大模型零样本预测易受噪声干扰，效果不如简单专用模型。
Revisiting LLMs as Zero-Shot Time-Series Forecasters: Small Noise Can Break Large Models
- 用提示工程测试大模型零样本时间序列预测能力
- 模型对噪声敏感，准确率低于基础领域模型
- 建议转向微调提升数值序列处理能力
大型语言模型（LLMs）在无需领域特定训练的情况下表现出色，激发了其在时间序列预测中的潜力。尽管已有研究显示仅通过提示即可实现零样本预测，但近期研究指出其内在预测能力有限。为验证这一矛盾结论，本文对比了LLM零样本预测器与先进领域专用模型的性能。实验表明，基于LLM的零样本预测器常因对噪声敏感而难以达到高精度，甚至表现逊于简单的领域专用模型。我们尝试缓解其对噪声的敏感性，但提升鲁棒性仍面临重大挑战。研究建议，与其强调零样本预测，不如聚焦于微调以增强模型处理数值序列的能力。实验代码已开源：https://github.com/junwoopark92/revisiting-LLMs-zeroshot-forecaster。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have shown remarkable performance across diverse tasks without domain-specific training, fueling interest in their potential for time-series forecasting. While LLMs have shown potential in zero-shot forecasting through prompting alone, recent studies suggest that LLMs lack inherent effectiveness in forecasting. Given these conflicting findings, a rigorous validation is essential for drawing reliable conclusions. In this paper, we evaluate the effectiveness of LLMs as zero-shot forecasters compared to state-of-the-art domain-specific models. Our experiments show that LLM-based zero-shot forecasters often struggle to achieve high accuracy due to their sensitivity to noise, underperforming even simple domain-specific models. We have explored solutions to reduce LLMs' sensitivity to noise in the zero-shot setting, but improving their robustness remains a significant challenge. Our findings suggest that rather than emphasizing zero-shot forecasting, a more promising direction would be to focus on fine-tuning LLMs to better process numerical sequences. Our experimental code is available at https://github.com/junwoopark92/revisiting-LLMs-zeroshot-forecaster.
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。