A Fast and Effective Solution to the Problem of Look-ahead Bias in LLMs
This addresses a critical issue for finance practitioners using LLMs, though it is an incremental improvement over existing techniques.
The paper tackles the problem of look-ahead bias in LLMs for financial predictive tasks by introducing a method that adjusts logits at inference time using specialized models, resulting in effective removal of knowledge and outperforming prior methods.
Applying LLMs to predictive tasks in finance is challenging due to look-ahead bias resulting from their training on long time-series data. This precludes the backtests typically employed in finance since retraining frontier models from scratch with a specific knowledge cutoff is prohibitive. In this paper, we introduce a fast, effective, and low-cost alternative. Our method 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 another on information to be retained. We demonstrate that our method effectively removes both verbatim and semantic knowledge, corrects biases, and outperforms prior methods.