Forecasting With LLMs: Improved Generalization Through Feature Steering

• URL: http://arxiv.org/abs/2606.27199v1
• Date: Thu, 25 Jun 2026 15:59:20 GMT
• Title: Forecasting With LLMs: Improved Generalization Through Feature Steering
• Abstract summary: We use sparse autoencoders to investigate whether forecasting tasks rely on time-specific pieces of knowledge or generalizable patterns.<n>We find that amplifying time-awareness features substantially reduces look-ahead bias on forecasting prompts while preserving general reasoning performance.<n>These results suggest that interpretable temporal features can be used to causally shift LLMs toward more historically grounded reasoning.
• Score: 2.1485350418225244
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Successful forecasting involves identifying patterns between historical and future states of the world which generalize to future observations. We apply LLMs to a variety of forecasting tasks and inspect their internal states using sparse autoencoders to understand whether they appear to rely on time-specific pieces of knowledge versus generalizable patterns. Our analyses identify features associated with both time-aware reasoning and look-ahead-biased reasoning. We then apply the LLMs to an entirely different domain and intervene on these features. We find that amplifying time-awareness features substantially reduces look-ahead bias on forecasting prompts while preserving general reasoning performance. In contrast, steering the candidate look-ahead-bias features does not produce an effect. These results suggest that interpretable temporal features can be used to causally shift LLMs toward more historically grounded reasoning.

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