Problem. There’s broad consensus today: LLMs are phenomenal personal productivity tools — they draft, summarize, and assist effortlessly.But there’s also growing recognition that they’re still not ready for enterprise-grade deployment. Why? Because enterprises need...
Rationale. NER tools are at the heart of how the scientific community is solving LLM issues using GraphRAG and NodeRAG architectures. LLMs need knowledge graphs to control hallucinations and make them more solid for enterprise-level use. And knowledge graphs are built...
As described in our previous post “Using Public Corpora to Build Your NER systems”, we are going to highlight areas where public datasets like OntoNotes or CoNLL can be improved. We will provide some tips on how to avoid these issues, whenever possible, using...
A robust discussion persists within the technical and academic communities about the suitability of LLMs for tasks like Named Entity Recognition (NER). While LLMs have demonstrated extraordinary capabilities across a wide range of language-related tasks, several...
On November 19, the Beyond Search Web log published a brief analysis of our multilingual NER (Named Entity Recognition system) technology. The post highlighted the challenges of handling Chinese personal names in English to enable accurate and consistent...
Getting GPT to Answer Consistently and with Style GPT, like other generative models, tends to provide disparate answers for the same question. Sometimes answers vary only slightly, but sometimes they are very inconsistent or even contradictory. Behavior of Standard...
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