Year: 2024
Type: article
Abstract: ABSTRACT To examine whether integrating large language models (LLMs) into library reference services can provide equitable services to users regardless of gender and race, we simulated interactions using names indicative of gender and race to evaluate biases across three different sizes of the Llama 2 model. Tentative results indicated that gender test accuracy (54.9%) and racial bias test accuracy (28.5%) are approximately at chance level, suggesting LLM‐powered reference services can provide equitable services. However, word frequency analysis showed some slight differences in language use across gender and race groups. Model size analysis showed that biases did not decrease as model size increased. These tentative results highlight a positive outlook on integrating LLMs into reference services, while underscoring the need for cautious AI integration and ongoing bias monitoring.
DOI: 10.1002/pra2.1061
Authors: Jieli Liu, Haining Wang
Institution: Indiana University Bloomington
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4403433231