Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model Bias

Exploring the Impact of Pre-training Data on LLM Biases in Healthcare

Introduction

LLMs have made significant strides in NLP applications. However, as these models are increasingly used in high-stakes fields like healthcare, the integrity and reliability of their outputs become crucial. This article explore how biases embedded in the pre-training data of LLMs can skew their understanding and representation of disease prevalence across different demographic groups.

The Challenge of Bias in LLMs

LLMs are trained on vast corpora of text data called pre-training datasets. While these models have shown remarkable language understanding capabilities, they are not immune to inheriting biases present in their training data. Such biases are particularly problematic in healthcare applications, where misrepresentations can lead to unequal or inadequate care delivery.

• Core Issue: The study focuses on how biases in pre-training datasets, especially concerning demographic data related to diseases, affect the LLMs' output.
• Tools and Methods: The researchers employed co-occurrence analysis, benchmarking against real-world disease prevalences, and analysis of logits produced by various LLM configurations.

Investigative Approach and Findings

The research team developed a benchmarking framework called Cross-Care. This framework evaluates discrepancies between the disease prevalence data encoded in LLMs against actual disease statistics from varied U.S. demographic groups.

Key Techniques Used:

• Analyzing Co-Occurrences: They quantitatively analyzed the frequency of mentions of disease and demographic group pairs in training datasets.
• Logits Analysis: The team evaluated how these biases influenced the LLMs' outputs by examining the logits from various model configurations.
• Comparison with Real-World Data: They benchmarked these outputs against epidemiological data from the U.S. to confirm the discrepancies in disease representation.

Significant Outcomes:

• The study found substantial mismatches between disease representations in LLMs and true disease prevalences, suggesting deep-seated biases.
• Alignment methods, designed to adjust model outputs, had minimal effect on correcting these discrepancies.

Tools for Exploration

The researchers have also developed a toolkit and a web application, available at www.crosscare.net, which allows further exploration of their datasets and findings. This tool is aimed at fostering further research and understanding of bias in healthcare-oriented LLMs.

Implications and Future Directions

Theoretical Implications:

• The findings highlight the need for more sophisticated methods for bias identification and correction in LLMs, particularly in sensitive domains like healthcare.

Practical Implications:

• For practitioners and stakeholders in healthcare, this study advises caution in deploying LLMs for clinical decision support without rigorous bias mitigation strategies.

Future Research:

• There is a clear avenue for future work to develop more effective techniques for de-biasing and to extend these methodologies to more languages and demographic categories.

Concluding Thoughts

This study provides a crucial look at the biases of LLMs in the context of healthcare. It underscores the importance of integrating robust, domain-specific data handling practices into the development of LLMs to ensure they deliver equitable and reliable support across all demographic groups. The continued exploration and mitigation of bias is essential to harness the full potential of LLMs in improving healthcare outcomes globally.