Attempt this previous-year question. The correct option stays hidden until you choose.
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 :
Pick an option to check yourself.
Approach: Analyze the core technical mechanisms of Generative AI (probabilistic modeling and optimization) versus the inherent limitations of training data.
- **Correct Answer:** (b) 1 and 2 only
- **Statement 1 is correct:** LLMs function as probabilistic models. They predict the next token (word/sub-word) by calculating a probability distribution over the entire vocabulary. While they often pick the highest probability token (greedy search), they can also use techniques like 'temperature' or 'top-k' sampling to introduce variety.
- **Statement 2 is correct:** The training process of an LLM is a massive optimization problem. Through backpropagation and gradient descent, the model iteratively adjusts its internal parameters (weights) to minimize the difference between its predicted output and the actual target text (loss function).
- **Statement 3 is incorrect:** LLMs are notorious for producing biased outputs. Because they are trained on vast datasets scraped from the internet, they inevitably mirror the societal, cultural, and linguistic biases present in that data. They are not inherently neutral or objective.
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