Heuristics and Biases in AI Decision-Making: Implications for Responsible AGI
This work addresses the problem of cognitive biases in LLMs for AI safety and AGI development, though it is incremental as it evaluates existing models on known biases.
The study investigated cognitive biases in three large language models (GPT-4o, Gemma 2, and Llama 3.1) using 1,500 experiments across nine biases, finding GPT-4o performed best overall while Llama 3.1 underperformed with inconsistencies.
We investigate the presence of cognitive biases in three large language models (LLMs): GPT-4o, Gemma 2, and Llama 3.1. The study uses 1,500 experiments across nine established cognitive biases to evaluate the models' responses and consistency. GPT-4o demonstrated the strongest overall performance. Gemma 2 showed strengths in addressing the sunk cost fallacy and prospect theory, however its performance varied across different biases. Llama 3.1 consistently underperformed, relying on heuristics and exhibiting frequent inconsistencies and contradictions. The findings highlight the challenges of achieving robust and generalizable reasoning in LLMs, and underscore the need for further development to mitigate biases in artificial general intelligence (AGI). The study emphasizes the importance of integrating statistical reasoning and ethical considerations in future AI development.