Benchmarking LLMs: Metrics, Challenges, and Best Practices for Evaluation

This conference talk from DevConf.IN 2025 explores the complex challenges of evaluating Large Language Models (LLMs) for enterprise adoption. Presented by Ravindra Patil, the 35-minute session delves into why traditional metrics like perplexity and BLEU scores fall short in assessing LLMs' real-world capabilities. Discover current benchmarking best practices, limitations of existing approaches, and emerging evaluation techniques essential for responsible AI implementation. Explore both qualitative and quantitative metrics across task-specific benchmarks (code generation, summarization) and user-centric evaluations (coherence, creativity, bias detection). Learn how specialized benchmarks test LLMs on ethical and explainability grounds. By the end of the talk, gain valuable insights on selecting LLMs that balance accuracy, efficiency, and fairness, plus understand the improvements in Granite 3.0 that enhance its performance as an LLM.