation 41675e70-fa0f-4496-8008-27eb014273b7 · inbound

Beyond the Sentence: A Survey on Context-Aware Machine Translation with Large Language Models cites this paper.

Beyond the Sentence: A Survey on Context-Aware Machine Translation with Large Language Models P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 54

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Observation 00d6884a-389f-47a9-9a4b-a47bbc0c35aa · inbound

PrefixMemory-Tuning: Modernizing Prefix-Tuning by Decoupling the Prefix from Attention cites this paper.

PrefixMemory-Tuning: Modernizing Prefix-Tuning by Decoupling the Prefix from Attention P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-19T09:27:14.633334Z

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Observation 712e8857-7bed-48e4-a9ec-92e57aa3073c · inbound

Test3R: Learning to Reconstruct 3D at Test Time cites this paper.

Test3R: Learning to Reconstruct 3D at Test Time P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 56

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Observation 20309761-7744-4cec-a52f-8d48e61ff41e · inbound

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models cites this paper.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 24

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Observation 6fd8c473-3fda-48ae-81fc-ca8731ef683a · inbound

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs cites this paper.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 19

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Observation 571983aa-256e-4f56-b96c-68a11dfe7ae9 · inbound

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices cites this paper.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T20:58:59.143355Z

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Observation fe6fb1cd-7926-46e0-9556-4034110176e4 · inbound

SI-Agent: An Agentic Framework for Feedback-Driven Generation and Tuning of Human-Readable System Instructions for Large Language Models cites this paper.

SI-Agent: An Agentic Framework for Feedback-Driven Generation and Tuning of Human-Readable System Instructions for Large Language Models P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 16

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 516f1b29-e1f9-4c7b-89f5-87d015388132 · inbound

Animation Needs Attention: A Holistic Approach to Slides Animation Comprehension with Visual-Language Models cites this paper.

Animation Needs Attention: A Holistic Approach to Slides Animation Comprehension with Visual-Language Models P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T20:04:13.722971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23628d81-c06b-4c72-8ecc-2009a00ec602 · inbound

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning cites this paper.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:26.565697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2c48fea-3174-4aff-b175-84f878d74b52 · inbound

CLAImate: AI-Enabled Climate Change Communication through Personalized and Localized Narrative Visualizations cites this paper.

CLAImate: AI-Enabled Climate Change Communication through Personalized and Localized Narrative Visualizations P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T17:09:12.694490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47933b35-c329-4c6f-8bac-e1a69b118de9 · inbound

Adversarial Demonstration Learning for Low-resource NER Using Dual Similarity cites this paper.

Adversarial Demonstration Learning for Low-resource NER Using Dual Similarity P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T18:01:27.980962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65dce568-9abe-404f-9b29-ccb3305d24c5 · inbound

Foundation Models and Transformers for Anomaly Detection: A Survey cites this paper.

Foundation Models and Transformers for Anomaly Detection: A Survey P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:51.587979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f48d25c0-f060-4fb6-8667-026af24bb107 · inbound

Zero-Residual Concept Erasure via Progressive Alignment in Text-to-Image Model cites this paper.

Zero-Residual Concept Erasure via Progressive Alignment in Text-to-Image Model P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-06T00:04:32.371883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79e3a429-aee6-46e7-9d5c-ab126569d40a · inbound

Adversarial Topic-aware Prompt-tuning for Cross-topic Automated Essay Scoring cites this paper.

Adversarial Topic-aware Prompt-tuning for Cross-topic Automated Essay Scoring P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T23:10:14.775430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 246a53c1-09e4-4d34-81bb-321ee383cd53 · inbound

Dual Information Speech Language Models for Emotional Conversations cites this paper.

Dual Information Speech Language Models for Emotional Conversations P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T21:45:22.786563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8806181-bed9-46ad-8da7-907b3f5d7ed7 · inbound

Towards Meta-Cognitive Knowledge Editing for Multimodal LLMs cites this paper.

Towards Meta-Cognitive Knowledge Editing for Multimodal LLMs P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-05T05:12:44.230741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54b3679d-f14a-4f55-9afc-712946621397 · inbound

AI-driven Remote Facial Skin Hydration and TEWL Assessment from Selfie Images: A Systematic Solution cites this paper.

AI-driven Remote Facial Skin Hydration and TEWL Assessment from Selfie Images: A Systematic Solution P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T23:55:38.963496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7a0dd14-83c8-42b5-81f4-48beb1a547bb · inbound

Latent Activation Editing: Inference-Time Refinement of Learned Policies for Safer Multirobot Navigation cites this paper.

Latent Activation Editing: Inference-Time Refinement of Learned Policies for Safer Multirobot Navigation P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T15:18:04.834479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf580ff8-8d8b-4563-89b2-e62c63e54c60 · inbound

MHA-RAG: Improving Efficiency, Accuracy, and Consistency by Encoding Exemplars as Soft Prompts cites this paper.

MHA-RAG: Improving Efficiency, Accuracy, and Consistency by Encoding Exemplars as Soft Prompts P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T11:23:50.599087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c604c66-1b7c-4579-96a5-4f84752a8ab4 · inbound

Meta-cavity Quantum Electrodynamics cites this paper.

Meta-cavity Quantum Electrodynamics P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-15T12:12:51.663341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87fa7bf1-4e7c-4a66-9636-8254a3f9378d · inbound

The nextAI Solution to the NeurIPS 2023 LLM Efficiency Challenge cites this paper.

The nextAI Solution to the NeurIPS 2023 LLM Efficiency Challenge P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:55:59.394134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a358a198-0bf9-4e7f-8106-33dce3a46733 · inbound

Seeing is Believing: Robust Vision-Guided Cross-Modal Prompt Learning under Label Noise cites this paper.

Seeing is Believing: Robust Vision-Guided Cross-Modal Prompt Learning under Label Noise P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:16:02.274020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 007153b7-7c67-43ba-bcd2-955a370607c5 · inbound

Transforming External Knowledge into Triplets for Enhanced Retrieval in RAG of LLMs cites this paper.

Transforming External Knowledge into Triplets for Enhanced Retrieval in RAG of LLMs P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:56:05.573047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 071f4d27-c8d3-4d75-b1bf-096024853ed6 · inbound

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

TLoRA: Task-aware Low Rank Adaptation of Large Language Models P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 26

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T09:48:48.190336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1fc255c9-1f44-499b-ade7-c951b23cbab4 · inbound

FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion cites this paper.

FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 46

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T12:56:06.347783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 14b05ce8-e8a2-436b-a75d-58efe2c1a945 · inbound

PEML: Parameter-efficient Multi-Task Learning with Optimized Continuous Prompts cites this paper.

PEML: Parameter-efficient Multi-Task Learning with Optimized Continuous Prompts P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 84

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:29:48.175330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 891fc892-3191-4915-81bc-8be160aaf4c5 · inbound

Foundation-Preserving Adaptation via Generalized Rayleigh-Quotient Optimization cites this paper.

Foundation-Preserving Adaptation via Generalized Rayleigh-Quotient Optimization P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-06-29T08:43:15.303977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 95d7c5db-79a8-4174-a240-45c1701b45d3 · inbound

Latent Diffusion Pretraining for Crystal Property Prediction cites this paper.

Latent Diffusion Pretraining for Crystal Property Prediction P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 41

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T19:32:34.861716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6005146d-aaa3-47de-aa04-3d9b89a1d15a · inbound

TadA-Bench: A Million-Variant Benchmark for Future-Round Discovery Toward Agentic Protein Engineering cites this paper.

TadA-Bench: A Million-Variant Benchmark for Future-Round Discovery Toward Agentic Protein Engineering P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T20:12:37.866501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5a46bd96-336f-4cc5-ae02-065db55e53c6 · inbound

Unveiling Privacy Risks in Multi-modal Large Language Models: Task-specific Vulnerabilities and Mitigation Challenges cites this paper.

Unveiling Privacy Risks in Multi-modal Large Language Models: Task-specific Vulnerabilities and Mitigation Challenges P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 30

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T01:27:31.059492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9b2c5fce-389d-41ec-bdbd-d40036f4d9f7 · inbound

Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models cites this paper.

Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 187

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T00:27:29.531190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8034e942-66b4-49d8-81f1-632387bb9165 · inbound

Small Data, Big Noise: Adversarial Training for Robust Parameter-Efficient Fine-Tuning cites this paper.

Small Data, Big Noise: Adversarial Training for Robust Parameter-Efficient Fine-Tuning P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T05:17:40.244105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b8e94ae4-4afd-4bd8-8b8a-ad789c82bedd · inbound

SoftSkill: Behavioral Compression for Contextual Adaptation cites this paper.

SoftSkill: Behavioral Compression for Contextual Adaptation P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-04T04:19:34.482854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e24ee796-9c62-4685-8b6b-dafb5b0b2652 · inbound

No Adaptation Without Observation: Observability-Constrained Test-Time Prompt Tuning for LiDAR Semantic Segmentation cites this paper.

No Adaptation Without Observation: Observability-Constrained Test-Time Prompt Tuning for LiDAR Semantic Segmentation P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-07-01T12:55:43.918179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5b3c18af-c98d-4cee-b9db-e189de7098a7 · inbound

Data-Efficient Adaptation of LLMs via Attention Head Reweighting cites this paper.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-02T05:16:36.094425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3bb60b3-0066-496d-9858-6cf0daf00bdc · inbound

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling cites this paper.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 282

Resolution: unresolved; no resolver link, observed 2026-08-02T09:51:03.925992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f440844-4da6-436e-b737-c91059ac5b05 · inbound

GrocLM: Grocery Category Recommendation in E-Commerce with Large Language Models cites this paper.

GrocLM: Grocery Category Recommendation in E-Commerce with Large Language Models P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T12:15:44.688680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers