Krishna discusses the challenges of large language models, particularly their lack of transparency and interpretability. By employing counterfactual prompts, he demonstrates how slight variations in questioning can reveal inconsistencies in model responses, ultimately helping to build trust and robustness in AI systems. This approach mirrors investigative techniques, probing the model's reliability through systematic questioning.
