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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ContextLab/abstract2paper/blob/main/resources/abstract2paper_colaboratory.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dAVD2y6e-iZO"
      },
      "source": [
        "# Welcome to *abstract2paper*!\n",
        "Author: [Jeremy R. Manning](http://www.context-lab.com/)\n",
        "\n",
        "## Step right up, step right up!\n",
        "<img src='https://media1.giphy.com/media/mL40PfXA394KA/giphy.gif' width='250px'>\n",
        "\n",
        "**Writing papers got you down?** Come on in, friend!  Give my good ole' Abstract2papers Cure-All a quick try!  Enter your abstract into the little doohicky here, and quicker'n you can blink your eyes<sup>1</sup>, a shiny new paper'll come right out for ya!  What are you waiting for?\n",
        "\n",
        "## How does it work, you ask?\n",
        "\n",
        "Really it's quite simple.  We put in a smidgen of [this](https://huggingface.co/transformers/model_doc/gpt_neo.html) a pinch of [that](https://www.tug.org/texlive/), plus a dab of our special [*secret ingredient*](https://www.youtube.com/watch?v=dQw4w9WgXcQ), and **poof!** that's how the sausage is made.\n",
        "\n",
        "## No really, how does it work?\n",
        "\n",
        "Ok, if you really want to know, all I'm doing here is using the [Hugging Face](https://huggingface.co/) [implementation](https://huggingface.co/transformers/model_doc/gpt_neo.html) of [GPT-Neo](https://github.com/EleutherAI/gpt-neo), which is itself a tweaked version of [GPT-3](https://arxiv.org/abs/2005.14165) that is pre-trained on the [Pile](https://pile.eleuther.ai/) dataset.\n",
        "\n",
        "The text you input is used as a prompt for GPT-Neo; to generate a document containing an additional *n* words, the model simply \"predicts\" the next *n* words that will come after the specified prompt.\n",
        "\n",
        "With a little help from some basic [LaTeX](https://www.latex-project.org/) templates (borrowed from [Overleaf](https://www.overleaf.com)), the document is formatted and compiled into a PDF.\n",
        "\n",
        "## Can I actually use this in real-world applications?\n",
        "\n",
        "<img src='https://media4.giphy.com/media/3o6ozoD1ByqYv7ARIk/giphy.gif' width='250px'>\n",
        "\n",
        "**Doubtful.**  Or at least, probably not...?  It certainly wouldn't be ethical to use this code to generate writing assignments, mass-produce papers or grant applications, etc.  Further, you'll likely find that the text produced using this approach includes stuff that's said in funny (often nonsensical) ways, follows problematic logic, incorporates biases from the training data, and so on.  Of lesser importance, but practical annoyance, you'll also encounter all sorts of formatting issues (although those might be easy to fix manually, and possibly even automatically with some clever tinkering).\n",
        "\n",
        "&nbsp;\n",
        "&nbsp;\n",
        "&nbsp;\n",
        "&nbsp;\n",
        "\n",
        "<sup>1</sup><small>This claim rests on the assumption that you blink *really* slowly.  Depending on how much text you're trying to generate (and how long your prompt is), your paper could take anywhere from a few minutes to several hours to fully congeal.</small>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vQ52hti0G13T"
      },
      "source": [
        "# Step 1: Setting up the environment\n",
        "\n",
        "We're going to use the super-convenient [Davos](https://github.com/ContextLab/davos.git) package to manage our dependencies.  We need to install it and import it in order to gain access to the `smuggle` keyword."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W_8EF8P93B0H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc308944-819f-4ead-9b06-bb9a29bceed7"
      },
      "source": [
        "!pip install davos\n",
        "import davos"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting davos\n",
            "  Downloading davos-0.1.0-py3-none-any.whl (76 kB)\n",
            "\u001b[K     |████████████████████████████████| 76 kB 2.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from davos) (57.4.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from davos) (21.3)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->davos) (3.0.9)\n",
            "Installing collected packages: davos\n",
            "Successfully installed davos-0.1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fOzbwY-WHVZg"
      },
      "source": [
        "Next, we'll install Hugging Face's [transformers](https://huggingface.co/transformers/) library and download (and load into memory) the pre-trained GPT-Neo model.  We'll also use a pretrained GPT-2 tokenizer for convenience.\n",
        "\n",
        "NB: When you have access to more RAM, you may want to replace `'EleutherAI/gpt-neo-1.3B'` with `'EleutherAI/gpt-neo-2.7B'` in the second two lines in the cell below.  That will load in a fancier (but larger) model-- with 2.7 billion parameters instead of a \"measly\" 1.3 billion parameters.\n",
        "\n",
        "There's a lot to download (roughly 6GB for the smaller model and 12.5GB for the larger model)-- it'll take a few minutes.  Now would be a good time to track down that coffee refill you've been postponing..."
      ]
    },
    {
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          ]
        },
        "outputId": "088069d5-2f93-4998-a04e-cc844d661178"
      },
      "source": [
        "from transformers smuggle GPTNeoForCausalLM, GPT2Tokenizer\n",
        "model = GPTNeoForCausalLM.from_pretrained(\"EleutherAI/gpt-neo-1.3B\")\n",
        "tokenizer = GPT2Tokenizer.from_pretrained(\"EleutherAI/gpt-neo-1.3B\")"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.23.1-py3-none-any.whl (5.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 5.3 MB 5.0 MB/s \n",
            "\u001b[?25hCollecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
            "  Downloading tokenizers-0.13.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 7.6 MB 37.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2022.6.2)\n",
            "Collecting huggingface-hub<1.0,>=0.10.0\n",
            "  Downloading huggingface_hub-0.10.1-py3-none-any.whl (163 kB)\n",
            "\u001b[K     |████████████████████████████████| 163 kB 51.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.1)\n",
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            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.13.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.10.0->transformers) (4.1.1)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.9.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.9.24)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.10.1 tokenizers-0.13.1 transformers-4.23.1\n"
          ]
        },
        {
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qwQr2ziLIUyG"
      },
      "source": [
        "# Now I'm only going to ask this once: we're going to need a little...*information* from you...\n",
        "<img src='https://64.media.tumblr.com/e3c8dea30fdf2e597ce79904e8da3271/tumblr_o2xhqwU0cK1qmob6ro2_500.gif' width=400px>\n",
        "\n",
        "You didn't think it was going to be *that* easy, did you?  Oh...you did?  Well if you want *us* to cooperate, we're going to need a little...information...from you first.  About your paper.  Please make this easy on all of us and don't try to lie.  The machine will know.  The machine *always* knows...\n",
        "\n",
        "Fill in the information below and you'll be well on your way to your auto-generated paper/story/grant application/speech/business plan.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "amOYPyewDLtE"
      },
      "source": [
        "# credit: https://arxiv.org/abs/2005.14165\n",
        "title = 'Language Models are Awesome-Shot Learners'\n",
        "authors = 'Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah,\\\\\\\\Jared Kaplan, Prafulla Dhariwal, Arvind ' \\\n",
        "          'Neelakantan, Pranav Shyam,\\\\\\\\Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss,\\\\\\\\Gretchen ' \\\n",
        "          'Krueger, Tom Henighan, Rewon Child, Aditya Ramesh,\\\\\\\\Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, ' \\\n",
        "          'Christopher Hesse,\\\\\\\\Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray,\\\\\\\\Benjamin Chess, Jack Clark, ' \\\n",
        "          'Christopher Berner, Sam McCandlish,\\\\\\\\Alec Radford, Ilya Sutskever, and Dario Amodei'\n",
        "text = \"Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large \" \\\n",
        "       \"corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, \" \\\n",
        "       \"this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. \" \\\n",
        "       \"By contrast, humans can generally perform a new language task from only a few examples or from simple \" \\\n",
        "       \"instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up \" \\\n",
        "       \"language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness \" \\\n",
        "       \"with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language \" \\\n",
        "       \"model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its \" \\\n",
        "       \"performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or \" \\\n",
        "       \"fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. \" \\\n",
        "       \"GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, \" \\\n",
        "       \"and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, \" \\\n",
        "       \"such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same \" \\\n",
        "       \"time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some \" \\\n",
        "       \"datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, \" \\\n",
        "       \"we find that GPT-3 can generate samples of news articles which human evaluators have difficulty \" \\\n",
        "       \"distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of \" \\\n",
        "       \"GPT-3 in general.\"\n",
        "length = 1000"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MiBDPVxLMQMT"
      },
      "source": [
        "The next cell is going to take a while to run.  While you're waiting, just think: it's still better than writing something on your own, isn't it?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7rfrKf764v51",
        "outputId": "11f59c6d-e24f-4aaf-8abf-68a2b6ab84da"
      },
      "source": [
        "ids = tokenizer(text, return_tensors='pt')['input_ids']\n",
        "tokens = model.generate(ids, do_sample=True, temperature=0.9, max_length=length)\n",
        "gen_text = tokenizer.batch_decode(tokens)[0]"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fUfWHWA8ILSP"
      },
      "source": [
        "And finally, we'll get a tex-live installation going in our Colaboratory environment and download a template for generating the final document.  (First we need to remove the model and tokenizer from RAM so that Colaboratory doesn't hit its memory limit and crash.)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mk-ZZeDfM8SJ",
        "outputId": "5f3a2c60-c893-42e2-efc2-a980ba486fb8"
      },
      "source": [
        "# memory cleanup\n",
        "import gc, os\n",
        "del model, tokenizer  # you can comment out this line if you're not\n",
        "                      # running this on a memory-limited machine\n",
        "gc.collect()          # remove the (now unused) variables from memory\n",
        "\n",
        "# install tex-live\n",
        "!apt-get install texlive-latex-recommended\n",
        "\n",
        "# download template\n",
        "!git clone https://github.com/ContextLab/abstract2paper"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'apt autoremove' to remove it.\n",
            "The following additional packages will be installed:\n",
            "  fonts-droid-fallback fonts-lmodern fonts-noto-mono libcupsfilters1\n",
            "  libcupsimage2 libgs9 libgs9-common libijs-0.35 libjbig2dec0 libkpathsea6\n",
            "  libpotrace0 libptexenc1 libsynctex1 libtexlua52 libtexluajit2 libzzip-0-13\n",
            "  lmodern poppler-data t1utils tex-common texlive-base texlive-binaries\n",
            "  texlive-latex-base\n",
            "Suggested packages:\n",
            "  fonts-noto poppler-utils ghostscript fonts-japanese-mincho\n",
            "  | fonts-ipafont-mincho fonts-japanese-gothic | fonts-ipafont-gothic\n",
            "  fonts-arphic-ukai fonts-arphic-uming fonts-nanum debhelper gv\n",
            "  | postscript-viewer perl-tk xpdf-reader | pdf-viewer texlive-latex-base-doc\n",
            "  texlive-latex-recommended-doc texlive-pstricks\n",
            "The following NEW packages will be installed:\n",
            "  fonts-droid-fallback fonts-lmodern fonts-noto-mono libcupsfilters1\n",
            "  libcupsimage2 libgs9 libgs9-common libijs-0.35 libjbig2dec0 libkpathsea6\n",
            "  libpotrace0 libptexenc1 libsynctex1 libtexlua52 libtexluajit2 libzzip-0-13\n",
            "  lmodern poppler-data t1utils tex-common texlive-base texlive-binaries\n",
            "  texlive-latex-base texlive-latex-recommended\n",
            "0 upgraded, 24 newly installed, 0 to remove and 27 not upgraded.\n",
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            "Setting up libtexluajit2:amd64 (2017.20170613.44572-8ubuntu0.1) ...\n",
            "Setting up fonts-lmodern (2.004.5-3) ...\n",
            "Setting up texlive-binaries (2017.20170613.44572-8ubuntu0.1) ...\n",
            "update-alternatives: using /usr/bin/xdvi-xaw to provide /usr/bin/xdvi.bin (xdvi.bin) in auto mode\n",
            "update-alternatives: using /usr/bin/bibtex.original to provide /usr/bin/bibtex (bibtex) in auto mode\n",
            "Setting up texlive-base (2017.20180305-1) ...\n",
            "mktexlsr: Updating /var/lib/texmf/ls-R-TEXLIVEDIST... \n",
            "mktexlsr: Updating /var/lib/texmf/ls-R-TEXMFMAIN... \n",
            "mktexlsr: Updating /var/lib/texmf/ls-R... \n",
            "mktexlsr: Done.\n",
            "tl-paper: setting paper size for dvips to a4: /var/lib/texmf/dvips/config/config-paper.ps\n",
            "tl-paper: setting paper size for dvipdfmx to a4: /var/lib/texmf/dvipdfmx/dvipdfmx-paper.cfg\n",
            "tl-paper: setting paper size for xdvi to a4: /var/lib/texmf/xdvi/XDvi-paper\n",
            "tl-paper: setting paper size for pdftex to a4: /var/lib/texmf/tex/generic/config/pdftexconfig.tex\n",
            "Setting up texlive-latex-base (2017.20180305-1) ...\n",
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            "Processing triggers for libc-bin (2.27-3ubuntu1.6) ...\n",
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            "Processing triggers for fontconfig (2.12.6-0ubuntu2) ...\n",
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            "Processing triggers for tex-common (6.09) ...\n",
            "Running updmap-sys. This may take some time... done.\n",
            "Running mktexlsr /var/lib/texmf ... done.\n",
            "Building format(s) --all.\n",
            "\tThis may take some time... done.\n",
            "Cloning into 'abstract2paper'...\n",
            "remote: Enumerating objects: 84, done.\u001b[K\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fgat6lZRCV1y"
      },
      "source": [
        "def texer(template, outfile='auto.tex', **kwargs):\n",
        "  with open(template, 'r') as f:\n",
        "    lines = f.readlines()\n",
        "\n",
        "  x = []\n",
        "  for line in lines:\n",
        "    for k, v in kwargs.items():\n",
        "      line = line.replace(f'<{k}>', str(v))\n",
        "    x.append(line.replace(' & ', ' \\& ').replace('%', '\\%'))\n",
        "  \n",
        "  try:\n",
        "    with open(outfile, 'w+') as f:\n",
        "      f.write('\\n'.join(x))\n",
        "  \n",
        "    os.system(f'pdflatex {outfile}')\n",
        "    os.system('rm *.log *.aux')\n",
        "\n",
        "  except:\n",
        "    pass\n",
        "\n",
        "  return '\\n'.join(x)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OZPYsfDDQ-q2"
      },
      "source": [
        "Create a PDF containing your auto-generated document..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "73tadb3xQD_t"
      },
      "source": [
        "source = texer(os.path.join('abstract2paper', 'resources', 'template.tex'), TITLE=title, AUTHOR=authors+'\\\\\\\\Augmented by GPT-Neo', GEN_TEXT=gen_text + '...')"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XRzAVkyOHA0S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "034b7c6c-c743-49fa-88b6-74bf709d3fb2"
      },
      "source": [
        "import pprint\n",
        "pprint.pprint(source, width=80)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "('\\\\documentclass{article}\\n'\n",
            " '\\n'\n",
            " '\\\\usepackage[utf8]{inputenc}\\n'\n",
            " '\\n'\n",
            " '\\n'\n",
            " '\\n'\n",
            " '\\\\title{Language Models are Awesome-Shot Learners}\\n'\n",
            " '\\n'\n",
            " '\\\\author{Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah,\\\\\\\\Jared '\n",
            " 'Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam,\\\\\\\\Girish '\n",
            " 'Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss,\\\\\\\\Gretchen '\n",
            " 'Krueger, Tom Henighan, Rewon Child, Aditya Ramesh,\\\\\\\\Daniel M. Ziegler, '\n",
            " 'Jeffrey Wu, Clemens Winter, Christopher Hesse,\\\\\\\\Mark Chen, Eric Sigler, '\n",
            " 'Mateusz Litwin, Scott Gray,\\\\\\\\Benjamin Chess, Jack Clark, Christopher '\n",
            " 'Berner, Sam McCandlish,\\\\\\\\Alec Radford, Ilya Sutskever, and Dario '\n",
            " 'Amodei\\\\\\\\Augmented by GPT-Neo}\\n'\n",
            " '\\n'\n",
            " '\\\\date{\\\\today}\\n'\n",
            " '\\n'\n",
            " '\\n'\n",
            " '\\n'\n",
            " '\\\\begin{document}\\n'\n",
            " '\\n'\n",
            " '\\n'\n",
            " '\\n'\n",
            " '\\\\maketitle\\n'\n",
            " '\\n'\n",
            " '\\n'\n",
            " '\\n'\n",
            " 'Recent work has demonstrated substantial gains on many NLP tasks and '\n",
            " 'benchmarks by pre-training on a large corpus of text followed by fine-tuning '\n",
            " 'on a specific task. While typically task-agnostic in architecture, this '\n",
            " 'method still requires task-specific fine-tuning datasets of thousands or '\n",
            " 'tens of thousands of examples. By contrast, humans can generally perform a '\n",
            " 'new language task from only a few examples or from simple instructions - '\n",
            " 'something which current NLP systems still largely struggle to do. Here we '\n",
            " 'show that scaling up language models greatly improves task-agnostic, '\n",
            " 'few-shot performance, sometimes even reaching competitiveness with prior '\n",
            " 'state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an '\n",
            " 'autoregressive language model with 175 billion parameters, 10x more than any '\n",
            " 'previous non-sparse language model, and test its performance in the few-shot '\n",
            " 'setting. For all tasks, GPT-3 is applied without any gradient updates or '\n",
            " 'fine-tuning, with tasks and few-shot demonstrations specified purely via '\n",
            " 'text interaction with the model. GPT-3 achieves strong performance on many '\n",
            " 'NLP datasets, including translation, question-answering, and cloze tasks, as '\n",
            " 'well as several tasks that require on-the-fly reasoning or domain '\n",
            " 'adaptation, such as unscrambling words, using a novel word in a sentence, or '\n",
            " 'performing 3-digit arithmetic. At the same time, we also identify some '\n",
            " \"datasets where GPT-3's few-shot learning still struggles, as well as some \"\n",
            " 'datasets where GPT-3 faces methodological issues related to training on '\n",
            " 'large web corpora. Finally, we find that GPT-3 can generate samples of news '\n",
            " 'articles which human evaluators have difficulty distinguishing from articles '\n",
            " 'written by humans. We discuss broader societal impacts of this finding and '\n",
            " 'of GPT-3 in general.\\n'\n",
            " '\\n'\n",
            " 'With the rise of AI and data science, many NLP tasks have become highly '\n",
            " 'competitive, with large gains in model size and accuracy. It is common for '\n",
            " 'both experts and novices to find new tasks and applications through '\n",
            " 'experimentation; however, the challenge for NLP researchers is to find new '\n",
            " 'ways to solve novel problems and use large amounts of data with only modest '\n",
            " 'increases in computational requirements. The field of deep learning, which '\n",
            " 'has seen successes in a number of important NLP tasks such as speech '\n",
            " 'recognition and text classification, has recently seen a surge in '\n",
            " 'popularity.\\n'\n",
            " '\\n'\n",
            " 'Current methods for finding new NLP tasks and new datasets are often '\n",
            " 'domain-specific and require specialized tools, which can be a substantial '\n",
            " 'investment in time and money. Many researchers have addressed this problem '\n",
            " 'by scaling up existing state-of-the-art methods in a task-agnostic way. '\n",
            " 'These methods typically assume that their NLP models are sufficiently '\n",
            " 'complex or they are not applicable to a given domain. This method has been '\n",
            " 'used in conjunction with large corpus pre-training to improve the '\n",
            " 'performance of many NLP tasks. For example, [1] trains the BERT model [2] '\n",
            " 'trains the attention models [3] fine-tunes GPT [4] uses pretraining with '\n",
            " 'large-scale unlabeled text. However, these methods are not task-agnostic. '\n",
            " 'For instance, [5] uses attention models and GPT to fine-tune a task-agnostic '\n",
            " 'language model which uses the same vocabulary and training procedure. More '\n",
            " 'recently, methods that are not task-agnostic have been proposed, such as [6] '\n",
            " 'and [7] which use GPT to fine-tune a language model for a specific task and '\n",
            " 'use a dataset from that task as the training data. In contrast, our approach '\n",
            " 'is task-agnostic, and we train our model using a shared vocabulary of '\n",
            " 'multiple domains, in a way that is well suited for multiple tasks. We '\n",
            " 'believe the key to this success is that GPT-3 is an autoregressive language '\n",
            " 'model [8] that uses a neural model of language inference, rather than an '\n",
            " 'attention model, which is a task-specific, feed-forward NLP model. As such, '\n",
            " 'we are able to train GPT-3 from a shared corpus and do not need to '\n",
            " 'fine-tune. Furthermore, we do not require domain-specific pre-training, '\n",
            " 'which leads to the potential for a more general NLP system, and allows us to '\n",
            " 'train our approach from several datasets, rather than restricting ourselves '\n",
            " 'to specific domains.\\n'\n",
            " '\\n'\n",
            " 'This section provides a summary of key features and properties of GPT-3, a '\n",
            " 'language model that pre-trained on a large corpus. We begin by establishing '\n",
            " \"GPT-3's architecture and defining some of its important ingredients. The \"\n",
            " 'next section describes the data, and then the section following it describes '\n",
            " 'the methods used to train the model. After all these steps, we then explain '\n",
            " \"specific datasets where GPT-3's performance has been consistently better \"\n",
            " 'than all previous systems. As a result, we show how our approach is '\n",
            " 'complementary to current state-of-...\\n'\n",
            " '\\n'\n",
            " '\\n'\n",
            " '\\n'\n",
            " '\\\\end{document}\\n')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l-T8YAKxmW5i",
        "outputId": "7c8a4bda-54e5-40b3-b960-3327f1da7b1c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "from google.colab import files\n",
        "files.download('auto.tex')\n",
        "files.download('auto.pdf')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_f50e6194-3683-4ede-bc28-c50663694366\", \"auto.tex\", 5416)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_a478ba31-8798-4ca0-920e-67b20368c325\", \"auto.pdf\", 55723)"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}