This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
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.
Rerank can improve the relevance of search results from lexical or semantic search. Rerank works by computingsemantic 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.
Whether you are working on a predictive model that computessemantic 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.
Word embeddings can then be used to predict numerical variables, computesemantic 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.
Techniques such as BERTSCORE and BARTSCORE employ smaller models to computesemantic similarity and evaluate texts from various perspectives. In addition to that, proxy models can predict LLM performance, reducing computational costs during model selection.
We organize all of the trending information in your field so you don't have to. Join 15,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content