Fig. 6: Diagrams of general information extraction and metal organic framework (MOF) information extraction using large language models (LLMs) for joint named entity and relation extraction (NERRE).
From: Structured information extraction from scientific text with large language models
In both panels, an LLM trained using a particular schema (desired output structure, far left) is prompted with raw text and produces a structured completion as JSON. This completion can then be parsed to construct relational diagrams (far right). Each task uses a different schema representing the desired output text structure from the LLM. a Schema and labeling example for the general materials-chemistry extraction task. Materials science research paper abstracts are passed to an LLM using General-JSON schema, which outputs a list of JSON objects representing individual material entries ordered by appearance in the text. Each material may have a name, formula, acronym, descriptors, applications, and/or crystal structure/phase information. b Schema and labeling example for the metal-organic frameworks extraction task. Similar to the General-JSON model, the MOF-JSON model takes in full abstracts from materials science research papers and outputs a list of JSON objects. In the example, only MOF name and application were present in the passage, and both MOFs (LaBTB and ZrPDA) are linked to both applications (luminescent and VOC sensor).
