TempoWave maps scalar observations to multi-wavelet multi-scale digit embeddings that override standard LLM tokens and improve forecasting performance on five context-enriched benchmarks to a new state-of-the-art.
Guiding large language models with divide-and-conquer program for discerning problem solving.arXiv preprint arXiv:2402.05359,
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Reasoning in large output spaces proceeds via shortlisting then fine-grained reasoning; this characterization enables a mechanistic distillation strategy that outperforms standard distillation.
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Speaking Numbers to LLMs: Multi-Wavelet Number Embeddings for Time Series Forecasting
TempoWave maps scalar observations to multi-wavelet multi-scale digit embeddings that override standard LLM tokens and improve forecasting performance on five context-enriched benchmarks to a new state-of-the-art.
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Characterize Then Distill: Mechanistic Reasoning in Large Output Spaces
Reasoning in large output spaces proceeds via shortlisting then fine-grained reasoning; this characterization enables a mechanistic distillation strategy that outperforms standard distillation.