Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-05T12:52:01.739790Z
Paper Citation Record · LEDGER
As of 7 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2509.01221.
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 2026-08-05T12:52:01.739790Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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
47 of 47 outbound references displayed
External citation measurements
No source-named external measurement is stored.
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
Reference 1
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Observation a97e75c8-df03-4b82-88f7-a0d5762605b6 · outbound
DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression AlpaGasus: Training A Better Alpaca with Fewer Data
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Reference 8
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression MoDS: Model-oriented Data Selection for Instruction Tuning
Reference 9
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression The Llama 3 Herd of Models
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression Unresolved cited work
Reference 11
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools
Reference 12
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression Unresolved cited work
Reference 13
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression Unresolved cited work
Reference 14
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression Mistral 7B
Reference 15
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models
Reference 16
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression
Reference 17
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression Unresolved cited work
Reference 18
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression Unresolved cited work
Reference 19
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression Unresolved cited work
Reference 20
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression Unresolved cited work
Reference 21
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression Unresolved cited work
Reference 22
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression Unresolved cited work
Reference 23
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression Large Language Model Agent for Hyper-Parameter Optimization
Reference 24
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression Unresolved cited work
Reference 25
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Observation 5a50fa02-844c-47c3-ae89-b09e4bb64c73 · outbound
DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression Unresolved cited work
Reference 26
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Observation 60e4bf11-73e1-422a-a89d-ad4476df37d4 · outbound
DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression Unresolved cited work
Reference 27
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression Unresolved cited work
Reference 28
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression Unresolved cited work
Reference 29
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Reference 30
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression Efficient Large Language Models: A Survey
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression Unresolved cited work
Reference 33
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression A Survey on Knowledge Distillation of Large Language Models
Reference 34
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression Unresolved cited work
Reference 37
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Observation ddae4f8b-b218-4b77-85d3-392f032d56c3 · outbound
DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression LaCo: Large Language Model Pruning via Layer Collapse
Reference 38
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Reference 39
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Reference 40
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression BERTScore: Evaluating Text Generation with BERT
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression Unresolved cited work
Reference 42
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Observation 882936b6-41ab-4fed-bd7c-ecfcdc81ac49 · outbound
DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression SWIFT:A Scalable lightWeight Infrastructure for Fine-Tuning
Reference 43
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression Unresolved cited work
Reference 44
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Observation 7d1220cd-4c89-4ff3-8003-db1ce7f96456 · outbound
DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression A Survey on Model Compression for Large Language Models
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No inbound Pith citation observations are available.