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Enhancing LLM Reliability: Detecting Confabulations with Semantic Entropy

Marktechpost

Semantic entropy is a method to detect confabulations in LLMs by measuring their uncertainty over the meaning of generated outputs. This technique leverages predictive entropy and clusters generated sequences by semantic equivalence using bidirectional entailment.

LLM 102
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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

Rerank can improve the relevance of search results from lexical or semantic search. Rerank works by computing semantic relevance scores for documents that are retrieved by a search system and ranking the documents based on these scores. Adding Rerank to an application requires only a single line of code change.

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Llama3 is out and it is awesome!

Bugra Akyildiz

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. Distilabel is the framework for synthetic data and AI feedback for AI engineers that require high-quality outputs, full data ownership, and overall efficiency.

LLM 52
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Using Hugging Face Transformers for Sentiment Analysis in R

Heartbeat

Word embeddings can then be used to predict numerical variables, compute semantic similarity scores across texts, visually represent statistically significant words across multiple dimensions, and much more. Text package: Hugging Face's Transformers in R can be used to convert text variables into word embeddings.

BERT 52
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Small but Mighty: The Enduring Relevance of Small Language Models in the Age of LLMs

Marktechpost

Techniques such as BERTSCORE and BARTSCORE employ smaller models to compute semantic similarity and evaluate texts from various perspectives. In addition to that, proxy models can predict LLM performance, reducing computational costs during model selection.

BERT 122