To evaluate whether a retriever is returning relevant information independent of the generator’s performance, focus on isolating metrics and tests that measure retrieval quality directly. Here’s a structured approach:
1. Use Information Retrieval (IR) Metrics with Ground Truth Data The most direct method is to compare the retriever’s output against a labeled dataset where queries are paired with known relevant documents. Metrics like precision@k (percentage of top-k retrieved documents that are relevant), recall@k (proportion of all relevant documents captured in top-k), and Mean Reciprocal Rank (MRR) (ranking quality of the first relevant document) quantify relevance without involving the generator. For example, if a query about "Python list comprehensions" retrieves 3/5 relevant tutorials in the top 5 results, precision@5 is 60%. Datasets like MS MARCO or TREC provide pre-labeled query-document pairs for benchmarking.
2. Human Evaluation of Retrieved Content Human annotators can assess whether retrieved documents address the query’s intent, even if the generator isn’t used. This involves tasks like:
- Relevance scoring: Annotators rate documents on a scale (e.g., 1-5) based on how well they answer the query.
- Aspect coverage: For complex queries (e.g., "steps to optimize SQL queries"), check if retrieved documents cover all sub-topics (indexing, query planning, etc.). This method avoids generator bias but requires effort to ensure annotator consistency, such as using clear guidelines and measuring inter-annotator agreement.
3. Ablation Studies and Controlled Experiments Compare the retriever’s output against baseline systems (e.g., BM25, a traditional keyword-based retriever) or ablated versions (e.g., disabling entity recognition in the retriever). For instance, if a neural retriever consistently outperforms BM25 on precision@10 for medical FAQs, it suggests the retriever itself is effective. Additionally, test with synthetic queries where the ground truth is predefined (e.g., a known document contains the answer). If the retriever fails to surface it, the issue lies in retrieval, not generation.
Key Considerations
- Avoid metrics like end-to-end answer accuracy, which conflate retrieval and generation performance.
- Analyze failure cases: If a retriever returns outdated or off-topic documents for specific queries, address gaps in its training data or ranking logic.
- Monitor diversity (e.g., using entropy metrics) to ensure the retriever isn’t fixating on a narrow subset of relevant content.
By combining automated metrics, human judgment, and controlled comparisons, you can isolate and improve the retriever’s performance independently of the generator.