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Retrieval-augmented language models are being increasingly tasked with subjective, contentious, and conflicting queries such as "is aspartame linked to cancer".
Derivation of new readability formulas (automated readability index, fog count and flesch reading ease formula) for Navy enlisted personnel
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Perceptions of internet information credibility
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Social and heuristic approaches to credibility evaluation online
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Reading Wikipedia to answer open-domain questions
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Bing’s top search results contain an alarming amount of disinformation
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Are you convinced? Choosing the more convincing evidence with a siamese network
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Language models are unsupervised multitask learners
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A brief review on search engine optimization
Dushyant Sharma, Rishabh Shukla, Anil Kumar Giri, and Sumit Kumar. 2019 · 2019
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Automatic argument quality assessment – new datasets and methods
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Language models are few-shot learners
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Retrieval augmented language model pre-training
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Dense passage retrieval for open-domain question answering
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Generating clarifying questions for information retrieval
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Trusted source alignment in large language models
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Tinystories: How small can language models be and still speak coherent english?
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PAL: Program-aided language models
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Entity-based knowledge conflicts in question answering
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ACT-1: Transformer for actions
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Textbooks are all you need
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Augmented language models: A survey
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Attacking open-domain question answering by injecting misinformation
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In-context retrieval-augmented language models
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Whose opinions do language models reflect?
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Toolformer: Language models can teach themselves to use tools
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REPLUG: Retrieval-augmented black-box language models
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Answering ambiguous questions with a database of questions, answers, and revisions
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Jian Xie, Kai Zhang, Jiangjie Chen, Renze Lou, and Yu Su. 2023 · 2023
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WizardLM: Empowering large language models to follow complex instructions
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