Remove Computational Semantics Remove Large Language Models Remove LLM
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Enhancing LLM Reliability: Detecting Confabulations with Semantic Entropy

Marktechpost

Understanding and addressing these nuanced error types is crucial for improving LLM reliability. Researchers from the OATML group at the University of Oxford have developed a statistical approach to detect a specific type of error in LLMs, known as “confabulations.” Check out the Paper , Project , and GitHub.

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Build enterprise-ready generative AI solutions with Cohere foundation models in Amazon Bedrock and Weaviate vector database on AWS Marketplace

AWS Machine Learning Blog

Generative AI solutions have the potential to transform businesses by boosting productivity and improving customer experiences, and using large language models (LLMs) with these solutions has become increasingly popular. Rerank can improve the relevance of search results from lexical or semantic search.

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Small but Mighty: The Enduring Relevance of Small Language Models in the Age of LLMs

Marktechpost

Large Language Models (LLMs) have revolutionized natural language processing in recent years. The pre-train and fine-tune paradigm, exemplified by models like ELMo and BERT, has evolved into prompt-based reasoning used by the GPT family. SMs play a crucial role in enhancing LLMs through data curation.

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