Nov. 21, 2023, 8:29 p.m. | Adnan Hassan


Researchers from Stanford University and UNC Chapel Hill address the issue of factually inaccurate claims, known as hallucinations, produced by LLMs. Without human labeling, the researchers fine-tune LLMs to enhance factual accuracy in open-ended generation settings. Leveraging recent innovations in NLP, they employ methods to assess factuality through consistency with external knowledge bases and use […]

The post Stanford Researchers Innovate in Large Language Model Factuality: Automatic Preference Rankings and NLP Advancements for Error Reduction appeared first on MarkTechPost.

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