Our Research On Responsible Generative AI

Evaluating LLMs requires objective, scalable, and efficient methods. Using an Agent as a Judge, AI-driven evaluators assess model responses based on accuracy, coherence, and relevance. This automated approach ensures consistency, reducing human bias while enabling large-scale comparisons. The agent scores outputs against predefined benchmarks or gold-standard answers, providing insights into model performance. By leveraging reinforcement learning, these AI judges continuously refine evaluation criteria. This method accelerates LLM development, ensuring robust, fair, and transparent assessments. Organizations can adopt agent-based evaluation for real-time monitoring, enhancing the reliability of AI-generated content across industries.

 Mechanistic interpretability aims to understand how Large Language Models (LLMs) generate outputs by dissecting their internal computations. Using an Agent as a Judge, AI-driven evaluators analyze model activations, attention patterns, and neuron interactions to assess reasoning transparency. This approach automates evaluations, ensuring objective insights into whether the model follows logical pathways or exhibits hidden biases. By benchmarking interpretability metrics, the agent detects anomalies and refines model explanations. Organizations leveraging agent-based evaluation enhance trust in AI systems, ensuring more reliable and ethical deployment. This method advances AI safety by making LLM decision-making more transparent and interpretable.