Closed-book question answering is a challenging task where a model is expected to generate accurate answers to questions without access to external information or documents during inference. This approach relies solely on the pre-trained knowledge embedded within the model, making it ideal for scenarios where retrieval-based methods are not feasible.
In this page, we will explore how to implement a pipeline that can automatically answer questions in a closed-book setting using state-of-the-art NLP techniques. We utilize a T5 Transformer model fine-tuned for closed-book question answering, providing accurate and contextually relevant answers to a variety of trivia questions.
The T5 (Text-To-Text Transfer Transformer) model by Google is a versatile transformer-based model designed to handle a wide range of NLP tasks in a unified text-to-text format. For closed-book question answering, T5 is fine-tuned to generate answers directly from its internal knowledge without relying on external sources.
The model processes input questions and, based on its training, generates a text response that is both relevant and accurate. This makes it particularly effective in applications where access to external data sources is limited or impractical.
The T5 model has been extensively benchmarked on various question-answering datasets, including natural questions and trivia challenges. In these evaluations, the closed-book variant of T5 has shown strong performance, often producing answers that are correct and contextually appropriate, even when the model is not allowed to reference any external data.
This makes the T5 model a powerful tool for generating answers in applications such as virtual assistants, educational tools, and any scenario where pre-trained knowledge is sufficient to provide responses.
The following example demonstrates how to implement a closed-book question answering pipeline using Spark NLP. The pipeline includes a document assembler, a sentence detector to identify questions, and the T5 model to generate answers.
Several T5 models are available, each pre-trained on different datasets and tasks. For closed-book question answering, it's important to select a model that has been fine-tuned specifically for this task. The model used in the example, "google_t5_small_ssm_nq," is optimized for answering trivia questions in a closed-book setting.
For more complex or varied question-answering tasks, consider using larger T5 models like T5-Base or T5-Large, which may offer improved accuracy and context comprehension. Explore the available models on the Spark NLP Models Hub to find the best fit for your application.