groundedai_vs_gpt4_hallucination_detection.ipynb

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      "metadata": {
        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/jlonge4/gen_ai_utils/blob/main/groundedai_vs_gpt4_hallucination_detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "rQhXmcSInkfB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b7faa190-d70b-4a23-c18b-4a8731f99065"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Successfully installed art-6.2 flash-attn-2.6.3 h11-0.14.0 httpcore-1.0.5 httpx-0.27.2 jiter-0.5.0 openai-1.46.0 peft-0.12.0 pycm-4.0\n"
          ]
        }
      ],
      "source": [
        "!pip install accelerate peft flash-attn pycm openai"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('test_hallucination.csv')\n",
        "df['hallucination'] = df['hallucination'].map({'yes': 1, 'no': 0})\n",
        "df.rename(columns={'hallucination': 'is_hallucination'}, inplace=True)\n",
        "df = df.rename(columns={'user_query': 'input', 'chatgpt_response': 'output', 'knowledge': 'reference'})\n",
        "df = df.sample(frac=1, random_state=42).reset_index(drop=True)"
      ],
      "metadata": {
        "id": "vdQKyXVlaGB9"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "lJpUAC5BaNeF",
        "outputId": "e9788128-4254-4b56-e3f8-2b8f2f3e87f4"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
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              "                                               input  \\\n",
              "0  [Human]: Do you like the Princess Diaries? Who...   \n",
              "1  Dua Lipa, an English singer, songwriter and mo...   \n",
              "2  What is the nickname of the airplane model flo...   \n",
              "3  [Human]: I think Thomas Muller is cute, but ha...   \n",
              "4  [Human]: Could you recommend any movies simila...   \n",
              "\n",
              "                                              output  is_hallucination  \\\n",
              "0      At the moment i do not have anything in mind.                 0   \n",
              "1                                               2017                 0   \n",
              "2                                     \"Triple Seven\"                 0   \n",
              "3  He actually is on the Germany chess team, and ...                 1   \n",
              "4  Yeah, it was one of the best thriller films I ...                 0   \n",
              "\n",
              "                                           reference  \n",
              "0        The Princess Diaries has genre Romance Film  \n",
              "1   Her self-titled debut studio album was releas...  \n",
              "2   The aircraft, a Boeing 777-200ER operated by ...  \n",
              "3  Thomas Müller : `~Player statistics`: Germany ...  \n",
              "4  Good Will Hunting has genre DramaDrama has exa...  "
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              "\n",
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              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"input\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 200,\n        \"samples\": [\n          \"What state is Ruxton Park and Pikes Peak located in?\",\n          \"Tightrope starred the actor who achieved success in what TV series?\",\n          \"Generate a list of five items an individual needs to make a snowman.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"output\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 200,\n        \"samples\": [\n          \"Colorado\",\n          \"Rawhide\",\n          \"1. Snow\\n2. Carrot\\n3. Coal or stones for eyes and mouth\\n4. Sticks for arms\\n5. Scarf or hat (optional)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_hallucination\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"reference\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 184,\n        \"samples\": [\n          \"The Broadway cast recording of the musical My Fair Lady was released as an album on April 2, 1956. The songs were composed by Frederick Loewe with lyrics by Alan Jay Lerner and conducted by Franz Allers, while the cast included stars Julie Andrews and Rex Harrison.Dame Julia Elizabeth \\\"Julie\\\" Andrews, {'1': \\\", '2': \\\", '3': \\\", '4': \\\"} (\\\"n\\u00e9e\\\" Wells; born 1 October 1935) is an English actress, singer, and author.\",\n          \"Rafael Nadal : `~Matches Lost`: Roger FedererRoger Federer is a/an AthleteAthlete has example: Roger Federer\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import PeftModel, PeftConfig\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
        "from transformers import pipeline\n",
        "import torch\n",
        "\n",
        "base_model_id = \"microsoft/Phi-3.5-mini-instruct\"\n",
        "groundedai_eval_id = \"grounded-ai/phi3.5-hallucination-judge\"\n",
        "groundedai_eval_id = 'Jlonge4/outputs'\n",
        "if torch.cuda.is_bf16_supported():\n",
        "  compute_dtype = torch.bfloat16\n",
        "  attn_implementation = 'flash_attention_2'\n",
        "# If bfloat16 is not supported, 'compute_dtype' is set to 'torch.float16' and 'attn_implementation' is set to 'sdpa'.\n",
        "else:\n",
        "  compute_dtype = torch.float16\n",
        "  attn_implementation = 'sdpa'\n",
        "\n",
        "config = PeftConfig.from_pretrained(groundedai_eval_id)\n",
        "base_model = AutoModelForCausalLM.from_pretrained(base_model_id, attn_implementation=attn_implementation,torch_dtype=compute_dtype)\n",
        "model_peft = PeftModel.from_pretrained(base_model, groundedai_eval_id, config=config)\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(base_model_id)\n",
        "\n",
        "merged_model = model_peft.merge_and_unload()\n",
        "merged_model.to('cuda')"
      ],
      "metadata": {
        "id": "ZXrCkdtXSwEE",
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          ]
        },
        "outputId": "4ff8fb38-b6d0-4475-8613-68bb1f9361e2"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
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          },
          "metadata": {}
        },
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        },
        {
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            "application/vnd.jupyter.widget-view+json": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "6d1fcfdc2d644e128d377589088c06a9"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        {
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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            ],
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          "metadata": {}
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        {
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            ],
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              "model_id": "ea87ef1318444184aac5e894c61eae35"
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Phi3ForCausalLM(\n",
              "  (model): Phi3Model(\n",
              "    (embed_tokens): Embedding(32064, 3072, padding_idx=32000)\n",
              "    (embed_dropout): Dropout(p=0.0, inplace=False)\n",
              "    (layers): ModuleList(\n",
              "      (0-31): 32 x Phi3DecoderLayer(\n",
              "        (self_attn): Phi3FlashAttention2(\n",
              "          (o_proj): Linear(in_features=3072, out_features=3072, bias=False)\n",
              "          (qkv_proj): Linear(in_features=3072, out_features=9216, bias=False)\n",
              "          (rotary_emb): Phi3LongRoPEScaledRotaryEmbedding()\n",
              "        )\n",
              "        (mlp): Phi3MLP(\n",
              "          (gate_up_proj): Linear(in_features=3072, out_features=16384, bias=False)\n",
              "          (down_proj): Linear(in_features=8192, out_features=3072, bias=False)\n",
              "          (activation_fn): SiLU()\n",
              "        )\n",
              "        (input_layernorm): Phi3RMSNorm((3072,), eps=1e-05)\n",
              "        (resid_attn_dropout): Dropout(p=0.0, inplace=False)\n",
              "        (resid_mlp_dropout): Dropout(p=0.0, inplace=False)\n",
              "        (post_attention_layernorm): Phi3RMSNorm((3072,), eps=1e-05)\n",
              "      )\n",
              "    )\n",
              "    (norm): Phi3RMSNorm((3072,), eps=1e-05)\n",
              "  )\n",
              "  (lm_head): Linear(in_features=3072, out_features=32064, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def format_func(reference, query, response):\n",
        "  if len(str(reference)) > 1:\n",
        "    prompt = f\"\"\"Your job is to evaluate whether a machine learning model has hallucinated or not.\n",
        "    You are given the following information:\n",
        "      ####INFO####\n",
        "      [Knowledge]: {reference}\n",
        "      [User Input]: {query}\n",
        "      [Model Response]: {response}\n",
        "      ####END INFO####\n",
        "      Based on the information provided, is the model response a hallucination? Respond with only \"yes\" or \"no\"\n",
        "      \"\"\"\n",
        "  else:\n",
        "    prompt = f\"\"\"Your job is to evaluate whether a machine learning model has hallucinated or not.\n",
        "    A hallucination occurs when the response is coherent but factually incorrect or nonsensical\n",
        "    outputs that are not grounded in the provided context.\n",
        "    You are given the following information:\n",
        "      ####INFO####\n",
        "      [User Input]: {query}\n",
        "      [Model Response]: {response}\n",
        "      ####END INFO####\n",
        "      Based on the information provided, is the model output a hallucination? Respond with only \"yes\" or \"no\"\n",
        "      \"\"\"\n",
        "  return prompt"
      ],
      "metadata": {
        "id": "hWTjA92botjC"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
        "\n",
        "def run_merged_model(reference, query, response):\n",
        "  input = format_func(reference, query, response)\n",
        "  messages = [\n",
        "      {\"role\": \"user\", \"content\": input}\n",
        "  ]\n",
        "\n",
        "  pipe = pipeline(\n",
        "      \"text-generation\",\n",
        "      model=merged_model,\n",
        "      tokenizer=tokenizer,\n",
        "      device='cuda'\n",
        "  )\n",
        "\n",
        "  generation_args = {\n",
        "      \"max_new_tokens\": 2,\n",
        "      \"return_full_text\": False,\n",
        "      \"temperature\": 0.0001,\n",
        "      \"do_sample\": True,\n",
        "  }\n",
        "\n",
        "  output = pipe(messages, **generation_args)\n",
        "  torch.cuda.empty_cache()\n",
        "  return output[0]['generated_text'].strip().lower()"
      ],
      "metadata": {
        "id": "351MnJ9prVlZ"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from openai import OpenAI\n",
        "from getpass import getpass\n",
        "\n",
        "API_KEY = getpass(\"Enter your OpenAI API key: \")\n",
        "client = OpenAI(api_key=API_KEY)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "th43rRM0aoBy",
        "outputId": "8b23f3d5-3c34-4fa6-96d7-9c6442005535"
      },
      "execution_count": 11,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter your OpenAI API key: ··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def run_gpt4(reference, query, response):\n",
        "  input = format_func(reference, query, response)\n",
        "\n",
        "  completion = client.chat.completions.create(\n",
        "      # model=\"gpt-4o\",\n",
        "      model='gpt-4o-mini',\n",
        "      # model='gpt-4',\n",
        "      messages=[\n",
        "          {\"role\": \"user\", \"content\": input}\n",
        "      ]\n",
        "  )\n",
        "  return completion.choices[0].message.content.strip().lower()"
      ],
      "metadata": {
        "id": "DDTYAd6AaFFv"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_balanced = df.copy()"
      ],
      "metadata": {
        "id": "RHjIrqYIXCh_"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_balanced['predictions-gai'] = df_balanced.apply(lambda x: run_merged_model(x['reference'], x['input'], x['output']), axis=1)"
      ],
      "metadata": {
        "id": "MshVrzz0hU2w"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_balanced['predictions-gpt4o'] = df_balanced.apply(lambda x: run_gpt4(x['reference'], x['input'], x['output']), axis=1)"
      ],
      "metadata": {
        "id": "N8SZOUn6aAJr"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_balanced['predictions-gpt4o-mini'] = df_balanced.apply(lambda x: run_gpt4(x['reference'], x['input'], x['output']), axis=1)"
      ],
      "metadata": {
        "id": "mwJwXMEGcziM"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_balanced['predictions-gpt4'] = df_balanced.apply(lambda x: run_gpt4(x['reference'], x['input'], x['output']), axis=1)"
      ],
      "metadata": {
        "id": "6OC9Pr78eOIh"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(df_balanced['predictions-gai']) == len(df_balanced['predictions-gpt4']) == len(df_balanced['predictions-gpt4o-mini']) == len(df_balanced['predictions-gpt4'])"
      ],
      "metadata": {
        "id": "37FVsgIjimIf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc7b1c7a-4775-491f-b44e-79b98c4645f4"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_balanced.reference.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RSg1QRMXXdL8",
        "outputId": "2a1b3037-6352-41d4-b3ea-e0e7812ed433"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.series.Series'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Series name: reference\n",
            "Non-Null Count  Dtype \n",
            "--------------  ----- \n",
            "184 non-null    object\n",
            "dtypes: object(1)\n",
            "memory usage: 1.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "PREDICTION_MAP = {\"no\": 0, \"yes\": 1}\n",
        "df_balanced['predictions-gai'] = df_balanced['predictions-gai'].replace('no,', 'no')\n",
        "unique_predictions = df_balanced['predictions-gai'].unique()\n",
        "unique_predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eaL7b7P-oX8g",
        "outputId": "8f6f2855-3217-4489-de73-38c1753fede9"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['yes', 'no'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_balanced['predictions-gpt4o'] = df_balanced['predictions-gpt4o'].replace('no.', 'no')\n",
        "unique_predictions = df_balanced['predictions-gpt4o'].unique()\n",
        "unique_predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0LJj5TzDcBVk",
        "outputId": "027e424f-f40f-42a4-d0d1-6985e0771aa4"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['no', 'yes'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unique_predictions = df_balanced['predictions-gpt4o-mini'].unique()\n",
        "unique_predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YkRaegm9dFfN",
        "outputId": "8c877f66-64bf-41a5-b0ee-0bc793f2115c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['no', 'yes'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unique_predictions = df_balanced['predictions-gpt4'].unique()\n",
        "unique_predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mXgnQTO6eY-7",
        "outputId": "14e5c629-f397-4455-847f-de1a9363ae09"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['no', 'yes'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_balanced.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "i52dt4DjhX7B",
        "outputId": "c43f58d4-ad86-4e1c-b94c-612a6d408edb"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               input  \\\n",
              "0  [Human]: Do you like the Princess Diaries? Who...   \n",
              "1  Dua Lipa, an English singer, songwriter and mo...   \n",
              "2  What is the nickname of the airplane model flo...   \n",
              "3  [Human]: I think Thomas Muller is cute, but ha...   \n",
              "4  [Human]: Could you recommend any movies simila...   \n",
              "\n",
              "                                              output  is_hallucination  \\\n",
              "0      At the moment i do not have anything in mind.                 0   \n",
              "1                                               2017                 0   \n",
              "2                                     \"Triple Seven\"                 0   \n",
              "3  He actually is on the Germany chess team, and ...                 1   \n",
              "4  Yeah, it was one of the best thriller films I ...                 0   \n",
              "\n",
              "                                           reference predictions-gai  \\\n",
              "0        The Princess Diaries has genre Romance Film             yes   \n",
              "1   Her self-titled debut studio album was releas...              no   \n",
              "2   The aircraft, a Boeing 777-200ER operated by ...              no   \n",
              "3  Thomas Müller : `~Player statistics`: Germany ...             yes   \n",
              "4  Good Will Hunting has genre DramaDrama has exa...              no   \n",
              "\n",
              "  predictions-gpt4 predictions-gpt4o predictions-gpt4o-mini  \n",
              "0               no                no                     no  \n",
              "1               no                no                     no  \n",
              "2               no                no                     no  \n",
              "3              yes               yes                    yes  \n",
              "4               no               yes                    yes  "
            ],
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              "type": "dataframe",
              "variable_name": "df_balanced",
              "summary": "{\n  \"name\": \"df_balanced\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"input\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 200,\n        \"samples\": [\n          \"What state is Ruxton Park and Pikes Peak located in?\",\n          \"Tightrope starred the actor who achieved success in what TV series?\",\n          \"Generate a list of five items an individual needs to make a snowman.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"output\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 200,\n        \"samples\": [\n          \"Colorado\",\n          \"Rawhide\",\n          \"1. Snow\\n2. Carrot\\n3. Coal or stones for eyes and mouth\\n4. Sticks for arms\\n5. Scarf or hat (optional)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_hallucination\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"reference\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 184,\n        \"samples\": [\n          \"The Broadway cast recording of the musical My Fair Lady was released as an album on April 2, 1956. The songs were composed by Frederick Loewe with lyrics by Alan Jay Lerner and conducted by Franz Allers, while the cast included stars Julie Andrews and Rex Harrison.Dame Julia Elizabeth \\\"Julie\\\" Andrews, {'1': \\\", '2': \\\", '3': \\\", '4': \\\"} (\\\"n\\u00e9e\\\" Wells; born 1 October 1935) is an English actress, singer, and author.\",\n          \"Rafael Nadal : `~Matches Lost`: Roger FedererRoger Federer is a/an AthleteAthlete has example: Roger Federer\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predictions-gai\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predictions-gpt4\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predictions-gpt4o\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predictions-gpt4o-mini\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Grounded-AI Hallucination Model"
      ],
      "metadata": {
        "id": "qHlbbpt57Ctn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.metrics import classification_report\n",
        "from pycm import ConfusionMatrix\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "true_labels = df_balanced[\"is_hallucination\"].tolist()  # These are already 0 and 1\n",
        "hallucination_classifications = df_balanced[\"predictions-gai\"].tolist()\n",
        "# Convert predictions from \"yes\"/\"no\" to 1/0\n",
        "predicted_labels = [PREDICTION_MAP[pred] for pred in hallucination_classifications]\n",
        "\n",
        "# Generate classification report\n",
        "print(classification_report(true_labels, predicted_labels))\n",
        "\n",
        "pycm_cm = ConfusionMatrix(actual_vector=true_labels, predict_vector=predicted_labels)\n",
        "pycm_cm.plot(cmap=plt.colormaps[\"Blues\"], number_label=True, normalized=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "id": "nsXjxSXe7Eo0",
        "outputId": "2d52373f-cf9f-4af5-9d02-3dfb7a610ef3"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.85      0.81       100\n",
            "           1       0.84      0.76      0.80       100\n",
            "\n",
            "    accuracy                           0.81       200\n",
            "   macro avg       0.81      0.80      0.80       200\n",
            "weighted avg       0.81      0.81      0.80       200\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Confusion Matrix (Normalized)'}, xlabel='Predicted Classes', ylabel='Actual Classes'>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# GPT-4o"
      ],
      "metadata": {
        "id": "yCDaD6FMcgaC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "true_labels = df_balanced[\"is_hallucination\"].tolist()  # These are already 0 and 1\n",
        "hallucination_classifications = df_balanced[\"predictions-gpt4o\"].tolist()\n",
        "# Convert predictions from \"yes\"/\"no\" to 1/0\n",
        "predicted_labels_gpt4o = [PREDICTION_MAP[pred] for pred in hallucination_classifications]\n",
        "\n",
        "# Generate classification report\n",
        "print(classification_report(true_labels, predicted_labels_gpt4o))\n",
        "\n",
        "pycm_cm = ConfusionMatrix(actual_vector=true_labels, predict_vector=predicted_labels_gpt4o)\n",
        "pycm_cm.plot(cmap=plt.colormaps[\"Blues\"], number_label=True, normalized=True)"
      ],
      "metadata": {
        "id": "hrd373vworHn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "outputId": "fd05afde-40a2-4965-ab15-7901f37d0191"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.71      0.78       100\n",
            "           1       0.76      0.90      0.82       100\n",
            "\n",
            "    accuracy                           0.81       200\n",
            "   macro avg       0.82      0.80      0.80       200\n",
            "weighted avg       0.82      0.81      0.80       200\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Confusion Matrix (Normalized)'}, xlabel='Predicted Classes', ylabel='Actual Classes'>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# GPT4o-mini"
      ],
      "metadata": {
        "id": "n10Qn5IAdLwP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "true_labels = df_balanced[\"is_hallucination\"].tolist()  # These are already 0 and 1\n",
        "hallucination_classifications = df_balanced[\"predictions-gpt4o-mini\"].tolist()\n",
        "# Convert predictions from \"yes\"/\"no\" to 1/0\n",
        "predicted_labels_gpt4o_mini = [PREDICTION_MAP[pred] for pred in hallucination_classifications]\n",
        "\n",
        "# Generate classification report\n",
        "print(classification_report(true_labels, predicted_labels_gpt4o_mini))\n",
        "\n",
        "pycm_cm = ConfusionMatrix(actual_vector=true_labels, predict_vector=predicted_labels_gpt4o_mini)\n",
        "pycm_cm.plot(cmap=plt.colormaps[\"Blues\"], number_label=True, normalized=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "id": "OUilpy3Qcjx7",
        "outputId": "28302802-9988-46e9-c382-4fe6ac371333"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.79      0.81       100\n",
            "           1       0.80      0.83      0.81       100\n",
            "\n",
            "    accuracy                           0.81       200\n",
            "   macro avg       0.81      0.81      0.81       200\n",
            "weighted avg       0.81      0.81      0.81       200\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Confusion Matrix (Normalized)'}, xlabel='Predicted Classes', ylabel='Actual Classes'>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# GPT4"
      ],
      "metadata": {
        "id": "YlFr5ACWfKAi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "true_labels = df_balanced[\"is_hallucination\"].tolist()  # These are already 0 and 1\n",
        "hallucination_classifications = df_balanced[\"predictions-gpt4\"].tolist()\n",
        "# Convert predictions from \"yes\"/\"no\" to 1/0\n",
        "predicted_labels_gpt4 = [PREDICTION_MAP[pred] for pred in hallucination_classifications]\n",
        "\n",
        "# Generate classification report\n",
        "print(classification_report(true_labels, predicted_labels_gpt4))\n",
        "\n",
        "pycm_cm = ConfusionMatrix(actual_vector=true_labels, predict_vector=predicted_labels_gpt4)\n",
        "pycm_cm.plot(cmap=plt.colormaps[\"Blues\"], number_label=True, normalized=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "id": "o2EVuunNds6n",
        "outputId": "b2d87284-7a5b-4cdb-fc22-0fe4a9cd6cab"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.79      0.89      0.84       100\n",
            "           1       0.88      0.77      0.82       100\n",
            "\n",
            "    accuracy                           0.83       200\n",
            "   macro avg       0.83      0.83      0.83       200\n",
            "weighted avg       0.83      0.83      0.83       200\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Confusion Matrix (Normalized)'}, xlabel='Predicted Classes', ylabel='Actual Classes'>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Results"
      ],
      "metadata": {
        "id": "ckT2zs1nfqPs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Calculate accuracies\n",
        "accuracy_gpt4 = accuracy_score(true_labels, predicted_labels_gpt4)\n",
        "accuracy_gpt4o = accuracy_score(true_labels, predicted_labels_gpt4o)\n",
        "accuracy_gpt4o_mini = accuracy_score(true_labels, predicted_labels_gpt4o_mini)\n",
        "accuracy_gai_model = accuracy_score(true_labels, predicted_labels)\n",
        "\n",
        "# Create data for plotting\n",
        "models = ['GroundedAI','GPT-4o mini','GPT-4o', 'GPT-4']\n",
        "accuracies = sorted([accuracy_gpt4, accuracy_gpt4o, accuracy_gpt4o_mini, accuracy_gai_model])\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "bars = plt.bar(models, accuracies, color=['blue', 'green', 'red', 'purple'])\n",
        "\n",
        "plt.title('Accuracy Comparison of Models')\n",
        "plt.xlabel('Models')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim(0, 1)\n",
        "\n",
        "for bar in bars:\n",
        "    height = bar.get_height()\n",
        "    plt.text(bar.get_x() + bar.get_width()/2., height,\n",
        "             f'{height:.2f}',\n",
        "             ha='center', va='bottom')\n",
        "\n",
        "# Display the plot\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "duNz2tOGfrC7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "405a3dcb-5dd0-4537-bd06-bb91ed4d8a2b"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2bhYIA_frzDv"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}