Which of the following statements with regard to Large Language Models (LLMs) used in machine learning is/are correct?
1. LLMs assign probabilities to the next possible words and then pick the one with the highest probability.
2. LLMs process data through mathematical optimization to minimise prediction errors.
3. LLMs produce unbiased outputs.
Select the answer using the code given below:

Statement 1 is correct: Large Language Models (LLMs) generate text by predicting the next word (or token) in a sequence. They assign probabilities to all possible next words in their vocabulary based on the preceding context and typically select the one with the highest probability (or sample from the top probabilities) to construct coherent sentences.

Statement 2 is correct: During their training phase, LLMs process massive amounts of text data using mathematical optimization algorithms (such as gradient descent). The goal of this optimization is to adjust the model's internal parameters (weights) to minimize prediction errors (the loss function).

Statement 3 is incorrect: LLMs do not produce unbiased outputs. Because they are trained on vast datasets scraped from the internet and other human-generated text, they inherently absorb and reflect the biases, stereotypes, and prejudices present in that training data.