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 :

QUESTION

Easy

Science & Technology

Prelims 2026

Which of the following statements with regard to Large Language Models (LLMs) used in machine learning is/are correct ?

LLMs assign probabilities to the next possible words and then pick the one with the highest probability.
LLMs process data through mathematical optimization to minimise prediction errors.
LLMs produce unbiased outputs.

Select the answer using the code given below :

Select an option to attempt

Explanation

Statement 1 is Correct: At their core, Large Language Models (LLMs) function by analyzing input context and calculating a statistical probability distribution for the next possible words (tokens) in a sequence. In their most basic form of text generation (known as "greedy decoding"), they pick the word with the absolute highest probability. While standard applications often introduce controlled randomness (stochastic sampling like top-k or temperature scaling) to make text more creative, the foundational mechanism relies on assigning probabilities to select the next word.

Statement 2 is Correct: LLM training relies heavily on mathematical optimization. Algorithms such as Stochastic Gradient Descent and backpropagation are used to iteratively adjust billions of model parameters. The explicit goal of this process is to minimize a "loss function," which mathematically represents the model's prediction errors compared to the actual training data.

Statement 3 is Incorrect: LLMs do not produce inherently unbiased outputs. Because they learn from massive, human-generated datasets scraped from the internet, they are well-documented to inherit, reflect, and sometimes amplify historical and societal biases (e.g., gender, racial, or cultural stereotypes) present in their training data.

Therefore, the correct option is B.

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