Mean Average Precision (MAP) and F1-score are metrics used to evaluate retrieval quality in RAG systems, each offering distinct insights. Here’s how they work and when to use them:
MAP measures the average precision of retrieved documents across multiple queries, emphasizing the ranking order of relevant results. For each query, Average Precision (AP) is calculated by averaging precision values at every position where a relevant document appears in the ranked list. MAP then takes the mean of these AP values across all queries. This makes MAP ideal for scenarios where the position of relevant documents matters. For example, in a RAG system that feeds only the top 3 retrieved documents to a generator, MAP highlights whether correct documents appear early in the list. If a medical RAG system retrieves critical research papers, MAP would quantify how reliably the most relevant studies rank higher, ensuring the generator has accurate inputs.
F1-score, on the other hand, balances precision (fraction of retrieved documents that are relevant) and recall (fraction of all relevant documents retrieved). It’s the harmonic mean of the two, making it useful when both false positives (irrelevant documents) and false negatives (missed relevant documents) need equal consideration. For instance, in a legal RAG tool, missing a key precedent (low recall) or including too many irrelevant cases (low precision) could both harm outcomes. F1-score would assess the trade-off between these errors. It’s less sensitive to ranking order, making it suitable when the system processes a fixed number of documents (e.g., top 10) and the focus is on overall relevance rather than their exact order.
When to choose one over the other:
- Use MAP when evaluating ranked lists where higher-ranked relevant documents significantly impact the generator’s output (e.g., chatbots prioritizing the first result).
- Use F1-score when the goal is to balance retrieval completeness and accuracy, such as in systems that aggregate information from multiple documents (e.g., summarizing research findings).
- MAP is better for comparing retrieval algorithms across diverse queries, while F1 is simpler for scenarios where a binary relevance judgment (retrieved/not retrieved) suffices.
In practice, combining both metrics provides a comprehensive view: MAP for ranking quality and F1 for overall relevance balance.