探究提示长度对大模型领域任务表现的影响。
Effects of Prompt Length on Domain-specific Tasks for Large Language Models
- 设计不同长度的提示，测试其对领域任务的影响
- 提示过长或过短都会降低模型准确率
- 适合做提示工程优化的研究者参考
近年来，大型语言模型在机器翻译、问答等自然语言任务中表现出色，展现出强大的泛化能力。然而，在金融情感分析、货币政策理解等需要专业知识和精确推理的领域任务上，其有效性仍存在争议。为提升表现，研究者常通过精心设计提示来引导模型输出。因此，提示工程成为研究重点。尽管模型与提示技术不断进步，提示设计如何影响模型在特定领域任务中的表现仍缺乏系统研究。本文旨在填补这一空白，探索提示长度对领域任务性能的影响。
原文摘要 · Abstract (English)
In recent years, Large Language Models have garnered significant attention for their strong performance in various natural language tasks, such as machine translation and question answering. These models demonstrate an impressive ability to generalize across diverse tasks. However, their effectiveness in tackling domain-specific tasks, such as financial sentiment analysis and monetary policy understanding, remains a topic of debate, as these tasks often require specialized knowledge and precise reasoning. To address such challenges, researchers design various prompts to unlock the models' abilities. By carefully crafting input prompts, researchers can guide these models to produce more accurate responses. Consequently, prompt engineering has become a key focus of study. Despite the advancements in both models and prompt engineering, the relationship between the two-specifically, how prompt design impacts models' ability to perform domain-specific tasks-remains underexplored. This paper aims to bridge this research gap.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。