A Fast and Effective Solution to the Problem of Look-ahead Bias in LLMs

TL;DR

This paper presents a novel inference-time method to mitigate look-ahead bias in large language models used for financial predictions, enabling more accurate backtesting without retraining.

Contribution

Introduces a fast, low-cost approach that adjusts model logits using specialized models to remove bias and semantic knowledge, improving predictive reliability.

Findings

Effectively removes verbatim and semantic knowledge.

Corrects biases in language models.

Outperforms prior bias mitigation methods.

Abstract

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.