Vector-Injected In-Context Learning Enhances Forecasting with Frozen Large Language Models

Global: Vector-Injected In-Context Learning Enhances Forecasting with Frozen Large Language Models

Researchers have introduced a new method called LVICL that improves time series forecasting performance while keeping all parameters of a large language model (LLM) frozen, thereby reducing computational overhead. The approach was detailed in a recent arXiv preprint and aims to address the dual challenges of prediction accuracy and resource consumption in LLM‑based forecasting.

Background

Time series forecasting (TSF) is essential for adapting web services to evolving user behavior. In recent years, large language models have been applied to TSF tasks, achieving notable results despite being originally trained on textual corpora rather than numeric sequences.

Problem Statement

The mismatch between pretraining data and time‑series inputs can degrade forecasting quality when LLMs are used directly. Fine‑tuning the models often restores performance but incurs substantial computational costs, creating a trade‑off between accuracy and efficiency.

Proposed Approach: LVICL

LVICL builds on the concept of in‑context learning by introducing a learnable context‑vector adapter that extracts a compact representation from multiple example series. This vector is then injected into every layer of the frozen LLM, prompting the model to leverage example‑related information without altering its original weights.

Mechanics of Vector Injection

During the forward pass, the context vector is added to the activations of each transformer layer, effectively guiding the model’s attention toward patterns relevant to the forecasting task. Because the LLM’s parameters remain unchanged, the method avoids the heavy compute typically associated with full model fine‑tuning.

Advantages Over Conventional In‑Context Learning

Unlike traditional in‑context learning, which extends the prompt length with raw examples, LVICL maintains a fixed prompt size. The adaptive extraction of a context vector also filters out components of the examples that could be detrimental to forecasting accuracy, leading to more reliable predictions.

Experimental Evidence

The authors report extensive experiments that demonstrate LVICL’s superiority over baseline approaches, achieving higher forecasting accuracy while keeping computational demands low. Specific metrics and datasets were not detailed in the abstract, but the results are described as “effective.”

Implications and Future Work

If validated across broader benchmarks, LVICL could enable more sustainable deployment of LLMs for real‑time forecasting in web services, finance, and IoT applications. Future research may explore scaling the technique to larger models and integrating it with other domain‑specific adaptation strategies.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.