Finding A Voice: Evaluating African American Dialect Generation for Chatbot Technology

• URL: http://arxiv.org/abs/2501.03441v1
• Date: Tue, 07 Jan 2025 00:07:01 GMT
• Title: Finding A Voice: Evaluating African American Dialect Generation for Chatbot Technology
• Authors: Sarah E. Finch, Ellie S. Paek, Sejung Kwon, Ikseon Choi, Jessica Wells, Rasheeta Chandler, Jinho D. Choi,
• Abstract summary: This study investigates the ability of Large Language Models to generate African American Vernacular English (AAVE)<n>We analyze the performance of three LLM families in producing AAVE-like utterances at varying dialect intensities.<n>We find that AAVE-speaking users prefer Standard American English (SAE) chatbots, with higher levels of AAVE correlating with lower ratings for a variety of characteristics.
• Score: 10.286802424882842
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As chatbots become increasingly integrated into everyday tasks, designing systems that accommodate diverse user populations is crucial for fostering trust, engagement, and inclusivity. This study investigates the ability of contemporary Large Language Models (LLMs) to generate African American Vernacular English (AAVE) and evaluates the impact of AAVE usage on user experiences in chatbot applications. We analyze the performance of three LLM families (Llama, GPT, and Claude) in producing AAVE-like utterances at varying dialect intensities and assess user preferences across multiple domains, including healthcare and education. Despite LLMs' proficiency in generating AAVE-like language, findings indicate that AAVE-speaking users prefer Standard American English (SAE) chatbots, with higher levels of AAVE correlating with lower ratings for a variety of characteristics, including chatbot trustworthiness and role appropriateness. These results highlight the complexities of creating inclusive AI systems and underscore the need for further exploration of diversity to enhance human-computer interactions.

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