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:

1. A

   1 only

   1 only: Incomplete statement. While statement 1 correctly describes token prediction based on probability distribution, statement 2 is also correct because LLMs rely heavily on loss functions and gradient descent (mathematical optimization) during their training phase to minimize prediction errors.

2. B

   1 and 2 only

   (b) 1 and 2 only. Statement 1 is correct because LLMs function by predicting the probability of subsequent tokens based on context and selecting from the distribution. Statement 2 is correct because model weights are adjusted iteratively via mathematical optimization (loss minimization) to reduce prediction errors. Statement 3 is incorrect because LLMs inherit biases present in their training corpora and do not produce inherently unbiased outputs.

3. C

   2 and 3 only

   2 and 3 only: Incorrect combination. While statement 2 is correct, statement 3 is factually wrong because large language models are susceptible to societal, cultural, and statistical biases from their training data, making them prone to biased outputs.

4. D

   1, 2 and 3

   1, 2 and 3: Incorrect because statement 3 is wrong. LLMs frequently generate biased or hallucinatory content due to data imbalances and probabilistic token generation, meaning they do not produce unbiased outputs.

Summary. Official key is (b). Statements 1 and 2 are correct as they accurately describe next-token probability assignment and loss minimization via mathematical optimization during training. Statement 3 is incorrect because LLMs notoriously reflect and amplify biases present in their training data.