Suppose you are building an open-domain QA system for a cons…
Questions
Suppоse yоu аre building аn оpen-domаin QA system for a consumer health website. The health website’s users type natural-language health questions such as "What are the early symptoms of type 2 diabetes?" or "Is ibuprofen safe to take with blood thinners?". Your knowledge base is a corpus of approximately 2 million paragraphs drawn from publicly available medical encyclopedias, clinical guidelines, and patient-education pages. The corpus is updated quarterly with new and revised articles. You plan to use a Retriever-Reader architecture. For the retriever, you are considering a sparse approach (e.g., BM25/TF-IDF) and a dense approach (e.g., DPR). For the reader, you will fine-tune a BERT-based model. Question Part 1: Health questions often use everyday language that differs from clinical terminology in the documents. Discuss the pros and cons of using sparse retrieval versus dense retrieval for this setting. (One pro and one con per method should be sufficient.) Question Part 2: Because the corpus is updated quarterly, some older passages may contain outdated information. Name a specific method at the retrieval, re-ranking, or reader stage that would help the system prefer up-to-date information, and explain where in the pipeline it would be applied. (One or two sentences should be sufficient.)
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