Rethinking search
| Autor: | Dara Bahri, Marc Najork, Donald Metzler, Yi Tay |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language Computer science Information needs 02 engineering and technology Computer Science - Information Retrieval Management Information Systems Domain (software engineering) World Wide Web Subject-matter expert Search engine Hardware and Architecture Hallucinating 020204 information systems Scalability 0202 electrical engineering electronic engineering information engineering Question answering 020201 artificial intelligence & image processing Language model Computation and Language (cs.CL) Information Retrieval (cs.IR) |
| Zdroj: | ACM SIGIR Forum. 55:1-27 |
| ISSN: | 0163-5840 |
| Popis: | When experiencing an information need, users want to engage with a domain 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. 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 domain 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 pre-trained language models can be synthesized and evolved into systems that truly deliver on the promise of domain expert advice. |
| Databáze: | OpenAIRE |
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