- Benchmarking systems are essential for verifying the performance of GenAI and LLMs, directing enhancements, and confirming real-world suitability. They assist us in evaluating the efficiency and preparedness of generative AI and LLMs for deployment in production environments.
- The correlation between evaluations and LLMs is a new and emerging area. We should use traditional metrics, LLM task-specific benchmarks, and human evaluation to assess LLM performance and ensure its suitability for real-world applications. G-Eval is a reference-free evaluation method using LLMs to assess the generated text’s coherence, consistency, and relevance.
- Conventional metrics such as BLEU, ROUGE, and BERTScore help measure text generation quality and evaluate text numerically based on n-gram matching or semantic similarity. They do face some challenges in fully representing contextual meaning and paraphrasing.
- LLM-specific benchmarks measure how well LLMs perform tasks such as text classification, sentiment analysis, and question answering. They introduce new metrics such as groundedness, coherence, fluency, and GPT similarity that help assess the quality of LLM outputs and how close they are to human-like standards.
- Effective evaluation methods for meaningful LLM evaluations include testing in relevant settings, creating fair prompts, conducting ethical reviews, and assessing the user experience. These include advanced benchmarks such as HELM, HEIM, HellaSWAG, and MMLU, which test LLMs against various scenarios and capabilities.
- Tools such as Azure AI Studio and the DeepEval framework enable effective LLM evaluations in an enterprise context. These tools allow the development of customized evaluation workflows, batch executions, and the incorporation of real-time evaluations into production settings.