📱Autocomplete on mobile phones uses word frequencies to suggest words as you type.
🔍Search engines score queries based on their frequency in other texts.
🧠Language models aim to assign a probability to every sentence and require more than just word frequencies.
🌐Modeling language involves considering grammar, style, and long-range dependencies.
💡Neural networks can approximate functions and are used to model language.
How does autocomplete work on mobile phones?
—Autocomplete on mobile phones suggests words as you type by using word frequencies and predicting the most probable next word.
How do search engines score queries?
—Search engines score queries by calculating their frequency in other texts, indicating how commonly they have been used.
What do language models aim to do?
—Language models aim to assign a probability to every sentence and require considering various factors like grammar, style, and long-range dependencies.
How do neural networks approximate language functions?
—Neural networks can approximate language functions by training on input and output pairs and optimizing their weights through gradient descent and backpropagation.
What are the important factors in modeling language using neural networks?
—Important factors include the capacity of the network, design decisions such as activation functions, and considering grammar, style, and long-range dependencies.
00:00Large language models, including autocomplete and search engine queries, use word frequencies and scores.
02:40Modeling language involves assigning probabilities to sentences, considering grammar, style, and long-range dependencies.
05:19Neural networks are universal approximators and can be used to model language functions.
06:57Training neural networks involves optimizing weights through gradient descent and backpropagation.
08:16Designing neural networks for modeling language requires capacity and careful design decisions.
