UPSC Prelims 2026 · Question 67 of 96

UPSC Prelims 2026 question on Large Language Models

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. 1 only
  2. 1 and 2 only
  3. 2 and 3 only
  4. 1, 2 and 3
Show answer

Answer: B. 1 and 2 only

Verdict

Statements 1 and 2 are correct, so the answer is Option (b). The incorrectness of the third statement can also be used to eliminate options (c) and (d).

Analysis

Large Language Models (LLMs) like GPT work by predicting the next token or word in a sequence. They assign probability distributions over possible next words and typically select based on highest probability, or use sampling strategies. LLMs are trained through mathematical optimization processes (gradient descent, backpropagation) that minimize a loss function, which measures prediction errors between the model output and actual training data.

Statement by statement

Statement 1 is correct: Large Language Models assign probabilities to the next possible words and then pick the one with the highest probability. This is the core mechanism of autoregressive language models, which generate text by predicting the most likely next token.

Statement 2 is correct: LLMs process data through mathematical optimization to minimise prediction errors. During training, LLMs use optimization algorithms like stochastic gradient descent to minimize the cross-entropy loss (prediction error).

Statement 3 is incorrect: LLMs are well-documented to produce biased outputs reflecting biases present in their training data. They are trained on large corpora of text data from the internet that contain inherent biases related to gender, race, culture, and other factors. This is a widely recognized limitation of current LLM technology.

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