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Found 4 papers, 1 papers with code
EdgeQAT: Entropy and Distribution Guided Quantization-Aware Training for the Acceleration of Lightweight LLMs on the Edge
In this paper, we propose EdgeQAT, the Entropy and Distribution Guided QAT for the optimization of lightweight LLMs to achieve inference acceleration on Edge devices.
Punctuation Matters! Stealthy Backdoor Attack for Language Models
Meanwhile, we conduct automatic evaluation and human inspection, which indicate the proposed method possesses good performance of stealthiness without bringing grammatical issues and altering the meaning of sentences.
A Survey on Backdoor Attack and Defense in Natural Language Processing
Deep learning is becoming increasingly popular in real-life applications, especially in natural language processing (NLP).
PromptAttack: Prompt-based Attack for Language Models via Gradient Search
Therefore, in this paper, we propose a malicious prompt template construction method (\textbf{PromptAttack}) to probe the security performance of PLMs.
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