Generative AI has rapidly developed since going mainstream, with new models emerging regularly. Evaluating generative models is more complex than discriminative models due to the challenge of assessing quality, coherence, diversity, and usefulness. Evaluation methods include task-specific metrics, research benchmarks, LLM self-evaluation, and human evaluation. Consistent benchmark evaluation is hindered due to data contamination. Additionally, LLM self-evaluation is sensitive to model choice and prompt, and human evaluation is considered reliable but slow and costly.
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Evaluating Large Language Models
Task-Specific Metrics
Using metrics such as ROUGE for summarization or BLEU for translation to evaluate LLMs allows us to quickly and automatically evaluate large portions of generated text. However, these metrics can capture only certain aspects of language quality and are only suitable for specific tasks. They tend not to work very well for tasks that require an understanding of nuance, style, cultural context, or idiomatic expressions.
Research Benchmarks
These vast sets of questions and answers cover a wide range of topics and allow us to score LLMs against them quickly and cheaply. Unfortunately, they are often contaminated: the benchmark test sets contain the same data that was used in LLM training sets, rendering the benchmarks unreliable as far as measuring the absolute performance is concerned (although they can still be useful to identify general trends or track performance over time).
LLM Self-Evaluation
LLM self-evaluation is fast and easy to implement but might be expensive to run. It’s a good approach when the task of evaluating is easier than the original task itself. Self-evaluation is especially applicable to RAG systems to verify whether the retrieved data is used correctly and efficiently. However, LLM evaluators are quite sensitive to the choice of model and prompt. They are also constrained by the difficulty of the original task: step-by-step reasoning about math problems is not easy to evaluate by an LLM.
Human Evaluation
Arguably the most reliable, but the slowest and most expensive to implement, especially when highly skilled human experts are needed. Attempts to crowsource human evaluation are very interesting, but can only provide model rankings according their general skills. This makes them less useful for task-specific model selection.
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