Question answering is the process of using language models to solve or answer specific questions based on the provided context. It is similar to information extraction, but with a slight difference. Language models have the ability to infer something from the data that may not be directly present. Let’s explore this concept with an example.
In this example, we have a prompt that asks the model to answer a question based on the given context. The context mentions the origin of a therapeutic antibody called okt3, which was originally sourced from mice. The question asks about the origin of okt4, which is not mentioned in the context. When the model is asked this question, it responds with uncertainty and mentions that more information is needed to answer the question.
This example demonstrates the ability of language models to understand and interpret context. When provided with the right question and context, the model can accurately answer the question. However, when asked a question that is not present in the data, the model can still provide a response based on inference, but with uncertainty.
Overall, question answering with language models is a powerful tool that can provide relevant and accurate information based on the given context. It can assist in various domains, including healthcare, research, and education, by extracting valuable insights from textual data.
