Given the structured nature of the data, how does this compare to running a specialized classification model that looks for specific words in a review and uses those to assign Chefs to Restaurants? With some fine tuning, you might get more consistent results than feeding the reviews into a generative model.
The data is initially not at all structured, and the critics talk about a chef's CV in passing. For instance, take this example:
> At Grenat, Antoine Joannier and Neil Mahatsry are bathed in an ardent red glow, much like the pomegranate-toned walls of their space. After working together at La Brasserie Communale, where they first met, the duo is now firing on all cylinders in the heart of Marseille, where Antoine tends to guests seated around blonde wood tables, delivering dishes ignited by Neil behind the bar. From oysters to prime cuts of red meat, […]
I tried using NER models and the results were not great. Furthermore, these models do not extract relationships between entities (other models exist for that though). Haven't tried fine-tuning at all!
There is also a lot of variation in the ways of presenting a chef's prior restaurants, which makes this a good use-case for LLMs.
LLMs have without a doubt replaced NER models and libraries like SpaCy. At least for my use-cases, creating ontologies and populating knowledge graphs.
