Question answering (Q/A) can be formulated as a generative task (Mitra, 2017) where the task is to generate an answer given the question and the passage (knowledge, if available). Recent advances in QA task is focused a lot on language model advancements and less on other areas such as sampling(Krishna et al., 2021), (Nakano et al., 2021). Keywords play very important role for humans in language generation. (Humans formulate keywords and use grammar to connect those keywords and work). In the research community, very little focus is on how humans generate answers to a question and how this behavior can be incorporated in a language model. In this paper, we want to explore these two areas combined, i.e., how sampling can be to used generate answers which are close to human-like behavior and factually correct. Hence, the type of decoding algorithm we think should be used for Q/A tasks should also depend on the keywords. These keywords can be obtained from the question, passage or internet results. We use knowledge distillation techniques to extract keywords and sample using these extracted keywords on top of vanilla decoding algorithms when formulating the answer to generate a human-like answer. In this paper, we show that our decoding method outperforms most commonly used decoding methods for Q/A task

翻译：问答（Q/A）可被形式化为一项生成任务（Mitra, 2017），其目标是根据给定问题与段落（若有知识可用）生成答案。近年来，问答任务的研究重点主要聚焦于语言模型的进步，而在采样等其他领域则关注较少（Krishna et al., 2021; Nakano et al., 2021）。关键词在人类语言生成中扮演着至关重要的角色（人类通过构思关键词，并运用语法将其连接与组织）。目前研究界鲜有关注人类如何生成问题答案，以及如何将这种行为融入语言模型。本文旨在探索这两个领域的结合点，即如何利用采样生成既贴近人类行为特征又事实准确的答案。因此，我们认为问答任务所使用的解码算法也应依赖于关键词。这些关键词可从问题、段落或互联网结果中获取。我们采用知识蒸馏技术提取关键词，并在基础解码算法之上，利用这些关键词进行采样，以生成类似人类的答案。实验表明，所提出的解码方法在问答任务中优于最常用的解码方法。