![]() ![]() ![]() If we continually repeat this process with a sufficiently large and diverse dataset, we will end up with a high-quality LM that contains a relatively nuanced and useful understanding of language. Update our model based on the correct next word Try to predict the next word with our model To teach the model to do this well, we gather a large dataset of unlabeled text from the internet and train the model using a self-supervised language modeling objective. These models are trained to perform a single, simple task: predicting the next word (or token) in a sequence. Most modern language models that we will be talking about utilize a decoder-only transformer architecture. Self-supervised pre-training of a language model. Though the definition of “useful” is highly variable across applications and human users, we will see that several techniques exist that can be used to adapt and modify existing, pre-trained LLMs, such that their performance and ease-of-use is drastically improved in a variety of applications. Though many different methodologies exist, they share a common theme: making LLMs more practically viable and useful. The concept of creating specialized LLMs for particular applications has been heavily explored in recent literature. These methods can be used to eliminate known limitations of LLMs (e.g., generating incorrect/biased info), modify LLM behavior to better suit our needs, or even inject specialized knowledge into an LLM such that it becomes a domain expert. We can modify the behavior of LLMs by using techniques like domain-specific pre-training, model alignment, and supervised fine-tuning. Within this overview, we will explore methods of specializing and improving LLMs for a variety of use cases. But, using LLMs practically usually requires that the model be taught new behavior that is relevant to a particular application. But, how much can we actually accomplish with a generic model? These models are adept at solving common natural language benchmarks that we see within the deep learning literature. Large language models (LLMs) are incredibly-useful, task-agnostic foundation models.
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