Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-11T22:41:16.681006Z
Paper Citation Record · LEDGER
As of 13 August 2026, this Paper Citation Record lists 100 of 299 outbound references and 0 inbound Pith citation observations for arXiv:2412.03220.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-11T22:41:16.681006Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
100 of 299 outbound references displayed
External citation measurements
No source-named external measurement is stored.
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Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning
Reference 91
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Observation eacfd047-015a-494b-aa2e-c9f059ce2dc7 · outbound
Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges Prototype-based HyperAdapter for Sample-Efficient Multi-task Tuning
Reference 92
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Observation 813512e1-cfec-4f35-9daf-e5d650e76c64 · outbound
Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges AdapterSoup: Weight Averaging to Improve Generalization of Pretrained Language Models
Reference 93
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Observation 0bdc982a-e757-470b-b333-b8ea50432b9e · outbound
Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges MerA: Merging Pretrained Adapters For Few-Shot Learning
Reference 94
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Observation dce691b8-f18f-4294-b7e6-b9f7fa9d4771 · outbound
Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks
Reference 95
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Observation b67e66eb-8e30-46b8-be79-95256142c204 · outbound
Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges Prefix-Tuning: Optimizing Continuous Prompts for Generation
Reference 96
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Observation a2aa64af-2cac-4ca4-966f-58d0ac4201c8 · outbound
Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges Prefix Propagation: Parameter-Efficient Tuning for Long Sequences
Reference 97
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Observation 54cb2d41-b1e8-4cfd-b900-b284415c9fed · outbound
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
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Observation c29b0258-87af-44c8-a039-369587852d2b · outbound
Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges Towards Adaptive Prefix Tuning for Parameter-Efficient Language Model Fine-tuning
Reference 99
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Observation f01d68d0-c585-41fb-a770-58a43cec40ea · outbound
Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges GPT Understands, Too
Reference 100
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No inbound Pith citation observations are available.