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:

Large Language Models like GPT, LLaMA, etc., are autoregressive models that predict the next token by computing a probability distribution over the vocabulary and selecting tokens based on that distribution (often the highest probability one, or by sampling), so statement 1 is correct. They are trained via gradient-based mathematical optimization (typically stochastic gradient descent on a cross-entropy loss) to minimise prediction errors, so statement 2 is correct. However, LLMs are known to inherit and even amplify biases present in their training data — biases related to gender, race, religion, ideology, etc. — so they do NOT produce unbiased outputs. Statement 3 is incorrect. Hence statements 1 and 2 only are correct.