Statistical Language Models
Statistical language models are probabilistic models that predict the likelihood of sequences of words in a language.
Statistical language models are probabilistic models that predict the likelihood of sequences of words in a language.
Symbolic natural language understanding involves the use of symbolic representations and rules to interpret and understand human language.
The Solver is an agent in the EvoLMM framework that answers questions generated by the Proposer through internal consistency.
Benchmarking is the process of comparing performance metrics of systems or models against a standard or set of criteria.
Word sense disambiguation is the process of determining which meaning of a word is activated by its use in a particular context.
Anomaly detection is the process of identifying unexpected or irregular patterns in data that do not conform to expected behavior.
A GUI agent is an intelligent software entity designed to interact with graphical user interfaces, performing tasks typically executed by human users.
The task of analyzing and interpreting long-duration video content for various applications, such as summarization or event detection.
Techniques in neural networks that allow models to focus on specific parts of the input data when making predictions.
Mathematical models that describe a system using state variables and equations governing their evolution over time.