Adjusting a large model's logits at inference with a pair of smaller specialized models removes look-ahead bias in financial prediction without retraining the frontier model
Addressing the look-ahead bias that arises when LLMs are applied to financial predictive tasks because they were trained on long time-series data, and the prohibitive cost of retraining frontier models from scratch with a specific knowledge cutoff, the work introduces a fast, effective, and low-cost alternative that guides generation at inference time by adjusting the logits of a large base model using a pair of smaller specialized models -- one fine-tuned on information to be forgotten and one on information to be retained -- and reports that the method effectively removes both verbatim and semantic knowledge, corrects biases, and outperforms prior methods.