Picture for Chulin Xie

Chulin Xie

Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs

Add code
Apr 10, 2024
Figure 1 for Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs
Figure 2 for Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs
Figure 3 for Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs
Figure 4 for Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs
Viaarxiv icon

FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning

Add code
Apr 03, 2024
Viaarxiv icon

TablePuppet: A Generic Framework for Relational Federated Learning

Add code
Mar 23, 2024
Viaarxiv icon

Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression

Add code
Mar 18, 2024
Figure 1 for Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression
Figure 2 for Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression
Figure 3 for Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression
Figure 4 for Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression
Viaarxiv icon

Differentially Private Synthetic Data via Foundation Model APIs 2: Text

Add code
Mar 04, 2024
Figure 1 for Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Figure 2 for Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Figure 3 for Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Figure 4 for Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Viaarxiv icon

Effective and Efficient Federated Tree Learning on Hybrid Data

Oct 18, 2023
Figure 1 for Effective and Efficient Federated Tree Learning on Hybrid Data
Figure 2 for Effective and Efficient Federated Tree Learning on Hybrid Data
Figure 3 for Effective and Efficient Federated Tree Learning on Hybrid Data
Figure 4 for Effective and Efficient Federated Tree Learning on Hybrid Data
Viaarxiv icon

Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?

Add code
Oct 16, 2023
Viaarxiv icon

DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Add code
Jun 20, 2023
Figure 1 for DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Figure 2 for DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Figure 3 for DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Figure 4 for DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Viaarxiv icon

FedMLSecurity: A Benchmark for Attacks and Defenses in Federated Learning and LLMs

Add code
Jun 08, 2023
Figure 1 for FedMLSecurity: A Benchmark for Attacks and Defenses in Federated Learning and LLMs
Figure 2 for FedMLSecurity: A Benchmark for Attacks and Defenses in Federated Learning and LLMs
Figure 3 for FedMLSecurity: A Benchmark for Attacks and Defenses in Federated Learning and LLMs
Figure 4 for FedMLSecurity: A Benchmark for Attacks and Defenses in Federated Learning and LLMs
Viaarxiv icon

PerAda: Parameter-Efficient and Generalizable Federated Learning Personalization with Guarantees

Feb 13, 2023
Figure 1 for PerAda: Parameter-Efficient and Generalizable Federated Learning Personalization with Guarantees
Figure 2 for PerAda: Parameter-Efficient and Generalizable Federated Learning Personalization with Guarantees
Figure 3 for PerAda: Parameter-Efficient and Generalizable Federated Learning Personalization with Guarantees
Figure 4 for PerAda: Parameter-Efficient and Generalizable Federated Learning Personalization with Guarantees
Viaarxiv icon