day_5/day_5.ipynb

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   "source": [
    "# Day 5\n",
    "In today's exercise, we will focus on extracting information from PDF articles and interview transcripts. We will again use the transformers text generation pipeline to extract information from these sources. The first part of the exercise will focus on extracting information from a PDF article, while the second part will focus on extracting information from an interview transcript.\n",
    "\n",
    "By the end of this exercise, you should be able to:\n",
    "- Extract information from text using the `transformers` text generation pipeline\n",
    "- Gain a sense for how subtle changes in the prompt can affect the quality of the generated text"
   ],
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   "cell_type": "markdown",
   "source": [
    "## Environment Setup\n",
    "**Make sure to set your runtime to use a GPU by going to `Runtime` -> `Change runtime type` -> `Hardware accelerator` -> `T4 GPU`**"
   ],
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    "outputId": "1ecfa1eb-d8c8-4c8f-f8c5-eb8e60f93e57"
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   "source": [
    "import sys\n",
    "if 'google.colab' in sys.modules:  # If in Google Colab environment\n",
    "    # Mount google drive to enable access to data files\n",
    "    from google.colab import drive\n",
    "    drive.mount('/content/drive')\n",
    "\n",
    "    # Installing requisite packages\n",
    "    !pip install transformers accelerate pymupdf &> /dev/null\n",
    "\n",
    "    # Change working directory to day_5\n",
    "    %cd /content/drive/MyDrive/LLM4BeSci_GSERM2024/day_5"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "import fitz\n",
    "from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer\n",
    "import torch\n",
    "import textwrap"
   ],
   "metadata": {
    "id": "32d1a87f8f8f332b"
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   "execution_count": null
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   "cell_type": "markdown",
   "source": [
    "## Part 1: Extracting Information from PDF articles\n",
    "This section will focus on extracting information from a PDF article. We will be using our working paper \"Against Justaism: A call for more measured discussions on LLM cognition\". \n",
    "\n",
    "The code begins by reading the PDF file using the `fitz` library. It then extracts the text from the first four pages of the PDF, which contains the whole article apart from the conclusion and references. We do this so that the prompt does not overload the available GPU memory in Colab:"
   ],
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   "source": [
    "# Extract text from PDF\n",
    "pdf = fitz.open('Against_Justaism.pdf')\n",
    "against_justaism = \"\"\n",
    "for page in pdf[:4]:\n",
    "    against_justaism += page.get_text()\n",
    "\n",
    "against_justaism[:1000]  # Display the first 500 characters to verify content extraction"
   ],
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   "cell_type": "markdown",
   "source": [
    "The code next loads the `'microsoft/Phi-3-mini-128k-instruct'` model and tokenizer:"
   ],
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   "source": [
    "torch.random.manual_seed(42) # For reproducibility\n",
    "\n",
    "# Load the model\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    \"microsoft/Phi-3-mini-128k-instruct\",\n",
    "    device_map=\"cuda\", # Use GPU\n",
    "    torch_dtype=torch.float16, # Use half-precision\n",
    "    trust_remote_code=True,\n",
    "    attn_implementation='eager' # For faster inference on T4 GPUs\n",
    ")\n",
    "\n",
    "# Load the tokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/Phi-3-mini-128k-instruct\")"
   ],
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   "source": [
    "The code then creates a text generation pipeline using the loaded model and tokenizer. It sets `\"do_sample\": False` to use greedy decoding and `\"max_new_tokens\": 300` to limit the number of tokens to a reasonable length. "
   ],
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   "cell_type": "code",
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   "source": [
    "pipe = pipeline(\n",
    "    \"text-generation\",\n",
    "    model=model,\n",
    "    tokenizer=tokenizer,\n",
    ")\n",
    "\n",
    "generation_args = {\n",
    "    \"max_new_tokens\": 300,  # Maximum number of tokens to generate\n",
    "    \"return_full_text\": False, # Return only the generated text\n",
    "    \"do_sample\": False # Use greedy decoding\n",
    "    # \"temperature\": 0.0 # Change for TASK 2 from part 2\n",
    "}"
   ],
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   "cell_type": "markdown",
   "source": [
    "We next use a specific prompting format recommended on the [model card](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct). The format alternates between the `\"assistant\"` and `\"user\"` roles to generate a chat-like conversation. In this case, the assistant provides the text as context, and the user asks questions about the text. The model is then prompted to play the assistant and generate a response to this simulated conversation."
   ],
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   },
   "id": "89367044e1d9776b"
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  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "def query_paper(query, text):\n",
    "    \"\"\"Generates a response to a query about a text using the text generation pipeline.\"\"\"\n",
    "    prompt = [\n",
    "        {\"role\": \"assistant\", \"content\": \"What do you want to know about the text?:\\n-------------------\\n\" + text},\n",
    "        {\"role\": \"user\", \"content\": query}\n",
    "    ]\n",
    "    output = pipe(prompt, **generation_args)\n",
    "    return '\\n'.join(textwrap.wrap(output[0]['generated_text'], 100))\n",
    "\n",
    "print(query_paper(\"What is the title of the paper?\", against_justaism))"
   ],
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     "base_uri": "https://localhost:8080/"
    },
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    "outputId": "f52aaefa-e5fc-4099-cdd2-18753ce327f2"
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  {
   "cell_type": "markdown",
   "source": [
    "Although the model successfully returns just the title of the paper, it does not do so well for the abstract:"
   ],
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   },
   "id": "11f2ac649b719895"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "print(query_paper(\"Return the full abstract from the paper\", against_justaism))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dc331c0f510d6801",
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   "id": "dc331c0f510d6801",
   "execution_count": null
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "print(query_paper(\"Summarise the paper\", against_justaism))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d049b257385e00e4",
    "outputId": "e165e0ec-bb97-4bba-c841-c9ad54daf6f3"
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   "cell_type": "markdown",
   "source": [
    "We find the summary to be quite accurate.\n",
    "\n",
    "**TASK 1**: Come up with your own prompts to extract information from the paper. Make the prompts more and more difficult until the model no longer provides satisfactory responses.\n",
    "**TASK 2**: Try tweaking the prompt to the unsatisfactory response to see if you can get a better response. "
   ],
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   "cell_type": "markdown",
   "source": [
    "## Part 2: Extracting information from Interview Transcripts\n",
    "This section will focus on extracting information from interview transcripts. Extracting information from interviews can be tricky due to the informal and sometimes grammatically incorrect nature of the text. We will be working with an interview transcript with Ilya Sutskever (conducted by Sven Strohband, processed [here](https://www.lesswrong.com/posts/TpKktHS8GszgmMw4B/ilya-sutskever-s-thoughts-on-ai-safety-july-2023-a)). The code begins by reading the interview transcript from the `ilya_interview.txt` file:"
   ],
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    "id": "8376ea4c6f0ec242"
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  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# Reads 'ilya_interview.txt' file into a string\n",
    "with open('ilya_interview.txt', 'r') as file:\n",
    "    ilya_interview = file.read()\n",
    "\n",
    "# Print the first 1000 characters to verify content extraction\n",
    "ilya_interview[:1000]"
   ],
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  {
   "cell_type": "markdown",
   "source": [
    "The code begins by asking a simple, high-level question about the interview:"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "c361b8cabf1bd180"
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  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "print(query_paper(\"What does Ilya think of AI safety?\", ilya_interview))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7f1d641eb5ce7313",
    "outputId": "aa9e1b08-2796-4b57-d686-38286f1a6877"
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   "id": "7f1d641eb5ce7313",
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  },
  {
   "cell_type": "markdown",
   "source": [
    "As you can see, the response isn't quite satisfactory, and sometimes mistrepresents Ilya's views or hallucinates information. In the next prompt, we will try to guide the model to focus on key sentences that illustrate Ilya's stance on AI safety to reduce the risk of hallucination:"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "101ad7ce564b309a"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "print(query_paper(\"Extract key sentences illustrating Ilya's stance about AI Safety\", ilya_interview))"
   ],
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     "base_uri": "https://localhost:8080/"
    },
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    "print(query_paper(\"What does Ilya think of AI safety? Focus on key sentences from the transcript and expand on these\", ilya_interview))"
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    "**TASK 1**: Now prompt the model in the code block below to pretend that is Ilya based on the interview transcript and ask it some completely unrelated and absurd questions. What kind of responses do you get?"
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