How do LLM reward functions' language impact fairness and performance?
Multilinguality in LLM-Designed Reward Functions for Restless Bandits: Effects on Task Performance and Fairness
This paper investigates the impact of using different languages, particularly low-resource languages, in prompts for LLMs that design reward functions for Restless Multi-Armed Bandits (RMABs), a type of multi-agent resource allocation problem. It specifically examines how non-English prompts affect both the task performance (how well the allocation aligns with the prompt's goal) and fairness (whether allocations are equitable across demographic groups). Key findings reveal that English prompts yield better reward functions and performance compared to other languages. Furthermore, prompt phrasing and complexity significantly influence the resulting allocations, with explicit phrasing and simpler prompts leading to better outcomes. Low-resource language prompts and complex prompts tend to introduce more unfairness in the allocation. These findings have implications for developing fair and effective LLM-based multi-agent systems, especially in real-world applications like public health resource allocation in multilingual communities.