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2024.02.07 Recording (Passcode: L58@v7Dg) Slides | Slides from the talk by Sebastian Lobentanzer
2024.03.20 Recording (Passcode: LZ!jZT4z) Slides | Architecture diagram in Draw.io | Architecture diagram PNG file
2024.04.17 Recording (Passcode: Yn2!5qJK) Slides | Slides from the talk by Jon Stevens
2024.08.07 Recording (Passcode: %.1&ukfM) Slides | Includes a talk by Peter Dorr: SPARQL query code generation with LLMs
2024.09.04 Recording (Passcode: t3?B*?CX) Slides | Includes a talk by Oleg Stroganov on agents controlling the actions of LLMs | Slides from the talk by Oleg Stroganov
Lessons Learned
The highest risk item is generation of the structured query (Cyphrer or SPARQL) from a plain English request. Some publications estimate success rate of about 48% on the first attempt.
The structure of the database used for queries matters. LLMs can easier produce meaningful structured queries for databases with flat, simple structure.
Practically useful system requires filtering or secondary mining of output in addition to natural language narration.
It is extremely important to implement a reliable named entity recognition system. The same acronym can refer to completely different entities, which can be differentiated either from the context (hard) or by asking clarifying questions. Must also map synonyms. Without these measures naïve queries in a RAG environment will fail.
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