The Shift from Static Corpora to Generative Models

Traditional terminology extraction relies on building and analyzing specialized corpora—large, curated datasets of domain-specific text. This process is resource-intensive, requiring significant time for data collection, cleaning, and linguistic processing. The emergence of Large Language Models (LLMs) introduces a paradigm shift: instead of querying a static dataset, builders can query a model's internal knowledge base to identify, define, and extract specialized terms. This approach offers immediate accessibility, as LLMs can often identify domain-specific jargon without the need for a pre-compiled corpus.

Trade-offs: Flexibility vs. Verifiability

The primary advantage of using LLMs for terminology extraction is their ability to handle low-resource domains where a sufficiently large corpus does not exist. However, this flexibility introduces significant risks. Unlike corpus-based methods, where every extracted term can be traced back to a specific source document, LLMs are probabilistic. They are susceptible to 'hallucinating' terms that sound plausible but do not exist or misrepresenting the nuances of specialized jargon.

Practical Implementation Strategy

For production-grade applications, the research suggests a hybrid approach rather than a total replacement of traditional methods. Builders should treat LLMs as a tool for initial discovery and candidate generation, followed by a verification layer that cross-references the model's output against authoritative sources or smaller, high-quality reference corpora. Relying solely on an LLM for critical terminology tasks risks introducing inaccuracies that are difficult to audit, whereas using them to augment existing pipelines can significantly accelerate the extraction process while maintaining data integrity.