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cs.CL2026
Forecasting With LLMs: Improved Generalization Through Feature Steering
Humzah Merchant, Bradford Levy
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 foreca…
cs.CL2026
Divergence Decoding: Inference-Time Unlearning via Auxiliary Models
Humzah Merchant, Bradford Levy
Large Language Models (LLMs) frequently memorize sensitive training data thereby creating significant privacy and copyright risks. Addressing these risks, i.e., removing such knowl…
cs.CL2024
BeanCounter: A low-toxicity, large-scale, and open dataset of business-oriented text
Siyan Wang, Bradford Levy
Many of the recent breakthroughs in language modeling have resulted from scaling effectively the same model architecture to larger datasets. In this vein, recent work has highlight…