Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machine learning (2505.11349v1)

Abstract: Recently-developed time series foundation models for scientific machine learning exhibit emergent abilities to predict physical systems. These abilities include zero-shot forecasting, in which a model forecasts future states of a system given only a short trajectory as context. Here, we show that foundation models applied to physical systems can give accurate predictions, but that they fail to develop meaningful representations of the underlying physics. Instead, foundation models often forecast by context parroting, a simple zero-shot forecasting strategy that copies directly from the context. As a result, a naive direct context parroting model scores higher than state-of-the-art time-series foundation models on predicting a diverse range of dynamical systems, at a tiny fraction of the computational cost. We draw a parallel between context parroting and induction heads, which explains why LLMs trained on text can be repurposed for time series forecasting. Our dynamical systems perspective also ties the scaling between forecast accuracy and context length to the fractal dimension of the attractor, providing insight into the previously observed in-context neural scaling laws. Context parroting thus serves as a simple but tough-to-beat baseline for future time-series foundation models and can help identify in-context learning strategies beyond parroting.

Understanding Context Parroting in Scientific Machine Learning

The paper authored by Yuanzhao Zhang and William Gilpin introduces "context parroting" as a baseline strategy pertinent to forecasting dynamical systems using foundation models. This strategy is presented as straightforward yet challenging to outperform, particularly with time-series foundation models that have been utilized recently for scientific machine learning (SciML). The paper evaluates the efficacy of foundation models designed for zero-shot forecasting, whereby models predict future states given only brief trajectories as context, revealing crucial insights into how these models operate and their core limitations.

Overview of the Proposed Approach

Context parroting is identified as a primary mechanism employed by foundation models when engaged in zero-shot forecasts of chaotic systems. The authors demonstrate that this simplistic approach significantly surpasses state-of-the-art time-series foundation models in forecasting accuracy, despite demanding relatively trivial computational resources. Specifically, context parroting involves identifying near-repeating motifs within a dataset's context and subsequently predicting future states by copying the sequences succeeding those motifs. This approach is akin to an "in-context nearest neighbor" algorithm, which bypasses the need for extensive pre-training, constituting zero trainable parameters.

Benchmarking Against Foundation Models

The paper benchmarks context parroting against three foundation models: Chronos, TimesFM, and TimeMoE. Interestingly, context parroting demonstrates superior performance in forecasting chaotic systems, indicating that foundation models often do not extract substantive insights into the underlying physical processes. The authors attribute the observed superior performance primarily to benchmark tasks solved by repetition.

The datasets utilized for evaluation comprise 135 low-dimensional chaotic systems, annotated by their largest Lyapunov exponent—a metric quantifying the systems' inherent chaotic nature. Despite the foundation models being trained on vast datasets, context parroting achieves better prediction accuracy with a negligible inference cost compared to these models, emphasizing its computational efficiency.

Implications of Context Parroting

From a theoretical perspective, the paper provides insight into the neural scaling law associated with context lengths, linking the scaling coefficient to the fractal dimension of chaotic attractors. This forms a geometric basis for understanding how increased context data aids in improving forecast precision, elucidating the performance scalability with context length as directly derivative of the system's correlation dimension.

Practically, context parroting provides a fundamental benchmark that can spur the development of more sophisticated foundation models. It exposes a need for time-series foundation models to evolve and integrate learning strategies that surpass simplistic parroting. The proposition of context parroting lays the groundwork for investigating zero-shot learning strategies beyond parroting, fostering advancements in machine learning model architectures tailored to scientific applications.

Future Directions for Research

Looking forward, embedding non-stationary features into the parroting methodology may potentiate advancements in more challenging domains within scientific forecasting. Additionally, exploring dynamic embedding dimensions could offer adaptive improvements contingent on context length and system complexity. The results press for a comprehensive exploration into baselines reflecting the complexity and variability of real-world applications.

In conclusion, the insights presented by Zhang and Gilpin importantly relate the performance of foundation models to geometrical and dynamics-based attributes. The paper proposes context parroting as an indispensable baseline for gauging more advanced forecasting techniques, challenging the status quo in SciML and advocating for a deeper understanding of AI mechanisms in chaotic prediction systems.