3 accepted papers
Large Language Models (LLMs) frequently memorize sensitive training data, creating significant privacy and copyright risks. We present a novel unlearning framework rooted in the principle that learning is easier than forgetting. We first introduce \textbf{Divergence Decoding (DD)}, a mechanism that …
Large Language Models (LLMs) are increasingly integrated into financial workflows, but evaluation practice has not kept up. Finance-specific biases can inflate performance, contaminate backtests, and make reported results useless for any deployment claim. We identify five recurring biases in financi…
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 highlighted performance gains from increasing training dataset size and quality, suggesting a need for novel sources of large-sca…