CounterRefine: Answer-Conditioned Counterevidence Retrieval for Inference-Time Knowledge Repair in Factual Question Answering
#CounterRefine #counterevidence retrieval #factual question answering #inference-time repair #knowledge correction
📌 Key Takeaways
- CounterRefine is a new method for factual question answering that retrieves counterevidence to correct errors.
- It operates at inference time, allowing real-time knowledge repair without retraining models.
- The approach conditions retrieval on candidate answers to find contradictory evidence efficiently.
- This improves answer accuracy by dynamically addressing factual inconsistencies in responses.
📖 Full Retelling
arXiv:2603.16091v1 Announce Type: cross
Abstract: In factual question answering, many errors are not failures of access but failures of commitment: the system retrieves relevant evidence, yet still settles on the wrong answer. We present CounterRefine, a lightweight inference-time repair layer for retrieval-grounded question answering. CounterRefine first produces a short answer from retrieved evidence, then gathers additional support and conflicting evidence with follow-up queries conditioned
🏷️ Themes
AI Accuracy, Knowledge Correction
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Original Source
arXiv:2603.16091v1 Announce Type: cross
Abstract: In factual question answering, many errors are not failures of access but failures of commitment: the system retrieves relevant evidence, yet still settles on the wrong answer. We present CounterRefine, a lightweight inference-time repair layer for retrieval-grounded question answering. CounterRefine first produces a short answer from retrieved evidence, then gathers additional support and conflicting evidence with follow-up queries conditioned
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