Hallucination
The phenomenon where the model generates factually incorrect information or unrelated content as if it were true
Role of temperature in the decoding process of a Large Language Model (LLM)
To adjust the sharpness of probability distribution over vocabulary when selecting the next word
When decoding with an LLM, the model assigns probabilities to each word in the vocabulary for the next word in the sequence. Temperature controls the sharpness or smoothness of this probability distribution. A low temperature value results in a sharper distribution, which means that the model is more confident in its predictions and tends to select the most likely word with higher probability. Conversely, a higher temperature value smooths out the distribution, making it more likely for lower probability words to be chosen, leading to more diverse and varied output.
Prompt engineering in the context of Large Language Models (LLMs)
Iteratively refining the ask to elicit a desired response
Prompt engineering in the context of Large Language Models (LLMs) refers to the practice of designing and refining prompts or input instructions to elicit desired responses from the model. It involves crafting specific textual cues or queries that guide the model towards generating outputs that align with the user's intentions or requirements.
What does in-context learning in Large Language Models involve
Conditioning the model with task-specific instructions or demonstration