Unanswerable Question Detection Via Confidence Thresholding

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

via “squad 2.0-compatible unanswerable question detection”

question-answering model by undefined. 1,90,899 downloads.

Unique: Trained on SQuAD 2.0's adversarial unanswerable questions (33% of dataset), learning to predict null spans rather than forcing answers from irrelevant text; uses disentangled attention to better distinguish between answerable and unanswerable contexts

vs others: Achieves 88%+ F1 on SQuAD 2.0 unanswerable detection vs 75-80% for models fine-tuned only on SQuAD 1.1, reducing false-positive answer hallucinations in production systems

via “adversarial unanswerable question detection”

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 “squad-optimized answer confidence scoring”

question-answering model by undefined. 40,750 downloads.

Unique: Fine-tuned on SQuAD 2.0 which explicitly includes unanswerable questions, enabling the model to learn when to assign low confidence rather than forcing an answer. Whole-word masking pre-training improves semantic understanding of question-passage relationships, producing more reliable confidence signals.

vs others: More reliable confidence scores than SQuAD 1.1-only models due to unanswerable question training; less sophisticated than ensemble-based or Bayesian uncertainty methods but requires no additional computation or model modifications.

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

via “squad 2.0-calibrated confidence scoring for unanswerable detection”

question-answering model by undefined. 66,453 downloads.

Unique: Trained on SQuAD 2.0's explicit unanswerable question set, enabling the model to learn when NOT to extract an answer rather than defaulting to the highest-scoring span — a critical distinction from SQuAD 1.1-only models that always force an extraction

vs others: More reliable at rejecting unanswerable questions than SQuAD 1.1-trained models, reducing false-positive answer extractions in production systems by ~15-20% on adversarial test sets