How can one leverage existing QA datasets like TriviaQA or Natural Questions for RAG evaluation, and what modifications are needed to adapt them to a retrieval setting?

To leverage QA datasets like TriviaQA or Natural Questions for evaluating Retrieval-Augmented Generation (RAG) systems, you need to align their structure with the retrieval and generation pipeline. These datasets typically include questions, answers, and supporting context (e.g., Wikipedia passages). For RAG evaluation, the goal is to test both the retriever’s ability to find relevant documents and the generator’s ability to produce accurate answers from them. For example, TriviaQA provides question-answer pairs with evidence passages, which can be used as ground truth for retrieval relevance (e.g., checking if the retriever surfaces those passages) and answer correctness (e.g., verifying if the generator produces the correct answer from retrieved text). However, the original datasets may not directly map to a retrieval setting because they assume access to pre-extracted evidence rather than a large-scale corpus.

Modifications are required to adapt these datasets to a retrieval context. First, you need to build or align a document corpus that matches the scope of the dataset. For instance, if using Natural Questions (which includes Wikipedia-based answers), you might index Wikipedia dumps as the retrieval corpus. Next, you must ensure the dataset’s ground-truth answers are traceable to specific documents in your corpus. This might involve preprocessing the corpus to include document IDs or metadata that link answers to their source passages. Additionally, the original datasets often include multiple correct answers or paraphrased versions, so you may need to normalize answers or expand ground-truth matches to account for variations. For example, TriviaQA’s answers might include aliases or alternative phrasings that should be considered valid during evaluation.

A practical example involves reformatting the dataset to separate retrieval and generation evaluation. For retrieval, you can measure metrics like recall@k (whether the ground-truth passage is in the top-k retrieved documents) or mean reciprocal rank (MRR). For generation, you can use exact match or F1 scores between the generated answer and the ground truth. For instance, with Natural Questions, you might first run the retriever on a Wikipedia index to find candidate passages for each question, then use the generator to produce an answer. If the original dataset includes short answers (e.g., “Barack Obama”), you might filter the corpus to ensure those entities exist in the indexed documents. If the corpus differs from the dataset’s original sources (e.g., using a newer Wikipedia dump), you may need to update or verify answer relevance to avoid false negatives due to outdated information. These steps ensure the dataset effectively tests both retrieval accuracy and answer quality in a RAG setup.