LM-SODP:语言模型自我优化离散提示符用于基于方面的情感分析.
1Faculty of Computing, Harbin Institute of Technology, No. 92 Xidazhi Street, Harbin 150001, China.
Entropy (Basel, Switzerland)
|December 24, 2025
概括
本研究介绍了LM-SODP,一种强化学习 (RL) 框架,可以自动优化面向基于情感分析 (ABSA) 中的大型语言模型 (LLM) 的离散提示和预测命令. 它增强了模型的性能,并减少了在快速工程中人工的努力.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 由于可解释性和兼容性,离散提示是大型语言模型 (LLM) 的标准.
- 针对细粒度任务,如基于方面的情感分析 (ABSA),优化提示是困难的,因为错误的传播和手动设计的努力.
研究的目的:
- 介绍LM-SODP,一种强化学习 (RL) 框架,用于ABSA中的自动离散提示符和预测顺序优化.
- 通过提高特定任务的信息利用率和减少输出不确定性来提高在ABSA上的LLM性能.
主要方法:
- 在RL框架 (LM-SODP) 中使用了蒸的GPT-2模型.
- 优化离散提示以减少输出并改善特定任务信息的使用.
- 开发了一种独立的方法来确定ABSA子任务的最佳执行序列.
主要成果:
- 在各种条件和领域中,LM-SODP表现出稳定的性能改善.
- 该框架有效地使用优化的提示指令指导具有有限计算资源的LLM.
- 在ABSA预测中实现了减少错误传播和不确定性.
结论:
- LM-SODP提供了一种自动化的方法,用于对ABSA进行快速优化,克服固定预测订单的局限性.
- 该方法提高了LLM在细粒度NLP任务中的效率和有效性.
- 打开了自动提示令牌生成和改进的人类-人工智能交互的新可能性.
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