article thumbnail

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. Also, don’t forget to follow us on Twitter.

LLM 102
article thumbnail

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. Her focus area is AI/ML, and she helps developers learn about generative AI.

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

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. Orion is a fine-grained, interference-free scheduler for GPU sharing across ML workloads. Our framework ensures that the hard data work pays off.

LLM 52
article thumbnail

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
article thumbnail

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. If you like our work, you will love our newsletter.

BERT 122