When experiencing an information need, users want to engage with an expert, but often turn to an information retrieval system, such as a search engine, instead. Classical information retrieval systems do not answer information needs directly, but instead provide references to (hopefully authoritative) answers. Successful question answering systems offer a limited corpus created on-demand by human experts, which is neither timely nor scalable. Large pre-trained language models, by contrast, are capable of directly generating prose that may be responsive to an information need, but at present they are dilettantes rather than experts - they do not have a true understanding of the world, they are prone to hallucinating, and crucially they are incapable of justifying their utterances by referring to supporting documents in the corpus they were trained over. This paper examines how ideas from classical information retrieval and large pre-trained language models can be synthesized and evolved into systems that truly deliver on the promise of expert advice.

中文翻译：

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重新思考搜索：让专家摆脱Dilettantes的困扰

当遇到信息需求时，用户希望与专家互动，但通常会转向信息检索系统，例如搜索引擎。经典的信息检索系统不会直接回答信息需求，而是提供对（希望权威）答案的引用。成功的问答系统可提供由人类专家按需创建的有限语料库，该语料库既不及时也不可扩展。相比之下，大型的经过预先训练的语言模型能够直接生成可能响应信息需求的散文，但是目前它们是离散的，而不是专家的-他们对世界没有真正的了解，因此很容易理解幻觉 至关重要的是，他们无法通过参考经过培训的语料库中的支持文档来证明自己的话语合理。本文探讨了如何将经典信息检索和经过大规模预训练的语言模型中的思想进行合成，并将其演化为真正能够兑现专家建议的系统。