🔑Large language models exhibit impressive capabilities beyond language modeling.
🚫These models often produce incorrect and contradictory answers and can generate dangerous or socially unacceptable content.
🔍Retrieval augmented language models retrieve relevant sections of documents to improve answer accuracy.
🌐Lack of non-linguistic knowledge and biases are areas that need improvement.
💡Making large language models more cost-effective to train and update is essential for their practical use.
What are the main problems with large language models?
—They often produce incorrect and contradictory answers, generate dangerous or socially unacceptable content, and lack non-linguistic knowledge.
How do retrieval augmented language models improve answer accuracy?
—They retrieve relevant sections of documents to provide additional context for better answering questions.
What areas need improvement in large language models?
—Non-linguistic knowledge and biases are areas that require further development.
Why is cost-effectiveness important for large language models?
—Training and updating these models are expensive, so making them more cost-effective is crucial for practical use.
Can large language models be trained on recent events?
—No, as training these models is time-consuming and costly, they may not have knowledge of recent events.
01:05Large language models have impressive capabilities beyond language modeling.
02:40These models often produce incorrect and contradictory answers and can generate dangerous or socially unacceptable content.
07:08Retrieval augmented language models retrieve relevant sections of documents to improve answer accuracy.
08:17Lack of non-linguistic knowledge and biases are areas that need improvement.
13:04Making large language models more cost-effective to train and update is essential for their practical use.
