Paper Citation Record · LEDGER

Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models

As of 19 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2501.15255.

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

pith.paper-citation-record.v1
2501.15255 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

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measured 47 of 47 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

47 of 47 outbound references displayed

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External citation measurements

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Outbound references

Observation 013c793a-1f4f-44fd-b559-a65f588a533b · outbound

This paper cites A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems.

Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems

Reference 1

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This paper cites Toolqa: A dataset for llm question answering with external tools,.

Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Toolqa: A dataset for llm question answering with external tools,

Reference 2

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Observation 1fc67cd9-391c-468d-bbff-7aea2db876af · outbound

This paper cites A comprehensive survey on process-oriented automatic text summarization with exploration of llm-based methods,.

Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models A comprehensive survey on process-oriented automatic text summarization with exploration of llm-based methods,

Reference 3

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This paper cites Exploding ai power use: an opportunity to rethink grid planning and management,.

Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Exploding ai power use: an opportunity to rethink grid planning and management,

Reference 4

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This paper cites Carbon Emissions and Large Neural Network Training.

Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Carbon Emissions and Large Neural Network Training

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This paper cites Drive as you speak: Enabling human-like interaction with large language models in au- tonomous vehicles,.

Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Drive as you speak: Enabling human-like interaction with large language models in au- tonomous vehicles,

Reference 6

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This paper cites LLM as a System Service on Mobile Devices.

Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models LLM as a System Service on Mobile Devices

Reference 7

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This paper cites Language models are few-shot learners advances,.

Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Language models are few-shot learners advances,

Reference 8

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Observation a448f3cd-607c-401b-b630-272fa0a7df22 · outbound

This paper cites Energy and policy consid- erations for modern deep learning research,.

Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Energy and policy consid- erations for modern deep learning research,

Reference 9

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Observation bfda8e4c-9cf8-440c-a99f-ef35692fcf58 · outbound

This paper cites Learning both weights and connections for efficient neural network,.

Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Learning both weights and connections for efficient neural network,

Reference 10

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Observation c3d97847-33d0-4074-bebf-99c305448449 · outbound

This paper cites The lottery ticket hypothesis: Finding sparse, trainable neural networks,.

Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models The lottery ticket hypothesis: Finding sparse, trainable neural networks,

Reference 11

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This paper cites Stabilizing the Lottery Ticket Hypothesis.

Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Stabilizing the Lottery Ticket Hypothesis

Reference 12

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Observation c848a58c-8bad-4138-84a1-1b8e3aad5b48 · outbound

This paper cites Sparsegpt: Massive language models can be accurately pruned in one-shot,.

Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Sparsegpt: Massive language models can be accurately pruned in one-shot,

Reference 13

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Observation f8778de6-39f8-4a3b-8b69-eb9b73f8b73d · outbound

This paper cites Plug-and-play: An efficient post-training pruning method for large language models,.

Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Plug-and-play: An efficient post-training pruning method for large language models,

Reference 14

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Observation 861098ea-9bf3-4585-864d-f04b67e44447 · outbound

This paper cites Nvidia a100 tensor core gpu: Performance and innovation,.

Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Nvidia a100 tensor core gpu: Performance and innovation,

Reference 15

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Observation c15ccb1a-c0a3-4875-bd00-d852470f5f1a · outbound

This paper cites A fast post-training pruning framework for transformers,.

Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models A fast post-training pruning framework for transformers,

Reference 16

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Observation 6025b344-80c9-4c59-be53-413b5e2085df · outbound

This paper cites Loraprune: Structured pruning meets low-rank parameter-efficient fine- tuning,.

Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Loraprune: Structured pruning meets low-rank parameter-efficient fine- tuning,

Reference 17

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Observation 914996d1-6d6c-4f73-a1a2-336f930a7cdf · outbound

This paper cites ShortGPT: Layers in Large Language Models are More Redundant Than You Expect.

Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models ShortGPT: Layers in Large Language Models are More Redundant Than You Expect

Reference 18

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This paper cites Llm-pruner: On the structural pruning of large language models,.

Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Llm-pruner: On the structural pruning of large language models,

Reference 19

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Observation 32b8e711-9b39-4c5d-84cb-5a62ec94e6f7 · outbound

This paper cites A Simple and Effective Pruning Approach for Large Language Models.

Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models A Simple and Effective Pruning Approach for Large Language Models

Reference 20

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

Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 21

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This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 22

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Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Compacter: Efficient low-rank hypercomplex adapter layers,

Reference 23

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Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Decoupled weight decay regularization,

Reference 24

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Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Privacy-preserving large language models for structured medical information retrieval,

Reference 25

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Observation 569f2be8-04f6-47c1-9a15-1184ec144354 · outbound

This paper cites Optimizing llm training for financial services: Best practices for model accuracy, risk management, and compliance in ai-powered financial applications,.

Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Optimizing llm training for financial services: Best practices for model accuracy, risk management, and compliance in ai-powered financial applications,

Reference 26

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Observation 413ef0f4-ae05-4b25-8b63-c54a62aa4674 · outbound

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Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 27

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This paper cites SliceGPT: Compress Large Language Models by Deleting Rows and Columns.

Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models SliceGPT: Compress Large Language Models by Deleting Rows and Columns

Reference 28

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This paper cites Deja vu: Contextual sparsity for efficient llms at inference time,.

Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Deja vu: Contextual sparsity for efficient llms at inference time,

Reference 29

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Observation fac87cdc-d51f-4666-9662-c5f8cee278ec · outbound

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Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 30

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Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models An estimate for the condition number of a matrix,

Reference 31

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Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Matrix inversion using cholesky decomposition,

Reference 32

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Observation 68e0d6a8-843e-49f8-9223-ddacac42845c · outbound

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Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Points of significance: model selection and overfitting,

Reference 33

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Observation 436bbed2-b290-45c9-8d32-a2f722798a0b · outbound

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Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models OPT: Open Pre-trained Transformer Language Models

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Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models GLM-130B: An Open Bilingual Pre-trained Model

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Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Pointer Sentinel Mixture Models

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Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Building a large annotated corpus of english: The penn treebank,

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Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Compressing pre-trained language models by matrix decomposition,

Reference 38

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Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models BoolQ: Exploring the surprising difficulty of natural yes/no questions,

Reference 39

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Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Winogrande: An adversarial winograd schema challenge at scale,

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Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning

Reference 41

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Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Measuring Massive Multitask Language Understanding

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Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Piqa: Reasoning about physical commonsense in natural language,

Reference 43

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Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Crowdsourcing Multiple Choice Science Questions

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Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Exploring the limits of transfer learning with a unified text-to-text transformer,

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Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Cupy: A numpy-compatible library for nvidia gpu calculations,

Reference 46

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Observation e4d836dd-cb1c-4e10-a6a2-522a50cae2bd · outbound

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Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models Decoupled Weight Decay Regularization

Reference 2019

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Pith citing papers

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