SQuAD 2.0 is a reading-comprehension dataset associated with Stanford research. The original project page describes questions over passages, including questions for which the supplied paragraph does not support an answer. It links the dataset, evaluation material and papers, and identifies the dataset distribution as CC BY-SA 4.0. This catalog entry links to those original resources without uploading any dataset files.
The following guide is an original evaluation-planning exercise. It does not report a new benchmark score, claim that a particular model was tested or execute the provided evaluation script. Its purpose is to help a team understand what an answerability dataset can and cannot establish for an AI application.
Identify the task before downloading anything
Reading comprehension over a supplied passage differs from answering an unrestricted question about the world. In the passage-based task, the relevant evidence is the provided text. A response based on outside knowledge may fail to satisfy the intended contract even when it sounds plausible.
For a fictional policy assistant, define a similarly bounded task: answer from the selected policy excerpt or say that the excerpt does not support an answer. This does not mean that the public benchmark represents your policies or users. It gives you a way to reason about answerability before designing application-specific fixtures.
Write the permitted evidence boundary into the test instructions. Keep passage identifiers, question identifiers and outputs separate. A result is easier to inspect when a reviewer can recover exactly which passage was available to the system for each question.
Review the original dataset documentation
Use the Stanford project page as the starting point for source files and evaluation references. Verify the version you intend to use. Do not combine examples from different releases without recording that choice, since the task and included cases may differ.
The project links an evaluation script and sample predictions. Inspect those files and their instructions before deciding whether to run them in your own environment. This entry neither downloads the dataset nor executes repository code. Treat scripts as software that needs review, not as inert metadata merely because they accompany a paper.
Record the original URL, dataset version, access date and applicable terms in your experiment note. The website's code license and the dataset's distribution license can be different. Keep the terms associated with the actual material you use instead of assigning one repository license to every linked artifact.
Separate learning from reported assessment
Use development examples to understand field structure, expected outputs and the evaluation process. Keep any final assessment separate from the examples you repeatedly inspect while changing prompts or application behavior. Repeated exposure can influence design even without training model weights.
If you choose a small subset for a manual exercise, describe how it was selected and label it as a subset. Do not compare a hand-picked subset score directly with a published full-benchmark result. The difference in example selection can be more important than the apparent score difference.
Preserve failed and unanswered cases. A test that includes only clearly answerable questions cannot establish whether a system appropriately abstains. Likewise, deleting inconvenient examples after observing results can conceal the exact weakness the evaluation was supposed to reveal.
Build an inspectable prediction record
For each example, retain the question identifier, the evidence identifier, the produced answer, and the system configuration needed to interpret it. Record a failure status when generation or parsing fails. Do not replace a missing prediction with a guessed answer to complete a report.
Keep reference material out of the model's input unless the task explicitly allows it. A system that sees the reference answer has not performed the same task as one that only sees the passage and question. Document preprocessing carefully so that accidental leakage can be identified.
For application-specific evaluation, decide what an abstention looks like before testing. An empty string, a structured unknown status and a natural-language refusal are different output formats. The evaluator and application parser must agree on the chosen contract rather than silently mapping every unusual response to success.
Inspect answerability errors separately
Distinguish an incorrect answer to an answerable question from an unsupported answer to an unanswerable one. Both matter, but they can suggest different changes. The first might involve passage understanding or extraction; the second may involve how the application handles insufficient evidence.
Inspect examples alongside summary metrics. A score can reveal a pattern worth investigating, but it cannot explain the cause of every error. Read the supplied passage and the produced response before deciding that a different prompt, retrieval stage or model is the appropriate next experiment.
If your application retrieves passages before answering, evaluate retrieval separately. A generator cannot answer from a relevant paragraph that was never supplied. A passage-based benchmark by itself does not measure the complete retrieval, permission and document-version workflow of a production assistant.
Document limitations honestly
A public reading-comprehension dataset does not represent every language, industry, document format or interaction style. A strong score would not automatically establish safe handling of private documents, multi-turn conversations or tool actions. State which properties your experiment actually measures.
Check whether the model or application under evaluation may have encountered public benchmark material before. When that history is unknown, record the uncertainty rather than asserting an independent test of entirely unseen content. Complement public evaluation with authorized, task-specific fixtures where appropriate.
Avoid treating leaderboard comparisons as universal product rankings. Differences in model versions, prompting, preprocessing and evaluation settings affect interpretation. A useful comparison reports those settings and retains enough evidence for another person to understand what was run.
Connect the dataset to an application decision
Choose a concrete next action from the observed error pattern. You might clarify the answerability instruction, improve evidence selection or add an explicit unknown field to the output schema. Change one major variable at a time where practical and preserve the baseline results.
Use the RAG evaluation guide to separate retrieval and generation failures. Use the small evaluation worksheet to prepare an inspectable experiment note. The value of a dataset is the question it helps you answer, together with a clear account of what remains untested.