Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks | Paper Citation Record

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This paper cites Will we run out of data? limits of llm scaling based on human-generated data, 2024.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Will we run out of data? limits of llm scaling based on human-generated data, 2024

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This paper cites Advances and open challenges in federated foundation models, 2024.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Advances and open challenges in federated foundation models, 2024

Reference 2

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Unresolved cited work

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Introducing chatgpt

Reference 4

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Gpt-4 technical report

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks A Survey of Resource-efficient LLM and Multimodal Foundation Models

Reference 6

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks BloombergGPT: A Large Language Model for Finance

Reference 7

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Deepseek-v3 technical report, 2024

Reference 8

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Deep residual learning for image recognition

Reference 9

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Long short-term memory

Reference 10

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This paper cites Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Reference 11

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This paper cites Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S

Reference 12

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This paper cites LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day

Reference 13

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This paper cites Biogpt: generative pre-trained transformer for biomedical text generation and mining.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Biogpt: generative pre-trained transformer for biomedical text generation and mining

Reference 14

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Biobert: a pre-trained biomedical language representation model for biomedical text mining

Reference 15

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Phi-2: The surprising power of small language models

Reference 16

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 17

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This paper cites Introducing llama 3.1: Our most capable models to date.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Introducing llama 3.1: Our most capable models to date

Reference 18

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Position: Will we run out of data? limits of llm scaling based on human-generated data

Reference 19

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This paper cites Communication- efficient learning of deep networks from decentralized data.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Communication- efficient learning of deep networks from decentralized data

Reference 20

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Federated machine learning: Concept and applications

Reference 21

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This paper cites Regulation (eu) 2016/679 of the european parliament and of the council.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Regulation (eu) 2016/679 of the european parliament and of the council

Reference 22

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks California consumer privacy act (ccpa)

Reference 23

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Health insurance portability and accountability act of 1996

Reference 24

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Federated learning for healthcare domain-pipeline, applications and challenges

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Review on security of federated learning and its application in healthcare

Reference 26

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Deep learning-based classification of mesothelioma improves prediction of patient outcome

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Use of Federated Learning and Blockchain towards Securing Financial Services

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Efficient and secure federated learning for financial applications

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Machine learning ledger orchestration for drug discovery, 2019

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Protecting intellectual property of large language model-based code generation apis via watermarks

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks History, Development, and Principles of Large Language Models-An Introductory Survey

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Llmcarbon: Modeling the end-to-end carbon footprint of large language models, 2024

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Distilling the Knowledge in a Neural Network

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Knowledge distillation: A survey

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks A Survey on Knowledge Distillation of Large Language Models

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Survey on knowledge distillation for large language models: Methods, evaluation, and application

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks MiniLLM: On-Policy Distillation of Large Language Models

Reference 39

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks On-policy distillation of language models: Learning from self-generated mistakes

Reference 40

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks For distillation, tokens are not all you need

Reference 41

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Baby llama: knowledge distillation from an ensemble of teachers trained on a small dataset with no performance penalty

Reference 42

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks LLAVADI: What Matters For Multimodal Large Language Models Distillation

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Less is more: Task-aware layer-wise distillation for language model compression

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks DDK: Distilling Domain Knowledge for Efficient Large Language Models

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Unresolved cited work

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Crash: Clustering, removing, and sharing enhance fine-tuning without full large language model

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Orchestration of emulator assisted mobile edge tuning for ai foundation models: A multi-agent deep reinforcement learning approach, 2023

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Offsite-Tuning: Transfer Learning without Full Model

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Fedpft: Federated proxy fine-tuning of foundation models

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks FedMD: Heterogenous Federated Learning via Model Distillation

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Ensemble attention distillation for privacy-preserving federated learning

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Preserving privacy in federated learning with ensemble cross-domain knowledge distillation

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Data shunt: Collaboration of small and large models for lower costs and better performance

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Parameterized knowledge transfer for personalized federated learning

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Ensemble distillation for robust model fusion in federated learning

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Depth anything: Unleashing the power of large-scale unlabeled data

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Robust federated learning with noisy and heterogeneous clients

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Fedtgp: Trainable global prototypes with adaptive-margin- enhanced contrastive learning for data and model heterogeneity in federated learning

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This paper cites An upload-efficient scheme for transferring knowledge from a server-side pre-trained generator to clients in heterogeneous federated learning.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks An upload-efficient scheme for transferring knowledge from a server-side pre-trained generator to clients in heterogeneous federated learning

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Observation 23c3a549-51d9-4166-9a34-a95158e04955 · outbound

This paper cites Learning from human educational wisdom: A student-centered knowledge distillation method.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Learning from human educational wisdom: A student-centered knowledge distillation method.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024

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Observation e5b36ebc-94c8-410c-b770-2ccc830933dc · outbound

This paper cites Bert learns to teach: Knowledge distillation with meta learning.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Bert learns to teach: Knowledge distillation with meta learning

Reference 62

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This paper cites Fedgems: Federated learning of larger server models via selective knowledge fusion, 2021.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Fedgems: Federated learning of larger server models via selective knowledge fusion, 2021

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Observation 53593872-91e9-4c34-8cb2-cb02754c28fb · outbound

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Multimodal Federated Learning via Contrastive Representation Ensemble

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Observation 9355f99e-8834-46d6-a432-ea3a2f808460 · outbound

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Ideal: Query-efficient data-free learning from black-box models

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Observation 45537e89-01b9-4769-b292-b9c29fd0bfef · outbound

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Towards data-free model stealing in a hard label setting

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Observation 99a43378-b052-4007-9267-08b137970370 · outbound

This paper cites Zero-shot knowledge distillation from a decision-based black-box model.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Zero-shot knowledge distillation from a decision-based black-box model

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This paper cites Bridging the gap between decision and logits in decision- based knowledge distillation for pre-trained language models.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Bridging the gap between decision and logits in decision- based knowledge distillation for pre-trained language models

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This paper cites Synthetic data distillation enables the extraction of clinical information at scale.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Synthetic data distillation enables the extraction of clinical information at scale

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks ZeroGen: Efficient zero-shot learning via dataset generation

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This paper cites ProGen: Progressive zero- shot dataset generation via in-context feedback.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks ProGen: Progressive zero- shot dataset generation via in-context feedback

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Observation 8a469541-c0e0-47e7-bac9-655870860a6f · outbound

This paper cites Generating training data with language models: Towards zero-shot language understanding.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Generating training data with language models: Towards zero-shot language understanding

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Self-guided noise-free data generation for efficient zero-shot learning

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks FuseGen: PLM Fusion for Data-generation based Zero-shot Learning

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Observation 0ca54650-e4fd-4abf-9808-bf8551cd04ab · outbound

This paper cites Retrieval-based knowledge transfer: An effective approach for extreme large language model compression.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Retrieval-based knowledge transfer: An effective approach for extreme large language model compression

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This paper cites Seeking Neural Nuggets: Knowledge Transfer in Large Language Models from a Parametric Perspective.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Seeking Neural Nuggets: Knowledge Transfer in Large Language Models from a Parametric Perspective

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This paper cites Mutual enhancement of large and small language models with cross-silo knowledge transfer, 2023.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Mutual enhancement of large and small language models with cross-silo knowledge transfer, 2023

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Zico Kolter

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This paper cites Zeroquant: Efficient and affordable post-training quantization for large-scale transformers.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Zeroquant: Efficient and affordable post-training quantization for large-scale transformers

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This paper cites Initializing models with larger ones, 2023.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Initializing models with larger ones, 2023

Reference 80

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Learning to Teach with Student Feedback

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Observation 653bd436-6f53-43c6-bc0f-4519ce29138c · outbound

This paper cites Meta pseudo labels.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Meta pseudo labels

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This paper cites Dual knowledge distillation for bidirectional neural machine translation.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Dual knowledge distillation for bidirectional neural machine translation

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Observation 1e94c6be-7799-44a1-80c9-a93f4ec14fd6 · outbound

This paper cites Shadow knowledge distillation: Bridging offline and online knowledge transfer.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Shadow knowledge distillation: Bridging offline and online knowledge transfer

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Observation 5631268b-b98b-4d40-aa12-e532f4a0fb99 · outbound

This paper cites Reverse knowledge distillation: Training a large model using a small one for retinal image matching on limited data.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Reverse knowledge distillation: Training a large model using a small one for retinal image matching on limited data

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This paper cites Weak-to-strong generalization: Eliciting strong capabilities with weak supervision.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Weak-to-strong generalization: Eliciting strong capabilities with weak supervision

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Models

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This paper cites Federated learning with gan-based data synthesis for non-iid clients.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Federated learning with gan-based data synthesis for non-iid clients

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This paper cites Gfl: Federated learning on non-iid data via privacy-preserving synthetic data.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Gfl: Federated learning on non-iid data via privacy-preserving synthetic data

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This paper cites Harnessing large- language models to generate private synthetic text, 2024.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Harnessing large- language models to generate private synthetic text, 2024

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Privacy-preserving instructions for aligning large language models, 2024

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This paper cites Stable federated learning with dataset condensation.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Stable federated learning with dataset condensation

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Federated learning via decentralized dataset distillation in resource-constrained edge environments

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Distilled One-Shot Federated Learning

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Dataset Distillation

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Dataset Condensation with Gradient Matching

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This paper cites Dauphin, and David Lopez-Paz.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Dauphin, and David Lopez-Paz

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Resolution: unresolved; no resolver link, observed 2026-08-16T10:44:46.441320Z

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Mix2fld: Downlink federated learning after uplink federated distillation with two-way mixup

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This paper cites Differentially private synthetic data via foundation model APIs 1: Images.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Differentially private synthetic data via foundation model APIs 1: Images

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Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Differentially Private Synthetic Data via Foundation Model APIs 2: Text

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