Abstract: Embodiments are disclosed for long document question answering using large language models. The method may include receiving a question for a document. A representation of the document may then be obtained. A large language model (LLM) is used to identify one or more sections of the document that are relevant to the question using the representation of the document. A document question answering model determines an answer to the question using the one or more sections of the document.

Abstract: Embodiments are disclosed for merging misidentified text structures. The method may include receiving a document including a plurality of text elements. The method may further include determining, by a machine learning model, a likelihood of merging a first text element of the plurality of text elements with a second text element of the plurality of text elements based on structure data and context data associated with the first and second text elements. The method may further include determining whether the likelihood of merging the first text element with the second text element satisfies a threshold. The method further includes responsive to determining that the likelihood of merging the first text element with the second text element satisfies the threshold, merging the first text element with the second text element.

Abstract: Embodiments are disclosed for long document question answering using large language models. The method may include receiving a question for a document. A representation of the document may then be obtained. A large language model (LLM) is used to identify one or more sections of the document that are relevant to the question using the representation of the document. A document question answering model determines an answer to the question using the one or more sections of the document.