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Related Concept Videos

Language Development01:22

Language Development

Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
Language01:16

Language

Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
Corballis and Suddendorf (2007) and Tomasello and Rakoczy (2003) highlight the role of language in...
Components of Language01:24

Components of Language

Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs. “eh”). Phonemes combine to...
Language and Cognition01:27

Language and Cognition

Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
Genetic Lingo01:11

Genetic Lingo

Overview
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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Related Experiment Video

Updated: May 24, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Context-Free Grammar-Guided Generation of FHIR Resources Using Large Language Models.

Johann Frei1, Frank Kramer1

  • 1University of Augsburg.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

Synthesizing grammars from FHIR schemas enables Large Language Models (LLMs) to generate valid healthcare data. This approach improves structured data generation for clinical pipelines.

Keywords:
Constrained decodingFHIRGrammar-guided generationLarge language modelsStructured output

Related Experiment Videos

Last Updated: May 24, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

  • Computational linguistics
  • Health informatics
  • Artificial intelligence

Background:

  • Large Language Models (LLMs) excel at medical data tasks but produce unstructured outputs.
  • Integrating LLM outputs into clinical data pipelines is challenging due to lack of structure.
  • Fast Healthcare Interoperability Resources (FHIR) offers standardized medical data exchange but is complex to generate.

Purpose of the Study:

  • To develop a method for generating schema-conformant structured data using LLMs.
  • To address the limitations of LLM unstructured outputs in clinical data integration.
  • To enable reliable generation of complex FHIR resources.

Main Methods:

  • Developed a pipeline to synthesize Context-Free Grammars (CFGs) from FHIR JSON Schemas.
  • Employed relaxed key ordering in CFG generation for structural validity.
  • Evaluated CFG-guided LLM generation against JSON Schema-guided and unguided methods using FHIR R5 resources.

Main Results:

  • CFG-guided LLM generation achieved 76.5% validity for FHIR resources, significantly outperforming JSON Schema-guided (41.2%) and unguided (17.6%) methods.
  • The approach successfully enforced value constraints, including enumerated types and UCUM unit codes.
  • Demonstrated superior structural compliance compared to other generation methods.

Conclusions:

  • Synthesizing CFGs from FHIR schemas is an effective strategy for schema-conformant structured data generation with LLMs.
  • This method enhances the reliability and validity of LLM-generated healthcare data for clinical applications.
  • The open-source release of the grammar synthesis pipeline aims to promote adoption in healthcare information systems.