Papers by Kang Gu
From Insight to Exploit: Leveraging LLM Collaboration for Adaptive Adversarial Text Generation (2025.findings-emnlp)
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| Challenge: | LLMs can provide substantial zero-shot performance on diverse tasks, but it is crucial to assess their robustness against adversarial inputs. |
| Approach: | They introduce Static Deceptor and Dynamic Deceptr to generate adversarial examples . they produce subtle and natural-looking adversarials that preserve semantic similarity to text . |
| Outcome: | The proposed attacks are based on two LLM-based attacks that generate natural-looking examples that deceive the target LLM. |
FedCoT: Federated Chain-of-Thought Distillation for Large Language Models (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have emerged as a transformative force in artificial intelligence, demonstrating exceptional proficiency across various tasks. |
| Approach: | They propose a federated framework for the Chain-of-Thought distillation of knowledge from LLMs to SLMs, while adhering to privacy requirements. |
| Outcome: | The proposed framework ensures secure knowledge transfer from an LLM on a high-powered server to an SLM on resource-constrained client while adhering to privacy requirements. |
FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Models (2025.coling-main)
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| Challenge: | Recent research in large language models (LLMs) has focused on enabling clients to fine-tune their locally deployed homogeneous LLMs collaboratively or on transferring knowledge from server-based LLM to small language models at downstream clients. |
| Approach: | They propose a parameter-efficient federated mutual knowledge transfer framework for large and small language models that allows for token alignment and selective knowledge transfer between client-side LLMs and a server-side SLM. |
| Outcome: | The proposed framework enhances the performance of both LLMs and SLMs with clients' unique domain insights while preserving the server's LLM and client's unique domain insight. |