אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Methods in question-answering (QA) that transform texts detailing processes into an intermediate code representation, subsequently executed to generate a response to the presented question, have demonstrated promising results in analyzing scientific texts that describe intricate processes. The limitations of these existing text-to-code models are evident when attempting to solve QA problems that require knowledge beyond what is presented in the input text. We propose a novel domain-agnostic model to address the problem by leveraging domain-specific and open-source code libraries. We introduce an innovative QA text-to-code algorithm that learns to represent and utilize external APIs from code repositories, such as GitHub, within the intermediate code representation. The generated code is then executed to answer a question about a text. We present three QA datasets, focusing on scientific problems in the domains of chemistry, astronomy, and biology, for the benefit of the community. Our study demonstrates that our proposed method is a competitive alternative to current state-of-the-art (SOTA) QA text-to-code models and generic SOTA QA models.