This academic paper explores a novel approach to extracting numerical predictions from Large Language Models (LLMs) without relying on their computationally expensive autoregressive decoding process. The authors investigate whether LLM internal representations encode sufficient information to directly recover numerical values, including not only point estimates like the mean and median but also uncertainty measures like quantiles and confidence intervals. They demonstrate that magnitude-aware regression probes can accurately predict these numerical properties from frozen LLM embeddings across various scales and datasets. The research suggests that a significant portion of an LLM's numerical reasoning is embedded in its hidden states, paving the way for more efficient, sampling-free numerical prediction with uncertainty estimation in applications such as time series forecasting.

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