Look-Ahead Bias in Large Language Models (LLMs): Implications and Applications in Finance
5 Pages Posted: 10 Jan 2025
Date Written: November 15, 2024
Abstract
Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains, but ensuring their robustness and reliability requires addressing key methodological challenges such as look-ahead bias. This paper discusses the concept of look-ahead bias in LLMs, with particular emphasis on its implications for financial applications. We explore common sources of bias, its manifestations in training and evaluation, and propose strategies to mitigate its effects. Special attention is given to backtesting methodologies, data cutoffs, and the challenges posed by entity embeddings. These mitigation strategies, while computationally and financially expensive, are crucial for accurately evaluating LLM prediction tasks. By addressing these challenges, we aim to enhance the applicability and trustworthiness of LLMs in sequential and time-sensitive tasks.
Keywords: artificial intelligence, large language models, look ahead bias, backtesting
JEL Classification: C45, C53, C58, G17, Q47
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