To leverage QA datasets like TriviaQA or Natural Questions for RAG evaluation, you need to adapt them to assess both retrieval and generation components. These datasets provide question-answer pairs, but RAG requires a retrieval step where relevant documents are fetched from a corpus before generating answers. Here’s how to adapt them:
1. Pair the Dataset with a Document Corpus TriviaQA and Natural Questions include context sources (e.g., web pages or Wikipedia snippets), but these are often insufficient for scalable retrieval. To adapt them, build or use a large document corpus (e.g., Wikipedia) that contains answers to the questions. For example, index Wikipedia articles split into passages and map each question to passages that contain its answer. This ensures the retriever can access the necessary information. If the original dataset’s context is outdated or incomplete, verify that the corpus includes up-to-date or relevant content. For instance, filter questions in Natural Questions to those answerable by your corpus to avoid unfair evaluation.
2. Preprocess Data for Retrieval Compatibility Original QA datasets aren’t structured for retrieval metrics. Modify them by:
- Splitting the corpus into smaller chunks (e.g., 100-word passages) to simulate real-world retrieval.
- Creating ground-truth relevance labels by identifying which passages contain the answer. For example, in TriviaQA, link evidence paragraphs to passages in your corpus using entity matching or manual alignment.
- Ensuring answer diversity is preserved. Some questions require synthesizing information from multiple passages—design retrieval metrics (e.g., recall@k) to account for multi-document answers.
3. Define Evaluation Metrics for Both Stages RAG evaluation requires separate metrics for retrieval and generation:
- Retrieval: Use recall@k (whether the correct passage is in the top-k results) or precision@k (proportion of relevant passages in top-k).
- Generation: Apply QA metrics like exact match (EM) or F1 score between the generated and ground-truth answers. For answers requiring synthesis, use metrics like ROUGE-L to assess overlap.
- End-to-End: Combine retrieval and generation (e.g., “answer recall” measuring if the correct answer appears in retrieved passages).
Example: For Natural Questions, use Wikipedia as the corpus, split into passages. For each question, evaluate if the retriever fetches passages containing the answer’s entities, then check if the generator produces the exact short answer (e.g., a date or name). If the corpus lacks certain answers, exclude those questions or expand the corpus.
By structuring the corpus, preprocessing data, and defining hybrid metrics, you can repurpose these datasets to rigorously evaluate RAG systems.