Large Language Model Techniques (1)

Abstract:
A language model is a probabilistic representation of a natural language, capable of estimating the likelihood of word
sequences based on the textual data it was trained on, encompassing one or more languages. The initial significant
statistical language model emerged in 1980, followed by IBM's 'Shannon-style' experiments in the same decade.
These experiments involved observing and analyzing human subjects' performance in predicting or correcting text,
leading to the identification of potential avenues for enhancing language modeling.
These models find application in various tasks, such as speech recognition (aiding in avoiding predictions of
improbable or nonsensical sequences), machine translation, natural language generation (producing text that
resembles human language more closely), optical character recognition, handwriting recognition, grammar
induction, and information retrieval.
 

Workshop Outline:
In this workshop you will be introduced to the techniques that are used in LM:

Prerequisites:
Computer Science knowledge
 

Biography:
Zahra is a PhD student at the University of Windsor. Her research is in the area of Information Retrieval. Particularly
about how to improve query refinement as a technique to make search engines to retrieve the most related documents
based on users' initial query.