LLM evaluation

Introduction to LLM Evaluation

LLM evaluation is crucial for understanding the performance and effectiveness of large language models in various applications. The primary purpose of LLM evaluation is to assess how well these models generate human-like text, comprehend context, and perform specific tasks.

However, evaluating LLMs presents several challenges, including the complexity of language, the subjectivity of human judgment, and the need for diverse benchmarks that reflect real-world scenarios. Additionally, the rapid evolution of LLM architectures necessitates continuous updates to evaluation methods.

Despite these challenges, effective LLM evaluation offers significant benefits. It helps researchers and developers identify strengths and weaknesses in models, guides improvements, and ensures that LLMs are reliable and safe for deployment in applications ranging from chatbots to content generation.

Specialised Evaluation Frameworks

Different LLM applications may require specific evaluation approaches:

RAG Evaluation

For Retrieval-Augmented Generation (RAG) systems, the RAGAS framework provides specialised metrics that assess both the quality of retrieved information and the generated responses.