Beyond Similarity: Coverage-Aware Prompt Selection for Time Series Forecasting with LLMs

2026-09-19

AI Summary

研究提出CASP-LLM框架，通过结合使用跟踪和饱和门技术来解决基于相似性的提示选择偏差问题，改进了时间序列预测中的覆盖率。

Abstract

Similarity-based retrieval is the dominant rule for conditioning large language models (LLMs) in in-context learning, retrieval-augmented generation, and prompt-based time series forecasting. The rule concentrates on near-duplicate candidates, an issue that has motivated diversity-aware retrieval but remains unexamined in other retrieval-conditioned pipelines. We study this issue using prompt-based time series forecasting as a test bed, where a learned prompt pool is retrieved by similarity. Dominant methods in this setting retrieve top-K entries by cosine similarity without redundancy control, producing a bias toward dominant temporal patterns while overlooking rare but informative events. We propose CASP-LLM, a coverage-aware semantic prompting framework that addresses this prompt selection bias by combining usage-tracking and saturating-gate techniques into a coverage regularizer that adds no learnable parameters. On six long-term benchmarks and the M4 short-term benchmark, CASP-LLM matches or improves on similarity-based LLM forecasters on most dataset-horizon settings, with the exceptions of Electricity, M4-Monthly, and the few-shot long-horizon setting. A controlled study locates the failure mode at the cross-batch usage level rather than per-retrieval redundancy: within-retrieval diversification such as MMR does not help, whereas regularizing anchor usage across training does.

Problem

• Similarity-based Retrieval
• Time Series Forecasting
• Prompt Selection Bias

Innovation

• coverage-aware semantic prompting
• usage-tracking
• saturating-gate techniques
• redundancy control