论文标题
使用生成模型来利用通道检索开放域问题回答
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
论文作者
论文摘要
事实证明，开放领域问题的生成模型在不诉诸外部知识的情况下具有竞争力。尽管有希望，但这种方法需要使用具有数十亿个参数的型号，而这些参数的训练和查询很昂贵。在本文中，我们研究了这些模型可以从检索文本段落中受益多少，该文本段落可能包含证据。我们获得了关于自然问题和Triviaqa开放基准的最新结果。有趣的是，我们观察到，在增加检索段落的数量时，该方法的性能会大大提高。这证明生成模型擅长汇总和结合多个段落的证据。
Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. While promising, this approach requires to use models with billions of parameters, which are expensive to train and query. In this paper, we investigate how much these models can benefit from retrieving text passages, potentially containing evidence. We obtain state-of-the-art results on the Natural Questions and TriviaQA open benchmarks. Interestingly, we observe that the performance of this method significantly improves when increasing the number of retrieved passages. This is evidence that generative models are good at aggregating and combining evidence from multiple passages.
