I’m trying to understand how entity extraction / entity-based methods are used inside LLM (GenAI) systems, especially in search-agent architectures. Recent papers like Qwen Agent CPT and DS-V3.2 mention using entities in their Search Agent pipeline — for example, detecting long-tail entities and generating QA pairs around them.
In my previous understanding, this kind of entity-based approach doesn’t really work well with traditional models like BERT or BiLSTM classifiers, especially when dealing with long-tail cases or generating QA data. But with modern LLMs, what does the actual workflow look like?
-
How do LLMs perform entity extraction in this context?
-
What does the prompting process look like?
-
How are entities turned into QA pairs or retrieval units?
-
Are there any best practices, templates, or empirical learnings?
I’m essentially looking for practical insights on how entities are operationalized in LLM-based agents.
Thanks!