Ensuring the safety of question answering (QA) systems is critical for
deploying them in biomedical and scientific domains. One approach to improving
these systems uses natural language inference (NLI) to determine whether
answers are supported, or entailed, by some background context. However, these
systems are vulnerable to supporting an answer with a source that is wrong or
misleading. Our work proposes a critical approach by selecting answers based on
whether they have been contradicted by some background context. We evaluate
this system on multiple choice and extractive QA and find that while the
contradiction-based systems are competitive with and often better than
entailment-only systems, models that incorporate contradiction, entailment, and
QA model confidence scores together are the best. Based on this result, we
explore unique opportunities for leveraging contradiction-based approaches such
for improving interpretability and selecting better answers.

使用自然语言推理技术和矛盾验证策略来提高问题回答系统的准确性和可靠性在生物医学领域具有重要意义。研究表明使用矛盾验证模型可以在问答选择任务方面取得良好的表现，并且对于解释模型智能性和提高系统可靠性具有潜在意义。