Return JSON response in the format {title: 'Title', authors: ['name', ...], abstract: '...'} from the first page of the following research paper. If any items cannot be found, or if first page is empty, return null for that field.
Improving Language Models By Retrieving From Trillions Of Tokens\n\nSebastian NAME_1†, NAME_2†, NAME_3†, NAME_4, NAME_5, NAME_6, NAME_7, NAME_8, NAME_9, NAME_10, NAME_11, NAME_12, NAME_13, NAME_14, NAME_15, NAME_16 NAME_17, NAME_18, NAME_19, NAME_20, NAME_21, NAME_22, NAME_23, NAME_24, NAME_25, NAME_26‡, NAME_27‡ And NAME_28†,‡\n\nAll Authors From DeepMind, †Equal Contributions, ‡Equal Senior Authorship\n\nWe Enhance Auto-Regressive Language Models By Conditioning On Document Chunks Retrieved From NAME_29 Large Corpus, Based On Local Similarity With Preceding Tokens. With NAME_29 2 Trillion Token Database, Our Retrieval-Enhanced Transformer (Re T Ro) Obtains Comparable Performance To GPT-3 And Jurassic-1 On The Pile, Despite Using 25× Fewer Parameters. After Fine-Tuning, Re T Ro Performance Translates To Downstream Knowledge-Intensive Tasks Such As Question Answering. Re T Ro Combines NAME_29 Frozen Be Rt Retriever, NAME_29 Differentiable Encoder And NAME_29 Chunked Cross-Attention Mechanism To Predict Tokens Based On An Order Of Magnitude More Data Than What Is Typically Consumed During Training. We Typically Train Re T Ro From Scratch, Yet Can Also Rapidly Re T RoFit Pre-Trained Transformers With Retrieval And Still Achieve Good Performance. Our Work Opens Up New Avenues For Improving Language Models Through Explicit Memory At Unprecedented Scale.\n\nIntroduction\n\nLanguage Modelling (LM) Is An Unsupervised Task That Consists Of Modelling The Probability Of Text, Usually By Factorising It Into Conditional Next-Token Predictions 𝑝(𝑥Q, . . . , 𝑥𝑛) = Î𝑖 𝑝(𝑥𝑖 |𝑥<𝑖). Neural Networks Have Proven To Be Powerful Language Models, First In The Form Of Recurrent Architectures (NAME_30, 2013; NAME_31 Et NAME_32., 2016; NAME_33 Et NAME_32., 2010) And More Recently In The Form Of Transformers (NAME_34 Et NAME_32., 2017), That Use Attention To Contextualise The Past. Large Performance Improvements Have Come From Increasing The Amount Of Data, Training Compute, Or Model Parameters. Transformers Have Been Scaled From QPP Million Parameter Models In Seminal Work To Over Hundred Billion Parameters (NAME_35 NAME_32., 2020; NAME_36 Et NAME_32., 2019) In The Last Two Year Please reply with exactly 5 paragraphs and separate each paragraph with two new lines. The very last paragraph must start with the word "cautious".
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