阅读笔记Security and Privacy Challenges of Large Language Models: A Survey

这篇综述论文探讨了大型语言模型在安全和隐私方面的问题,包括数据隐私泄露、越狱攻击、防御策略等,强调了跨领域应用的风险及未来研究的必要性。

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论文标题: Security and Privacy Challenges of Large Language Models: A Survey

作者: Badhan Chandra Das, M. Hadi Amini, Yanzhao Wu

发表日期: 2024年2月

论文链接: arXiv:2402.00888

摘要:

这篇综述论文全面探讨了大型语言模型(LLMs)在安全性和隐私方面的挑战。LLMs在多个领域展现出了卓越的能力,如文本生成、摘要、语言翻译和问答。然而,这些模型也面临着安全和隐私攻击的脆弱性,例如越狱攻击、数据投毒攻击和个人身份信息(PII)泄露攻击。作者全面回顾了LLMs在训练数据和用户方面的安全和隐私挑战,以及在交通、教育和医疗等不同领域的应用风险。论文评估了LLMs的脆弱性,调查了针对LLMs的新兴安全和隐私攻击,并回顾了潜在的防御机制。此外,论文概述了该领域的现有研究空白,并强调了未来的研究方向。

主要内容:
  1. LLMs的兴起与应用:

    • LLMs在学术和工业界越来越受欢迎,能够处理从日常语言沟通到特定挑战的广泛任务。
    • 它们通过预训练和微调过程,学习语言的深层结构、模式和上下文关系。
  2. 安全与隐私挑战:

### Chain-of-Thought Prompting Mechanism in Large Language Models In large language models, chain-of-thought prompting serves as a method to enhance reasoning capabilities by guiding the model through structured thought processes. This approach involves breaking down complex problems into simpler components and providing step-by-step guidance that mirrors human cognitive processing. The creation of these prompts typically includes selecting examples from training datasets where each example represents part of an overall problem-solving process[^2]. By decomposing tasks into multiple steps, this technique encourages deeper understanding and more accurate predictions compared to traditional methods. For instance, when faced with multi-hop question answering or logical deduction challenges, using such chains allows models not only to generate correct answers but also articulate intermediate thoughts leading up to those conclusions. Such transparency facilitates better interpretability while improving performance on various NLP benchmarks. ```python def create_chain_of_thought_prompt(task_description, examples): """ Creates a chain-of-thought prompt based on given task description and examples. Args: task_description (str): Description of the task at hand. examples (list): List containing tuples of input-output pairs used for demonstration purposes. Returns: str: Formatted string representing the final prompt including both instructions and sample cases. """ formatted_examples = "\n".join([f"Input: {ex[0]}, Output: {ex[1]}" for ex in examples]) return f""" Task: {task_description} Examples: {formatted_examples} Now try solving similar questions following above pattern. """ # Example usage examples = [ ("What color do you get mixing red and blue?", "Purple"), ("If it rains tomorrow, will we have our picnic?", "No") ] print(create_chain_of_thought_prompt("Solve logic puzzles", examples)) ```
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