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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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Llama3 is out and it is awesome!

Bugra Akyildiz

The data processing pipeline is optimized for LLM performance and ran on the 🏭 datatrove library, our large scale data processing library. Whether you are working on a predictive model that computes semantic similarity or the next generative model that is going to beat the LLM benchmarks.

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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

Despite their wealth of general knowledge, state-of-the-art LLMs only have access to the information they were trained on. This can lead to factual inaccuracies (hallucinations) when the LLM is prompted to generate text based on information they didn’t see during their training.

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

Marktechpost

This surprising trend highlights the continued relevance of SLMs and raises important questions about their role in the LLM era, a topic previously overlooked in research. This study examines the role of SMs in the LLM era from two perspectives: collaboration with LLMs and competition against them.

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