Evaluating Gender, Racial, and Age Biases in Large Language Models: A Comparative Analysis of Occupational and Crime Scenarios

Recent advancements in Large Language Models(LLMs) have been notable, yet widespread enterprise adoption remains limited due to various constraints. This paper examines bias in LLMs-a crucial issue affecting their usability, reliability, and fairness. Researchers are developing strategies to mitigate bias, including debiasing layers, specialized reference datasets like Winogender and Winobias, and reinforcement learning with human feedback (RLHF). These techniques have been integrated into the latest LLMs. Our study evaluates gender bias in occupational scenarios and gender, age, and racial bias in crime scenarios across four leading LLMs released in 2024: Gemini 1.5 Pro, Llama 3 70B, Claude 3 Opus, and GPT-4o. Findings reveal that LLMs often depict female characters more frequently than male ones in various occupations, showing a 37% deviation from US BLS data. In crime scenarios, deviations from US FBI data are 54% for gender, 28% for race, and 17% for age. We observe that efforts to reduce gender and racial bias often lead to outcomes that may over-index one sub-class, potentially exacerbating the issue. These results highlight the limitations of current bias mitigation techniques and underscore the need for more effective approaches.

Compositionality and Bounds for Optimal Value Functions in Reinforcement Learning

An agent's ability to reuse solutions to previously solved problems is critical for learning new tasks efficiently. Recent research using composition of value functions in reinforcement learning has shown that agents can utilize solutions of primitive tasks to obtain solutions for exponentially many new tasks. However, previous work has relied on restrictive assumptions on the dynamics, the method of composition, and the structure of reward functions. Here we consider the case of general composition functions without any restrictions on the structure of reward functions, applicable to both deterministic and stochastic dynamics. For this general setup, we provide bounds on the corresponding optimal value functions and characterize the value of corresponding policies. The theoretical results derived lead to improvements in training for both entropy-regularized and standard reinforcement learning, which we validate with numerical simulations.