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   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dbamman/anlp25/blob/main/10.llms/LLM_Personas.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oz2dtjGGPe0C"
   },
   "source": [
    "In this notebook, we'll use LLMs to elicit personas, first replicating Horton 2023, \"[Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?](https://arxiv.org/abs/2301.07543)\" and then eliciting your own personas to answer questions.\n",
    "\n",
    "Work in pairs; have one person download the base model and the other download the instruction-tuned model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "x8lrHnucLBfk"
   },
   "outputs": [],
   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell ASAP to download the models\n",
    "\n",
    "# model = AutoModelForCausalLM.from_pretrained(\"Qwen/Qwen3-4B-Base\", device_map=\"cuda\", dtype=\"auto\")\n",
    "model = AutoModelForCausalLM.from_pretrained(\"Qwen/Qwen3-4B\", device_map=\"cuda\", dtype=\"auto\")\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"Qwen/Qwen3-4B\")"
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   "source": [
    "def prompt_model(messages, model, thinking=False):\n",
    "    text = tokenizer.apply_chat_template(\n",
    "        messages,\n",
    "        tokenize=False,\n",
    "        add_generation_prompt=True,\n",
    "        enable_thinking=thinking # Switches between thinking and non-thinking modes. Default is True.\n",
    "    )\n",
    "    \n",
    "    model_inputs = tokenizer([text], return_tensors=\"pt\").to(model.device)\n",
    "    \n",
    "    # conduct text completion\n",
    "    generated = model.generate(\n",
    "        **model_inputs,\n",
    "        max_new_tokens=500\n",
    "    )\n",
    "\n",
    "    # let's break this down:\n",
    "    #                      | we take the element of the batch (our batch size is 1)\n",
    "    #                      |  |-----------------------------| skip our original input\n",
    "    output_ids = generated[0][len(model_inputs.input_ids[0]):].tolist()\n",
    "\n",
    "    # decode into token space\n",
    "    return tokenizer.decode(output_ids, skip_special_tokens=True).strip(\"\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RixD-pCQLC1_"
   },
   "outputs": [],
   "source": [
    "def elicit_persona(persona_prompt, query, base=True):\n",
    "    messages = [\n",
    "        {\"role\": \"system\", \"content\": persona_prompt},\n",
    "        {\"role\": \"user\", \"content\": query}\n",
    "    ]\n",
    "    print(prompt_model(messages, model))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prompting base model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QFoL-6sBP4d9"
   },
   "source": [
    "First, let's examine's Horton's use of LLMs to elicit preferences with respect to pricing increases (originally from [Kahneman et al. 1986](https://www.jstor.org/stable/1806070)).\n",
    "\n",
    "Run these prompts through the base model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "persona_prompt = \"Adopt the persona of a libertarian and answer the following query under that persona.\"\n",
    "query = \"\"\"\n",
    "A hardware store has been selling snow shovels for $15. The morning after a large snowstorm, the store raises the price to $20.\n",
    "Please rate this action as: 1) Completely Fair 2) Acceptable 3) Unfair 4) Very Unfair\"\"\"\n",
    "\n",
    "elicit_persona(persona_prompt, query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "h5qiC19KOW8p",
    "outputId": "e59a625b-f380-40b5-c963-9fa2d4f73d2c"
   },
   "outputs": [],
   "source": [
    "persona_prompt = \"Adopt the persona of a socialist and answer the following query under that persona.\"\n",
    "query = \"\"\"\n",
    "A hardware store has been selling snow shovels for $15. The morning after a large snowstorm, the store raises the price to $20.\n",
    "Please rate this action as: 1) Completely Fair 2) Acceptable 3) Unfair 4) Very Unfair\"\"\"\n",
    "\n",
    "elicit_persona(persona_prompt, query)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prompting post-trained model\n",
    "\n",
    "The base model has not been further post-trained with instruction or preference tuning. If you're interested in the specifics of the pre-training and post-training, you can read the [Qwen 3 technical report](https://arxiv.org/pdf/2505.09388#page=3.77).\n",
    "\n",
    "Now try running the same inputs through an instruction-tuned model. How do the outputs change with the post-trained versions of the models?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gvR_eontQj0W"
   },
   "source": [
    "## Constructing your own persona\n",
    "\n",
    "Write a persona prompt to simulate yourself. Consider what details you might want to include. Experiment with the base and preference-tuned models.\n",
    "\n",
    "Note down your own answers to these questions, then see how the model outputs compare:\n",
    "\n",
    "1. Do you think money brings happiness?\n",
    "2. Do you prefer hiking or video games?\n",
    "3. Are you a morning person or a night owl?\n",
    "4. What is your favorite sport to watch?\n",
    "5. Do you think greater respect for authority is a good thing, a bad thing, or you don't mind?\n",
    "6. How interested in politics are you? Are you 1) very interested, 2) somewhat interested, 3) not very interested, 4) not at all interested?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "h8fnyMy-RKNx"
   },
   "outputs": [],
   "source": [
    "persona_prompt = \"\"\"FILL IN YOUR PERSONA HERE\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "questions = [\n",
    "    \"Do you think money brings happiness?\",\n",
    "    \"Do you prefer hiking or video games?\",\n",
    "    \"Are you a morning person or a night owl?\",\n",
    "    \"What is your favorite sport to watch?\",\n",
    "    \"Do you think greater respect for authority is a good thing, a bad thing, or you don't mind?\",\n",
    "    \"How interested in politics are you? Are you 1) very interested, 2) somewhat interested, 3) not very interested, 4) not at all interested?\",\n",
    "]\n",
    "\n",
    "for question in questions:\n",
    "    print(\"=======\")\n",
    "    print(question)\n",
    "    print()\n",
    "    elicit_persona(persona_prompt, question, base=False)"
   ]
  }
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