在指令数据挖掘的对抗提示上更多地关注LLM的稳定性
Qiang Wang1, Dawei Feng1, Xu Zhang1
1National Key Laboratory of Parallel and Distributed Computing, College of Computer Science and Technology, National University of Defense Technology, Hunan Changsha, 410073, China.
这项研究引入了通过挖掘高质量的教学数据来改进大型语言模型的新方法. 我们开发了识别具有挑战性的教学样本的技术,
科学领域:
- 人工智能
- 自然语言处理
- 机器学习
背景情况:
- 指令调整是定制大型语言模型 (LLM) 行为的关键.
- 通过有限的高质量的指令数据可以实现高性能.
- 在LLM未能遵循指令时,IFD会挖掘数据.
研究的目的:
- 调查LLM对对抗提示的强度如何影响高质量的指令数据选择.
- 为指令调整提供高质量的指令数据挖掘的新框架.
主要方法:
- 通过攻击提示生成对手指令数据.
- 引入使用样本对的对抗指令执行难度 (AIFD) 度量.
- 开发了对抗指令输出嵌入一致性 (AIOEC),仅使用在线数据挖掘提示.
主要成果:
- 实验结果证明了拟议的AIFD和AIOEC方法的有效性.
- 这项研究强调了LLM在数据挖掘中的强度的重要性.
- 这两种方法都成功地识别了高质量的调指令数据.
结论:
- 对于有效的指令数据挖掘来说,LLM对对抗提示的强度至关重要.
- 拟议的AIFD和AIOEC方法在指令调整方面提供了显著的改进.
- 考虑对抗性强度可以提高挖掘指令数据的质量和实用性.
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