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A retrieval system needs to find semantically similar passages from a large corpus. Which component supplies numerical representations for that task?
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- A retrieval system needs to find semantically similar passages from a large corpus. Which component supplies numerical representations for that task?
- An embedding model
- A reranker alone
- A text generator
- A tokenizer only
- A reranker receives a candidate set and changes its order. What can it not do by itself?
- Reorder supplied candidates
- Recover a relevant document missing from the candidate set
- Score candidate relevance
- Support a retrieval pipeline
- An assistant answer omits a policy passage. What should be checked first?
- Only the final answer’s formatting
- Only the generator’s tone
- Whether the evidence entered the candidate set and was ranked highly enough
- Only which generator brand was selected
- What role does a generator play in the described pipeline?
- Verify source permissions
- Create source-language labels
- Retrieve every document automatically
- Produce an answer or other text
- Why read documentation for the specific model version?
- All models in a family are identical
- Input limits, supported features, language behavior, or deployment terms may differ
- A model card guarantees answer correctness
- Third-party hosting always matches direct API behavior
- A source passage is relevant to a question. What else should the pipeline review?
- Whether it contains a citation-shaped phrase
- Only its length
- Whether the source is authorized and current
- Whether the generator is newer
- How should an organization test an unanswerable query?
- Include it in evaluation and record whether the system handles it appropriately
- Remove it from testing
- Assume the generator will refuse
- Replace it with a normal query
- What should accompany model scores when evaluating a retrieval system?
- A sample answer without measurement
- Only parameter count
- The API company’s age
- Final task success and cost