Adversarial Unanswerable Question Detection

via “adversarial unanswerable question generation and validation”

150K reading comprehension questions including unanswerable ones.

Unique: Pioneered adversarial unanswerable questions in QA benchmarks by having crowdworkers explicitly write questions that CANNOT be answered from a passage. This is fundamentally different from randomly sampling unanswerable questions; adversarial construction ensures questions are plausible but genuinely unanswerable.

vs others: More challenging than datasets with random negative examples (e.g., MS MARCO) because adversarial questions require models to understand semantic relevance, not just keyword matching, to distinguish answerable from unanswerable.

via “answerability classification with unanswerable question handling”

307K real Google Search queries answered from Wikipedia.

Unique: Explicitly includes unanswerable questions with labels rather than filtering them out, forcing systems to learn rejection as a valid output rather than always attempting answer extraction

vs others: More realistic than QA benchmarks that only include answerable questions, and directly addresses the hallucination problem that production systems face

via “adversarial no-answer detection via binary classification head”

question-answering model by undefined. 8,99,590 downloads.

Unique: Explicitly trained on SQuAD 2.0's adversarial no-answer examples (human-written questions that appear answerable but have no correct answer in the passage), giving it a specialized capability to reject unanswerable questions rather than extracting incorrect spans. This is a distinct training objective from standard SQuAD 1.1 models.

vs others: More robust to adversarial no-answer cases than BERT-base QA models trained only on SQuAD 1.1, but requires careful threshold tuning and may not generalize to no-answer patterns outside SQuAD 2.0's distribution.

via “squad 2.0 unanswerable question detection”

question-answering model by undefined. 2,87,434 downloads.

Unique: Trained on SQuAD 2.0's adversarial unanswerable questions, learning to distinguish answerable from unanswerable via the same span prediction mechanism rather than a separate binary classifier. This is more parameter-efficient but less explicit than dedicated answerability heads.

vs others: More robust to unanswerable questions than SQuAD 1.1-only models because it was explicitly trained on adversarial non-answers, reducing hallucination on out-of-scope queries.

via “unanswerable question detection”

question-answering model by undefined. 1,45,572 downloads.

Unique: Explicitly trained on SQuAD 2.0's adversarial unanswerable questions (33% of dataset), learning to recognize when context genuinely lacks information rather than defaulting to low-confidence extractions like SQuAD 1.1-only models

vs others: More reliable than post-hoc confidence filtering because the model learned unanswerable patterns during training, rather than relying on threshold heuristics applied to models trained only on answerable questions

question-answering model by undefined. 1,24,380 downloads.

Unique: SQuAD v2 training includes 30% adversarial unanswerable examples written by humans to trick extractive models, enabling robust null prediction vs SQuAD v1 models that assume all questions are answerable

vs others: Provides built-in unanswerable detection without separate classifier, reducing latency vs ensemble approaches; more robust than simple confidence thresholding due to adversarial training

via “unanswerable question detection with confidence scoring”

question-answering model by undefined. 32,657 downloads.

Unique: SQuAD v2 training includes adversarially-written unanswerable questions (plausible but incorrect passages) rather than random negatives, forcing the model to learn semantic mismatch detection. MobileBERT preserves this capability through its [CLS] token 'no answer' head, enabling robust abstention without post-hoc filtering.

vs others: More reliable unanswerable detection than SQuAD v1-only models due to adversarial training data; comparable to full BERT-base but with 5.5x faster inference, making it practical for real-time filtering in retrieval pipelines.

via “unanswerable question detection via confidence thresholding”

question-answering model by undefined. 49,594 downloads.

Unique: Trained on SQuAD v2's explicit unanswerable examples (33% of dataset), enabling the model to learn patterns of when passages lack relevant information, rather than relying on post-hoc confidence thresholding alone — this is baked into the model's learned representations

vs others: More reliable than generic confidence thresholding on SQuAD v2 benchmarks because the model explicitly learned unanswerable patterns; more interpretable than learned rejection classifiers because decisions map directly to span prediction confidence