Downstream Task: Table Question Answering
Explore how to enhance chatbot functionality by using large language models to perform table question answering. This lesson guides you through understanding the process of interpreting structured tabular data to answer questions accurately. Learn the step-by-step coding implementation using the TAPAS model, including data preparation, tokenization, and inference, to apply these concepts in your projects.
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Downstream tasks leveraging LLMs are essential for developing chatbot functionalities. These tasks, such as Table Question Answering enrich the chatbots with the ability to access and interpret structured information.
Accessing structured information via Table Question Answering
Table Question Answering refers to the process of understanding and interpreting data presented in a tabular form to answer questions related to the information within these tables. This task combines natural language processing and the understanding of structured data for providing answers to queries. Language models analyze the textual descriptions in the tables (categorical values) and the numerical values in the tables. This technique is very challenging because it requires the models to understand the semantic relationships between the table headers and the table cells. Language models apply logical reasoning across both the text ...