{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 🎯 White-Box Uncertainty Quantification\n",
    "\n",
    "<div style=\"background-color: rgba(200, 200, 200, 0.1); padding: 20px; border-radius: 8px; margin-bottom: 20px; border: 1px solid rgba(127, 127, 127, 0.2); max-width: 97.5%; overflow-wrap: break-word;\">\n",
    "  <p style=\"font-size: 16px; line-height: 1.6\">\n",
    "     White-box Uncertainty Quantification (UQ) methods leverage token probabilities to estimate uncertainty. The single-generation white-box UQ methods are significantly faster and cheaper than black-box methods, but require access to the LLM's internal probabilities, meaning they are not necessarily compatible with all LLMs/APIs. This demo provides an illustration of how to use state-of-the-art white-box UQ methods with <code>uqlm</code>. The following single-generation scorers are available:\n",
    "  </p>\n",
    "      \n",
    "*   Minimum token probability ([Manakul et al., 2023](https://arxiv.org/abs/2303.08896))\n",
    "*   Length-Normalized Sequence Probability ([Malinin & Gales, 2021](https://arxiv.org/pdf/2002.07650))\n",
    "*   Sequence Probability ([Vashurin et al., 2024](https://arxiv.org/abs/2406.15627))\n",
    "*   Mean Top-K Token Negentropy ([Scalena et al., 2025](https://arxiv.org/abs/2510.11170); [Manakul et al., 2023](https://arxiv.org/abs/2303.08896))\n",
    "*   Min Top-K Token Negentropy ([Scalena et al., 2025](https://arxiv.org/abs/2510.11170); [Manakul et al., 2023](https://arxiv.org/abs/2303.08896))\n",
    "*   Probability Margin ([Farr et al., 2024](https://arxiv.org/abs/2408.08217))\n",
    "</div>\n",
    "\n",
    "## 📊 What You'll Do in This Demo\n",
    "\n",
    "<div style=\"display: flex; margin-bottom: 15px; align-items: center\">\n",
    "  <div style=\"background-color: #34a853; color: white; border-radius: 50%; width: 30px; height: 30px; display: flex; justify-content: center; align-items: center; margin-right: 15px; flex-shrink: 0\"><strong>1</strong></div>\n",
    "  <div>\n",
    "    <p style=\"margin: 0; font-weight: bold\"><a href=#section1>Set up LLM and prompts.</a></p>\n",
    "    <p style=\"margin: 0; color: rgba(95, 99, 104, 0.8)\">Set up LLM instance and load example data prompts.</p>\n",
    "  </div>\n",
    "</div>\n",
    "\n",
    "<div style=\"display: flex; margin-bottom: 15px; align-items: center\">\n",
    "  <div style=\"background-color: #34a853; color: white; border-radius: 50%; width: 30px; height: 30px; display: flex; justify-content: center; align-items: center; margin-right: 15px; flex-shrink: 0\"><strong>2</strong></div>\n",
    "  <div>\n",
    "    <p style=\"margin: 0; font-weight: bold\"><a href=#section2>Generate LLM Responses and Confidence Scores</a></p>\n",
    "    <p style=\"margin: 0; color: rgba(95, 99, 104, 0.8)\">Generate and score LLM responses to the example questions using the <code>WhiteBoxUQ()</code> class.</p>\n",
    "  </div>\n",
    "</div>\n",
    "\n",
    "<div style=\"display: flex; margin-bottom: 25px; align-items: center\">\n",
    "  <div style=\"background-color: #34a853; color: white; border-radius: 50%; width: 30px; height: 30px; display: flex; justify-content: center; align-items: center; margin-right: 15px; flex-shrink: 0\"><strong>3</strong></div>\n",
    "  <div>\n",
    "    <p style=\"margin: 0; font-weight: bold\"><a href=#section3>Evaluate Hallucination Detection Performance</a></p>\n",
    "    <p style=\"margin: 0; color: rgba(95, 99, 104, 0.8)\">Visualize model accuracy at different thresholds of the various white-box UQ confidence scores. Compute precision, recall, and F1-score of hallucination detection.</p>\n",
    "  </div>\n",
    "</div>\n",
    "\n",
    "## ⚖️ Advantages & Limitations\n",
    "\n",
    "<div style=\"display: flex; gap: 20px\">\n",
    "  <div style=\"flex: 1; background-color: rgba(0, 200, 0, 0.1); padding: 15px; border-radius: 8px; border: 1px solid rgba(0, 200, 0, 0.2)\">\n",
    "    <h3 style=\"color: #2e8b57; margin-top: 0\">Pros</h3>\n",
    "    <ul style=\"margin-bottom: 0\">\n",
    "      <li><strong>Minimal Latency Impact:</strong> Token probabilities are already returned by the LLM.</li>\n",
    "      <li><strong>No Added Cost:</strong> Doesn't require additional LLM calls.</li>\n",
    "    </ul>\n",
    "  </div>\n",
    "  \n",
    "  <div style=\"flex: 1; background-color: rgba(200, 0, 0, 0.1); padding: 15px; border-radius: 8px; border: 1px solid rgba(200, 0, 0, 0.2)\">\n",
    "    <h3 style=\"color: #b22222; margin-top: 0\">Cons</h3>\n",
    "    <ul style=\"margin-bottom: 0\">\n",
    "      <li><strong>Limited Compatibility:</strong> Requires access to token probabilities, not available for all LLMs/APIs.</li>\n",
    "    </ul>\n",
    "  </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "\n",
    "from uqlm import WhiteBoxUQ\n",
    "from uqlm.utils import load_example_dataset, math_postprocessor, plot_model_accuracies, Tuner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='section1'></a>\n",
    "## 1. Set up LLM and Prompts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this demo, we will illustrate this approach using a set of math questions from the [GSM8K benchmark](https://github.com/openai/grade-school-math). To implement with your use case, simply **replace the example prompts with your data**.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading dataset - gsm8k...\n",
      "Processing dataset...\n",
      "Dataset ready!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>question</th>\n",
       "      <th>answer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Natalia sold clips to 48 of her friends in Apr...</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Weng earns $12 an hour for babysitting. Yester...</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Betty is saving money for a new wallet which c...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Julie is reading a 120-page book. Yesterday, s...</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>James writes a 3-page letter to 2 different fr...</td>\n",
       "      <td>624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            question answer\n",
       "0  Natalia sold clips to 48 of her friends in Apr...     72\n",
       "1  Weng earns $12 an hour for babysitting. Yester...     10\n",
       "2  Betty is saving money for a new wallet which c...      5\n",
       "3  Julie is reading a 120-page book. Yesterday, s...     42\n",
       "4  James writes a 3-page letter to 2 different fr...    624"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load example dataset (gsm8k)\n",
    "gsm8k = load_example_dataset(\"gsm8k\", n=200)\n",
    "gsm8k.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define prompts\n",
    "MATH_INSTRUCTION = \"When you solve this math problem only return the answer with no additional text.\\n\"\n",
    "prompts = [MATH_INSTRUCTION + prompt for prompt in gsm8k.question]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we use `AzureChatOpenAI` to instantiate our LLM, but any [LangChain Chat Model](https://js.langchain.com/docs/integrations/chat/) may be used. Be sure to **replace with your LLM of choice.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import sys\n",
    "# !{sys.executable} -m pip install langchain-openai\n",
    "\n",
    "# # User to populate .env file with API credentials\n",
    "from dotenv import load_dotenv, find_dotenv\n",
    "from langchain_openai import AzureChatOpenAI\n",
    "\n",
    "load_dotenv(find_dotenv())\n",
    "llm = AzureChatOpenAI(\n",
    "    deployment_name=\"gpt-4.1\",\n",
    "    openai_api_type=\"azure\",\n",
    "    openai_api_version=\"2024-02-15-preview\",\n",
    "    temperature=1,  # User to set temperature\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='section2'></a>\n",
    "## 2. Generate responses and confidence scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `WhiteBoxUQ()` - Generate LLM responses and compute token-probability-based confidence scores for each response.\n",
    "\n",
    "![Sample Image](https://raw.githubusercontent.com/cvs-health/uqlm/develop/assets/images/white_box_graphic.png)\n",
    "\n",
    "#### 📋 Class Attributes\n",
    "\n",
    "<table style=\"border-collapse: collapse; width: 100%; border: 1px solid rgba(127, 127, 127, 0.2);\">\n",
    "  <tr>\n",
    "    <th style=\"background-color: rgba(200, 200, 200, 0.2); width: 20%; padding: 8px; text-align: left; border: 1px solid rgba(127, 127, 127, 0.2);\">Parameter</th>\n",
    "    <th style=\"background-color: rgba(200, 200, 200, 0.2); width: 25%; padding: 8px; text-align: left; border: 1px solid rgba(127, 127, 127, 0.2);\">Type & Default</th>\n",
    "    <th style=\"background-color: rgba(200, 200, 200, 0.2); width: 55%; padding: 8px; text-align: left; border: 1px solid rgba(127, 127, 127, 0.2);\">Description</th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td style=\"font-weight: bold; padding: 8px; border: 1px solid rgba(127, 127, 127, 0.2);\">llm</td>\n",
    "    <td style=\"padding: 8px; border: 1px solid rgba(127, 127, 127, 0.2);\">BaseChatModel<br><code>default=None</code></td>\n",
    "    <td style=\"padding: 8px; border: 1px solid rgba(127, 127, 127, 0.2);\">A langchain llm `BaseChatModel`. User is responsible for specifying temperature and other relevant parameters to the constructor of their `llm` object.</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td style=\"font-weight: bold; padding: 8px; border: 1px solid rgba(127, 127, 127, 0.2);\">scorers</td>\n",
    "    <td style=\"padding: 8px; border: 1px solid rgba(127, 127, 127, 0.2);\">List[str]<br><code>default=None</code></td>\n",
    "    <td style=\"padding: 8px; border: 1px solid rgba(127, 127, 127, 0.2);\">Specifies which white-box (token-probability-based) scorers to include. Must be subset of {\"normalized_probability\", \"min_probability\", \"sequence_probability\", \"max_token_negentropy\", \"mean_token_negentropy\", \"probability_margin\", \"monte_carlo_negentropy\", \"consistency_and_confidence\"}. If None, defaults to [\"normalized_probability\", \"min_probability\"].</td>\n",
    "  </tr>    \n",
    "  <tr>\n",
    "    <td style=\"font-weight: bold; padding: 8px; border: 1px solid rgba(127, 127, 127, 0.2);\">system_prompt</td>\n",
    "    <td style=\"padding: 8px; border: 1px solid rgba(127, 127, 127, 0.2);\">str or None<br><code>default=\"You are a helpful assistant.\"</code></td>\n",
    "    <td style=\"padding: 8px; border: 1px solid rgba(127, 127, 127, 0.2);\">Optional argument for user to provide custom system prompt for the LLM.</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td style=\"font-weight: bold; padding: 8px; border: 1px solid rgba(127, 127, 127, 0.2);\">max_calls_per_min</td>\n",
    "    <td style=\"padding: 8px; border: 1px solid rgba(127, 127, 127, 0.2);\">int<br><code>default=None</code></td>\n",
    "    <td style=\"padding: 8px; border: 1px solid rgba(127, 127, 127, 0.2);\">Specifies how many API calls to make per minute to avoid rate limit errors. By default, no limit is specified.</td>\n",
    "  </tr>\n",
    "</table>\n",
    "\n",
    "#### 🔍 Parameter Groups\n",
    "\n",
    "<div style=\"display: flex; gap: 20px; margin-bottom: 20px\">\n",
    "  <div style=\"flex: 1; padding: 10px; background-color: rgba(0, 100, 200, 0.1); border-radius: 5px; border: 1px solid rgba(0, 100, 200, 0.2);\">\n",
    "    <p style=\"font-weight: bold\">🧠 Model-Specific</p>\n",
    "    <ul>\n",
    "      <li><code>llm</code></li>\n",
    "      <li><code>system_prompt</code></li>\n",
    "    </ul>\n",
    "  </div>\n",
    "  <div style=\"flex: 1; padding: 10px; background-color: rgba(0, 200, 0, 0.1); border-radius: 5px; border: 1px solid rgba(0, 200, 0, 0.2);\">\n",
    "    <p style=\"font-weight: bold\">📊 Confidence Scores</p>\n",
    "    <ul>\n",
    "      <li><code>scorers</code></li>\n",
    "    </ul>\n",
    "  </div>\n",
    "  <div style=\"flex: 1; padding: 10px; background-color: rgba(200, 0, 200, 0.1); border-radius: 5px; border: 1px solid rgba(200, 0, 200, 0.2);\">\n",
    "    <p style=\"font-weight: bold\">⚡ Performance</p>\n",
    "    <ul>\n",
    "      <li><code>max_calls_per_min</code></li>\n",
    "    </ul>\n",
    "  </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyter/uqlm/uqlm/scorers/white_box.py:177: UQLMBetaWarning: Scoring with top_logprobs is in beta. Please use it with caution as it may change in future releases.\n",
      "  beta_warning(\"Scoring with top_logprobs is in beta. Please use it with caution as it may change in future releases.\")\n"
     ]
    }
   ],
   "source": [
    "scorers = [\n",
    "    # these two scorers require only one logprob per token\n",
    "    \"min_probability\",\n",
    "    \"normalized_probability\",\n",
    "    # LLM must support access to Top K logprobs for the below scorers (in Beta). Comment these out if your LLM does not support.\n",
    "    \"mean_token_negentropy\",\n",
    "    \"min_token_negentropy\",\n",
    "    \"probability_margin\",\n",
    "]\n",
    "wbuq = WhiteBoxUQ(llm=llm, scorers=scorers, max_calls_per_min=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 🔄 Class Methods\n",
    "\n",
    "<table style=\"border-collapse: collapse; width: 100%; border: 1px solid rgba(127, 127, 127, 0.2);\">\n",
    "  <tr>\n",
    "    <th style=\"background-color: rgba(200, 200, 200, 0.2); width: 25%; padding: 8px; text-align: left; border: 1px solid rgba(127, 127, 127, 0.2);\">Method</th>\n",
    "    <th style=\"background-color: rgba(200, 200, 200, 0.2); width: 75%; padding: 8px; text-align: left; border: 1px solid rgba(127, 127, 127, 0.2);\">Description & Parameters</th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td style=\"font-weight: bold; vertical-align: top; padding: 8px; border: 1px solid rgba(127, 127, 127, 0.2);\">WhiteBoxUQ.generate_and_score</td>\n",
    "    <td style=\"padding: 8px; border: 1px solid rgba(127, 127, 127, 0.2);\">\n",
    "      <p>Generate LLM responses and compute confidence scores for the provided prompts.</p>\n",
    "      <p><strong>Parameters:</strong></p>\n",
    "      <ul>\n",
    "        <li><code>prompts</code> - (<strong>List[str] or List[List[BaseMessage]]</strong>) A list of input prompts for the model.</li>\n",
    "        <li><code>show_progress_bars</code> - (<strong>bool, default=True</strong>) If True, displays a progress bar while generating and scoring responses.</li>  \n",
    "      </ul>\n",
    "      <p><strong>Returns:</strong> <code>UQResult</code> containing data (prompts, responses, log probabilities, and confidence scores) and metadata</p>\n",
    "      <div style=\"background-color: rgba(0, 200, 0, 0.1); padding: 8px; border-radius: 3px; margin-top: 10px; border: 1px solid rgba(0, 200, 0, 0.2); margin-right: 5px; box-sizing: border-box; width: 100%;\">\n",
    "        <strong>💡 Best For:</strong> Complete end-to-end uncertainty quantification when starting with prompts.\n",
    "      </div>\n",
    "    </td>\n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bf2ccf28d8324760b833fca4054fffcf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = await wbuq.generate_and_score(prompts=prompts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prompt</th>\n",
       "      <th>response</th>\n",
       "      <th>logprob</th>\n",
       "      <th>normalized_probability</th>\n",
       "      <th>min_probability</th>\n",
       "      <th>min_token_negentropy</th>\n",
       "      <th>mean_token_negentropy</th>\n",
       "      <th>probability_margin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>When you solve this math problem only return t...</td>\n",
       "      <td>72</td>\n",
       "      <td>[{'token': '72', 'bytes': [55, 50], 'logprob':...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>When you solve this math problem only return t...</td>\n",
       "      <td>$10</td>\n",
       "      <td>[{'token': '$', 'bytes': [36], 'logprob': -5.2...</td>\n",
       "      <td>0.999974</td>\n",
       "      <td>0.999948</td>\n",
       "      <td>0.999782</td>\n",
       "      <td>0.999891</td>\n",
       "      <td>0.999951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>When you solve this math problem only return t...</td>\n",
       "      <td>$20</td>\n",
       "      <td>[{'token': '$', 'bytes': [36], 'logprob': -2.5...</td>\n",
       "      <td>0.767919</td>\n",
       "      <td>0.589715</td>\n",
       "      <td>0.654468</td>\n",
       "      <td>0.827175</td>\n",
       "      <td>0.655557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>When you solve this math problem only return t...</td>\n",
       "      <td>24</td>\n",
       "      <td>[{'token': '24', 'bytes': [50, 52], 'logprob':...</td>\n",
       "      <td>0.738933</td>\n",
       "      <td>0.738933</td>\n",
       "      <td>0.751666</td>\n",
       "      <td>0.751666</td>\n",
       "      <td>0.499037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>When you solve this math problem only return t...</td>\n",
       "      <td>624</td>\n",
       "      <td>[{'token': '624', 'bytes': [54, 50, 52], 'logp...</td>\n",
       "      <td>0.999996</td>\n",
       "      <td>0.999996</td>\n",
       "      <td>0.999977</td>\n",
       "      <td>0.999977</td>\n",
       "      <td>0.999993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              prompt response  \\\n",
       "0  When you solve this math problem only return t...       72   \n",
       "1  When you solve this math problem only return t...      $10   \n",
       "2  When you solve this math problem only return t...      $20   \n",
       "3  When you solve this math problem only return t...       24   \n",
       "4  When you solve this math problem only return t...      624   \n",
       "\n",
       "                                             logprob  normalized_probability  \\\n",
       "0  [{'token': '72', 'bytes': [55, 50], 'logprob':...                1.000000   \n",
       "1  [{'token': '$', 'bytes': [36], 'logprob': -5.2...                0.999974   \n",
       "2  [{'token': '$', 'bytes': [36], 'logprob': -2.5...                0.767919   \n",
       "3  [{'token': '24', 'bytes': [50, 52], 'logprob':...                0.738933   \n",
       "4  [{'token': '624', 'bytes': [54, 50, 52], 'logp...                0.999996   \n",
       "\n",
       "   min_probability  min_token_negentropy  mean_token_negentropy  \\\n",
       "0         1.000000              0.999999               0.999999   \n",
       "1         0.999948              0.999782               0.999891   \n",
       "2         0.589715              0.654468               0.827175   \n",
       "3         0.738933              0.751666               0.751666   \n",
       "4         0.999996              0.999977               0.999977   \n",
       "\n",
       "   probability_margin  \n",
       "0            0.999999  \n",
       "1            0.999951  \n",
       "2            0.655557  \n",
       "3            0.499037  \n",
       "4            0.999993  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df = results.to_df()\n",
    "result_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='section3'></a>\n",
    "## 3. Evaluate Hallucination Detection Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To evaluate hallucination detection performance, we 'grade' the responses against an answer key. Note the `math_postprocessor` is specific to our use case (math questions). **If you are using your own prompts/questions, update the grading method accordingly**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prompt</th>\n",
       "      <th>response</th>\n",
       "      <th>logprob</th>\n",
       "      <th>normalized_probability</th>\n",
       "      <th>min_probability</th>\n",
       "      <th>min_token_negentropy</th>\n",
       "      <th>mean_token_negentropy</th>\n",
       "      <th>probability_margin</th>\n",
       "      <th>answer</th>\n",
       "      <th>response_correct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>When you solve this math problem only return t...</td>\n",
       "      <td>72</td>\n",
       "      <td>[{'token': '72', 'bytes': [55, 50], 'logprob':...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>72</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>When you solve this math problem only return t...</td>\n",
       "      <td>$10</td>\n",
       "      <td>[{'token': '$', 'bytes': [36], 'logprob': -5.2...</td>\n",
       "      <td>0.999974</td>\n",
       "      <td>0.999948</td>\n",
       "      <td>0.999782</td>\n",
       "      <td>0.999891</td>\n",
       "      <td>0.999951</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>When you solve this math problem only return t...</td>\n",
       "      <td>$20</td>\n",
       "      <td>[{'token': '$', 'bytes': [36], 'logprob': -2.5...</td>\n",
       "      <td>0.767919</td>\n",
       "      <td>0.589715</td>\n",
       "      <td>0.654468</td>\n",
       "      <td>0.827175</td>\n",
       "      <td>0.655557</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>When you solve this math problem only return t...</td>\n",
       "      <td>24</td>\n",
       "      <td>[{'token': '24', 'bytes': [50, 52], 'logprob':...</td>\n",
       "      <td>0.738933</td>\n",
       "      <td>0.738933</td>\n",
       "      <td>0.751666</td>\n",
       "      <td>0.751666</td>\n",
       "      <td>0.499037</td>\n",
       "      <td>42</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>When you solve this math problem only return t...</td>\n",
       "      <td>624</td>\n",
       "      <td>[{'token': '624', 'bytes': [54, 50, 52], 'logp...</td>\n",
       "      <td>0.999996</td>\n",
       "      <td>0.999996</td>\n",
       "      <td>0.999977</td>\n",
       "      <td>0.999977</td>\n",
       "      <td>0.999993</td>\n",
       "      <td>624</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              prompt response  \\\n",
       "0  When you solve this math problem only return t...       72   \n",
       "1  When you solve this math problem only return t...      $10   \n",
       "2  When you solve this math problem only return t...      $20   \n",
       "3  When you solve this math problem only return t...       24   \n",
       "4  When you solve this math problem only return t...      624   \n",
       "\n",
       "                                             logprob  normalized_probability  \\\n",
       "0  [{'token': '72', 'bytes': [55, 50], 'logprob':...                1.000000   \n",
       "1  [{'token': '$', 'bytes': [36], 'logprob': -5.2...                0.999974   \n",
       "2  [{'token': '$', 'bytes': [36], 'logprob': -2.5...                0.767919   \n",
       "3  [{'token': '24', 'bytes': [50, 52], 'logprob':...                0.738933   \n",
       "4  [{'token': '624', 'bytes': [54, 50, 52], 'logp...                0.999996   \n",
       "\n",
       "   min_probability  min_token_negentropy  mean_token_negentropy  \\\n",
       "0         1.000000              0.999999               0.999999   \n",
       "1         0.999948              0.999782               0.999891   \n",
       "2         0.589715              0.654468               0.827175   \n",
       "3         0.738933              0.751666               0.751666   \n",
       "4         0.999996              0.999977               0.999977   \n",
       "\n",
       "   probability_margin answer  response_correct  \n",
       "0            0.999999     72              True  \n",
       "1            0.999951     10              True  \n",
       "2            0.655557      5             False  \n",
       "3            0.499037     42             False  \n",
       "4            0.999993    624              True  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Populate correct answers\n",
    "result_df[\"answer\"] = gsm8k.answer\n",
    "\n",
    "# Grade responses against correct answers\n",
    "result_df[\"response_correct\"] = [math_postprocessor(r) == a for r, a in zip(result_df[\"response\"], gsm8k[\"answer\"])]\n",
    "result_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline LLM accuracy: 0.495\n"
     ]
    }
   ],
   "source": [
    "print(f\"\"\"Baseline LLM accuracy: {np.mean(result_df[\"response_correct\"])}\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1 Filtered LLM Accuracy Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we explore ‘filtered accuracy’ as a metric for evaluating the performance of our confidence scores. Filtered accuracy measures the change in LLM performance when responses with confidence scores below a specified threshold are excluded. By adjusting the confidence score threshold, we can observe how the accuracy of the LLM improves as less certain responses are filtered out.\n",
    "\n",
    "We will plot the filtered accuracy across various confidence score thresholds to visualize the relationship between confidence and LLM accuracy. This analysis helps in understanding the trade-off between response coverage (measured by sample size below) and LLM accuracy, providing insights into the reliability of the LLM’s outputs. We conduct this analysis separately for each of our scorers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for scorer in scorers:\n",
    "    plot_model_accuracies(scores=result_df[scorer], correct_indicators=result_df.response_correct, title=f\"LLM Accuracy by {scorer} Score Threshold\", display_percentage=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2 Precision, Recall, F1-Score of Hallucination Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, we compute the optimal threshold for binarizing confidence scores, using F1-score as the objective. Using this threshold, we compute precision, recall, and F1-score for black box scorer predictions of whether responses are correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======================================================================================================================================================\n",
      "Metrics                  min_probability          normalized_probability   mean_token_negentropy    min_token_negentropy     probability_margin       \n",
      "------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "Precision                0.667                    0.76                     0.687                    0.776                    0.787                    \n",
      "Recall                   0.898                    0.776                    0.939                    0.776                    0.755                    \n",
      "F1-score                 0.765                    0.768                    0.793                    0.776                    0.771                    \n",
      "------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "F-1 optimal threshold    0.59                     0.96                     0.83                     0.91                     0.95                     \n",
      "======================================================================================================================================================\n"
     ]
    }
   ],
   "source": [
    "# instantiate UQLM tuner object for threshold selection\n",
    "split = len(result_df) // 2\n",
    "t = Tuner()\n",
    "\n",
    "correct_indicators = (result_df.response_correct) * 1  # Whether responses is actually correct\n",
    "metric_values = {\"Precision\": [], \"Recall\": [], \"F1-score\": []}\n",
    "optimal_thresholds = []\n",
    "for confidence_score in wbuq.scorers:\n",
    "    # tune threshold on first half\n",
    "    y_scores = result_df[confidence_score]\n",
    "    y_scores_tune = y_scores[0:split]\n",
    "    y_true_tune = correct_indicators[0:split]\n",
    "    best_threshold = t.tune_threshold(y_scores=y_scores_tune, correct_indicators=y_true_tune, thresh_objective=\"fbeta_score\")\n",
    "\n",
    "    y_pred = [(s > best_threshold) * 1 for s in y_scores]  # predicts whether response is correct based on confidence score\n",
    "    optimal_thresholds.append(best_threshold)\n",
    "\n",
    "    # evaluate on last half\n",
    "    y_true_eval = correct_indicators[split:]\n",
    "    y_pred_eval = y_pred[split:]\n",
    "    metric_values[\"Precision\"].append(precision_score(y_true=y_true_eval, y_pred=y_pred_eval))\n",
    "    metric_values[\"Recall\"].append(recall_score(y_true=y_true_eval, y_pred=y_pred_eval))\n",
    "    metric_values[\"F1-score\"].append(f1_score(y_true=y_true_eval, y_pred=y_pred_eval))\n",
    "\n",
    "# print results\n",
    "header = f\"{'Metrics':<25}\" + \"\".join([f\"{scorer_name:<25}\" for scorer_name in wbuq.scorers])\n",
    "print(\"=\" * len(header) + \"\\n\" + header + \"\\n\" + \"-\" * len(header))\n",
    "for metric in metric_values.keys():\n",
    "    print(f\"{metric:<25}\" + \"\".join([f\"{round(x_, 3):<25}\" for x_ in metric_values[metric]]))\n",
    "print(\"-\" * len(header))\n",
    "print(f\"{'F-1 optimal threshold':<25}\" + \"\".join([f\"{round(x_, 3):<25}\" for x_ in optimal_thresholds]))\n",
    "print(\"=\" * len(header))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='section4'></a>\n",
    "## 4. Scorer Definitions\n",
    "White-box UQ scorers leverage token probabilities of the LLM's generated response to quantify uncertainty. All scorers have outputs ranging from 0 to 1, with higher values indicating higher confidence. We define several white-box UQ scorers below.\n",
    "\n",
    "The first three scorers we consider require accessing only a single logprob per token in the generated response. Let the tokenization LLM response $y_i$ be denoted as $\\{t_1,...,t_{L_i}\\}$, where $L_i$ denotes the number of tokens the response.  \n",
    "#### Sequence Probability (`sequence_probability`)\n",
    "Sequence probability is the joint probability of all tokens:\n",
    "\n",
    "\n",
    " $$       SP(y_i) = \\prod_{t \\in y_i}  p_t,$$    \n",
    "\n",
    "where $p_t$ denotes the token probability for token $t$. For more on this scorer, refer to [Vashurin et al., 2024](https://arxiv.org/abs/2406.15627).\n",
    "\n",
    "#### Length-Normalized Sequence Probability (`normalized_probability`)\n",
    "Length-normalized token (sequence) probability (LNTP) computes a length-normalized analog of joint token probability:\n",
    "\n",
    "\n",
    "$$        LNTP(y_i) = \\prod_{t \\in y_i}  p_t^{\\frac{1}{L_i}}.$$    \n",
    "\n",
    "Note that this score is equivalent to the geometric mean of token probabilities for response $y_i$. For more on this scorer, refer to [Malinin & Gales, 2021](https://arxiv.org/pdf/2002.07650).\n",
    "\n",
    "#### Minimum Token Probability (`min_probability`)\n",
    "Minimum token probability (MTP) uses the minimum among token probabilities for a given responses as a confidence score:\n",
    "\n",
    "\n",
    "$$        MTP(y_i) = \\min_{t \\in y_i}  p_t,$$    \n",
    "\n",
    "where $t$ and $p_t$ follow the same definitions as above. For more on this scorer, refer to [Manakul et al., 2023](https://arxiv.org/abs/2303.08896).\n",
    "\n",
    "---\n",
    "\n",
    "The next three scorers we consider require accessing the top-K logprobs per token in the generated response. Let the top-K token probabilities for token $t_j$ be denoted as $\\{p_{t_{jk}}\\}_{k=1}^K$.\n",
    "\n",
    "### Probability Margin (`probability_margin`)\n",
    "Probability margin is the average difference between the top two token probabilities for each token:\n",
    "\n",
    "\n",
    "$$        PM(y_i) = \\frac{1}{L_i}\\sum_{j = 1}^{L_i} (p_{t_{j1}} - p_{t_{j2}}),$$    \n",
    "\n",
    "For more on this scorer, refer to [Farr et al., 2024](https://arxiv.org/abs/2408.08217).\n",
    "\n",
    "### Average Token Negentropy (`mean_token_negentropy`)\n",
    "Average token negentropy (ATN) computes the entropy of each token using the top-K logprobs, transforms them to normalized negentropy scores (bound in [0,1]; higher values indicate higher confidence), and averages these negentropy scores to obtain a confidence score for each response. We first define Top-K token entropy for token $j$ as follows:\n",
    "\n",
    "\n",
    "$$        TE@K(t_j) = -\\sum_{k=1}^{K} p_{t_{jk}} \\log p_{t_{jk}},$$    \n",
    "\n",
    "\n",
    "Following [Bouchard and Chauhan, 2025](https://arxiv.org/abs/2504.19254), the token negentropy (TN) transformation is obtained as follows:\n",
    "\n",
    "$$        TN@K(t_j) = 1 + \\frac{TE@K(t_j)}{\\log K}.$$    \n",
    "\n",
    "\n",
    "Finally, ATN is the simple average of token negentropies across tokens in the response.\n",
    "\n",
    "$$        ATN(y_i) = \\frac{1}{L_i}\\sum_{j = 1}^{L_i} TN@K(t_j).$$    \n",
    "\n",
    "This scorer is adapted from [Scalena et al., 2025](https://arxiv.org/abs/2510.11170) and [Manakul et al., 2023](https://arxiv.org/abs/2303.08896).\n",
    "\n",
    "### Minimum Token Negentropy (`min_token_negentropy`)\n",
    "Minimum token negentropy (MTN) uses the minimum among token-level negentropies for a given responses as a confidence score:\n",
    "\n",
    "\n",
    " $$       MTN(y_i) = \\min_{j \\in \\{1,...,L_i\\}} TN@K(t_j).$$    \n",
    "\n",
    "This scorer is adapted from [Scalena et al., 2025](https://arxiv.org/abs/2510.11170) and [Manakul et al., 2023](https://arxiv.org/abs/2303.08896).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "© 2025 CVS Health and/or one of its affiliates. All rights reserved."
   ]
  }
 ],
 "metadata": {
  "environment": {
   "kernel": "uqlm_my_test",
   "name": "workbench-notebooks.m126",
   "type": "gcloud",
   "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/workbench-notebooks:m126"
  },
  "kernelspec": {
   "display_name": "uqlm_my_test",
   "language": "python",
   "name": "uqlm_my_test"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}