S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity | Paper Citation Record

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This paper cites GPT-4 Technical Report.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity GPT-4 Technical Report

Reference 2

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This paper cites Composable Sparse Fine-Tuning for Cross-Lingual Transfer.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Composable Sparse Fine-Tuning for Cross-Lingual Transfer

Reference 3

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This paper cites Scaling Sparse Fine-Tuning to Large Language Models.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Scaling Sparse Fine-Tuning to Large Language Models

Reference 4

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Implicit regularization in deep matrix factorization

Reference 5

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Llemma: An Open Language Model For Mathematics

Reference 6

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This paper cites Rapid Switching and Multi-Adapter Fusion via Sparse High Rank Adapters.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Rapid Switching and Multi-Adapter Fusion via Sparse High Rank Adapters

Reference 7

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This paper cites LoRA Learns Less and Forgets Less.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity LoRA Learns Less and Forgets Less

Reference 8

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This paper cites Piqa: Reasoning about physical common- sense in natural language.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Piqa: Reasoning about physical common- sense in natural language

Reference 9

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This paper cites Parameter-Efficient Fine-Tuning Design Spaces.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Parameter-Efficient Fine-Tuning Design Spaces

Reference 10

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This paper cites Spectral methods for data science: A statistical perspective.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Spectral methods for data science: A statistical perspective

Reference 11

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This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 12

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This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 13

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Observation 7a9a8bd9-b649-43e3-82ad-bead7ff095d5 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Training Verifiers to Solve Math Word Problems

Reference 14

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This paper cites Sparse Low-rank Adaptation of Pre-trained Language Models.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Sparse Low-rank Adaptation of Pre-trained Language Models

Reference 15

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This paper cites The Llama 3 Herd of Models.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity The Llama 3 Herd of Models

Reference 16

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This paper cites Depgraph: Towards any structural pruning.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Depgraph: Towards any structural pruning

Reference 17

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This paper cites Optimal gradient checkpoint search for arbitrary computation graphs.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Optimal gradient checkpoint search for arbitrary computation graphs

Reference 18

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This paper cites Implicit regularization in matrix factorization.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Implicit regularization in matrix factorization

Reference 19

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This paper cites Parameter-Efficient Transfer Learning with Diff Pruning.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Parameter-Efficient Transfer Learning with Diff Pruning

Reference 20

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

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 21

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This paper cites Identity Matters in Deep Learning.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Identity Matters in Deep Learning

Reference 22

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This paper cites LoRA+: Efficient Low Rank Adaptation of Large Models.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity LoRA+: Efficient Low Rank Adaptation of Large Models

Reference 23

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Towards a Unified View of Parameter-Efficient Transfer Learning

Reference 24

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Learning to solve arithmetic word problems with verb categorization

Reference 25

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Parameter-efficient transfer learning for NLP

Reference 26

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

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity LoRA: Low-Rank Adaptation of Large Language Models

Reference 27

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This paper cites LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models

Reference 28

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning

Reference 29

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Deep learning without poor local minima

Reference 30

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Parsing algebraic word problems into equations

Reference 31

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Mawps: A math word problem repository

Reference 32

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity LoRA-Switch: Boosting the Efficiency of Dynamic LLM Adapters via System-Algorithm Co-design

Reference 33

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Deep linear networks with arbitrary loss: All local minima are global

Reference 34

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 35

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 36

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems

Reference 37

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity DoRA: Weight-Decomposed Low-Rank Adaptation

Reference 38

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization

Reference 39

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity GPT understands, too

Reference 40

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models

Reference 41

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Deja vu: Contextual sparsity for efficient llms at inference time

Reference 42

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Depth Creates No Bad Local Minima

Reference 43

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning

Reference 44

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Llm-pruner: On the structural pruning of large language models

Reference 45

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 46

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data

Reference 47

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning

Reference 48

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Lottery Ticket Adaptation: Mitigating Destructive Interference in LLMs

Reference 49

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Unresolved cited work

Reference 50

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer

Reference 51

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity SBoRA: Low-Rank Adaptation with Regional Weight Updates

Reference 52

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This paper cites Initialization using update approximation is a silver bullet for extremely efficient low-rank fine-tuning.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Initialization using update approximation is a silver bullet for extremely efficient low-rank fine-tuning

Reference 53

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This paper cites Controlling text-to-image diffusion by orthogonal finetuning.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Controlling text-to-image diffusion by orthogonal finetuning

Reference 54

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Solving General Arithmetic Word Problems

Reference 55

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Code Llama: Open Foundation Models for Code

Reference 56

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Observation c9fbbe38-055d-4030-8182-8e4c4e8e3682 · outbound

This paper cites Winogrande: An adversarial winograd schema challenge at scale.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Winogrande: An adversarial winograd schema challenge at scale

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:55:13.420631Z

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Observation 77c6c965-af56-454c-b6e0-bd3c7abb8fee · outbound

This paper cites SocialIQA: Commonsense Reasoning about Social Interactions.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity SocialIQA: Commonsense Reasoning about Social Interactions

Reference 58

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This paper cites Exact solutions to the nonlinear dynamics of learning in deep linear neural networks.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Exact solutions to the nonlinear dynamics of learning in deep linear neural networks

Reference 59

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Observation 52fa31b7-9737-43c0-8b4a-ac48083c1cf4 · outbound

This paper cites S-LoRA: Serving Thousands of Concurrent LoRA Adapters.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity S-LoRA: Serving Thousands of Concurrent LoRA Adapters

Reference 60

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Observation 6668e837-756e-43a7-8967-d2ca8dac6c2b · outbound

This paper cites Maintaining Structural Integrity in Parameter Spaces for Parameter Efficient Fine-tuning.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Maintaining Structural Integrity in Parameter Spaces for Parameter Efficient Fine-tuning

Reference 61

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This paper cites On the continuity of the generalized inverse.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity On the continuity of the generalized inverse

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:55:13.408167Z

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Observation 40678142-2549-44fd-aaea-a027051c7265 · outbound

This paper cites Training neural networks with fixed sparse masks.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Training neural networks with fixed sparse masks

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:55:13.395030Z

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Observation 79d14af2-b6d6-4a0a-a04b-afb4d9638cd2 · outbound

This paper cites Training neural networks with fixed sparse masks.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Training neural networks with fixed sparse masks

Reference 64

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Observation 4c7b8d65-347e-4c41-8b89-6e07669a0bf1 · outbound

This paper cites Hashimoto.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Hashimoto

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:55:13.370245Z

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This paper cites Gemini: A Family of Highly Capable Multimodal Models.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Gemini: A Family of Highly Capable Multimodal Models

Reference 66

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity An inequality for trace ideals

Reference 67

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Observation 0e362f9e-b2b8-4432-871e-93e550b221f5 · outbound

This paper cites RoseLoRA: Row and Column-wise Sparse Low-rank Adaptation of Pre-trained Language Model for Knowledge Editing and Fine-tuning.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity RoseLoRA: Row and Column-wise Sparse Low-rank Adaptation of Pre-trained Language Model for Knowledge Editing and Fine-tuning

Reference 68

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This paper cites ReFT: Representation Finetuning for Language Models.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity ReFT: Representation Finetuning for Language Models

Reference 69

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

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 70

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This paper cites Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning

Reference 71

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This paper cites Tuning large neural networks via zero-shot hyperparameter transfer.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Tuning large neural networks via zero-shot hyperparameter transfer

Reference 72

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Observation dc673908-a5dc-42e7-a016-9e8975ccaf2e · outbound

This paper cites A useful variant of the davis–kahan theorem for statisticians.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity A useful variant of the davis–kahan theorem for statisticians

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:55:13.332535Z

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Observation 5f51df90-a2bc-47fc-bdfd-96e1d0514f62 · outbound

This paper cites ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge

Reference 74

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This paper cites Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-11T19:55:12.184446Z

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This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 76

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Observation 797aaac3-05c2-4566-91b9-138e21c59d12 · outbound

This paper cites Understanding deep learning (still) requires rethinking generalization.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Understanding deep learning (still) requires rethinking generalization

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:55:13.320141Z

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Observation 3c27c234-64c7-48a8-af0d-b4994aa1c573 · outbound

This paper cites Composing parameter-efficient modules with arithmetic operation.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Composing parameter-efficient modules with arithmetic operation

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:55:13.307513Z

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Observation 4b0853ef-8030-41db-8f0c-af7a20ee9c2e · outbound

This paper cites LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning

Reference 79

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Observation 071000b4-3f6b-4e05-8d9e-ca2cd9522012 · outbound

This paper cites GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-11T19:55:12.204947Z

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Observation f43bfaaf-0b73-47c9-b993-5d1e6518ebe6 · outbound

This paper cites LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4, A Technical Report.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4, A Technical Report

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-11T19:55:12.209404Z

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Observation 3a6b3171-1391-43d2-aa8c-7cd8e3fdadc3 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Judging llm-as-a-judge with mt-bench and chatbot arena

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:55:13.293936Z

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Observation 6603938e-b3d5-4a3f-a08e-b6b5a2309493 · outbound

This paper cites Multi-LoRA Composition for Image Generation.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Multi-LoRA Composition for Image Generation

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-11T19:55:12.217792Z

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Observation f3aa1c38-cb01-424e-b0f0-6bd18f0a9b6f · outbound

This paper cites Asymmetry in Low-Rank Adapters of Foundation Models.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Asymmetry in Low-Rank Adapters of Foundation Models

Reference 84

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This paper cites Layer-wise Importance Matters: Less Memory for Better Performance in Parameter-efficient Fine-tuning of Large Language Models.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Layer-wise Importance Matters: Less Memory for Better Performance in Parameter-efficient Fine-tuning of Large Language Models

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-11T19:55:12.226365Z

Source-reported events for the cited work

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Observation 1ddbdeb5-f9f8-41fe-b1e6-8678afd08458 · outbound

This paper cites For the output projection, all channels in the selected heads will be included to enable dense-only computation.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity For the output projection, all channels in the selected heads will be included to enable dense-only computation

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:55:13.280354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ee85900c-9a48-48d7-a33d-b57e7fbaa10e · outbound

This paper cites We will test subsets corresponding to both the largest and smallest weights.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity We will test subsets corresponding to both the largest and smallest weights

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:55:13.267332Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6ff098b2-ec6c-4d1c-99c8-a7107cfd9417 · outbound

This paper cites Since collecting activations requires only forward passes, this approach maintains the same memory footprint as inference and incurs a negligible increase in training time.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Since collecting activations requires only forward passes, this approach maintains the same memory footprint as inference and incurs a negligible increase in training time

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:55:13.253829Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8120162f-9ea6-4d23-a77e-4d3367a3c2f9 · outbound

This paper cites The activation values are collected in a manner similar to S2FT-A.

S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity The activation values are collected in a manner similar to S2FT-A

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:55:13.240550Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ad835a2b-2782-4e01-8b9a-9ea031ea336d · outbound

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S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Unresolved cited work

Reference 90

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