SOURCE-LINKED INTELLIGENCE

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

arXiv · AI, language, vision and robotics · article · Sep 19, 2026 · UTC

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

Read original source ↗ Open in workspace

recordType

paper

region

Global