notebooks/approaches_analysis.ipynb

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      "metadata": {},
      "source": [
        "# Анализ возможных подходов к задаче"
      ]
    },
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      "metadata": {
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      "source": [
        "## 1. Information Retrieval + Ranking\n",
        "\n",
        "В данном случае, Sberquad - это база знаний (corpus).\n",
        "\n",
        ">\\+ Легко имплементируется \\\n",
        ">\\+ Применим для больших баз данных\n",
        "\n",
        "\n",
        ">\\- Ограничен корпусом знаний \\\n",
        ">\\- Ограничен качеством ранжирования: если полученный документ не содержит точного ответа, точность системы падает \\\n",
        ">\\- Не всегда извлекает конкретные ответы, а только релевантные документы\n"
      ]
    },
    {
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      "metadata": {
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      "source": [
        "## 2. Information Retrieval + RAG\n",
        "\n",
        "В данном случае, Sberquad - это база знаний (corpus).\n",
        "\n",
        ">\\+ Более точные ответы за счет генеративной части \\\n",
        ">\\+ Может комбинировать несколько источников данных, улучшая точность\n",
        "\n",
        ">\\- Сложнее в реализации и требует больше ресурсов, как вычислительных, так и временных \\\n",
        ">\\- Генеративная часть может ошибаться или придумывать ответы"
      ]
    },
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      "metadata": {
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      "source": [
        "## 3. Encoder-decoder model\n",
        "\n",
        "В данном случае, датасет Sberquad - это датасет для тренировки модели.\n",
        "\n",
        ">\\+ Подходит для вопросов, где требуется сложная интерпретация данных или перефразирование \\\n",
        ">\\+ Способна генерировать ответы на естественном языке, даже если ответ не представлен явно в контексте\n",
        "\n",
        ">\\- Требует больше данных и **вычислительных ресурсов для обучения**\n",
        ">\\- Склонна к генерации некорректных или не полностью релевантных ответов; может создавать ответ, которого не было в контексте"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "## 4. Start/end token search\n",
        "\n",
        "В данном случае, датасет Sberquad - это датасет для тренировки модели.\n",
        "\n",
        "\n",
        ">\\+ Точный и детерминированный, извлекает конкретный ответ из контекста \\\n",
        ">\\+ Высокая эффективность, поиск токенов быстрее, чем генерация ответа\n",
        "\n",
        "\n",
        ">\\- Ограничен задачами, где ответ представлен явно в контексте \\\n",
        ">\\- Не подходит для более сложных вопросов, требующих понимания контекста"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lz19eQIOPJ0G"
      },
      "source": [
        "# Выводы\n",
        "\n",
        "Методы на основе IR, такие как ранжирование и RAG, обладают преимуществами при работе с большими базами знаний. Но для задачи, где ответы уже содержатся в контексте, такие методы могут быть излишне сложными и менее точными, так как требуют больше вычислительных ресурсов и могут не всегда извлекать конкретные ответы, полагаясь на сложные ранжирования или генерацию. В итоге, такие подходы чаще дают приблизительные или даже неверные ответы, особенно когда речь идет об **extractive QA**, а не generative QA.\n",
        "\n",
        "Подход с encoder-decoder моделью полезен в ситуациях, когда ответ не явно присутствует в тексте и требуется перефразирование или сложная интерпретация. Однако в задачах, где ответ уже содержится в контексте, этот подход может быть менее эффективным, так как генерация ответов может быть менее точной, требовать больше данных для обучения и быть более ресурсоемкой.\n",
        "\n",
        "Подводя итоги, метод с поиском start/end токенов, является самым эффективным для задачи extractive QA. Он нацелен на точное извлечение ответа, который уже есть в контексте, что делает его:\n",
        "\n",
        "* Более точным: модель чётко указывает на начало и конец ответа в тексте, что снижает вероятность ошибок.\n",
        "* Более быстрым: за счет того, что модель просто извлекает уже существующие фрагменты текста, а не генерирует новые ответы, она требует меньше вычислительных ресурсов и быстрее находит решение.\n",
        "\n",
        "Таким образом, для задачи построения QA-системы с явным извлечением ответа из текста наилучший подход - это **модель с поиском start/end токенов**. Она предлагает наивысшую точность и быстроту для extractive QA, делая её оптимальным выбором по сравнению с более сложными и ресурсоемкими методами. Поэтому выбор пал на модели с поиском start/end токенов, такие как multilingual DistilBert и RuBert."
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