Fig. 5: Diagrams of doping 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 all three panels, an LLM trained to output a particular schema (far left) reads a raw text prompt and outputs a structured completion in that schema. The structured completion can then be parsed, decoded, and formatted to construct relational diagrams (far right). We show an example for each schema (desired output structure). Parsing refers to the reading of the structured output, while decoding refers to the programmatic (rule-based) conversion of that output into JSON form. Normalization and postprocessing are programmatic steps which transform raw strings (e.g., “Co+2") into structured entities with attributes (e.g., Element: Co, Oxidation state +2). a Raw sentences are passed to the model with Doping-English schema, which outputs newline-separated structured sentences that contain one host and one or more dopant entities. b Raw sentences are passed to a model with Doping-JSON schema, which outputs a nested JSON object. Each host entity has its own key-value pair, as does each dopant entity. There is also a list of host2dopant relations that links the corresponding dopant keys to each host key. c Example for the extraction with a model using the DopingExtra-English schema. This first part of the schema is the same as in a, but additional information is contained in doping modifiers, and results-bearing sentences are included at the end of the schema.
