🎬AI Lectures9

Evaluation tells us how good a language model really is. There are two big ways to judge models: intrinsic (measure the model directly) and extrinsic (measure it through real tasks). Intrinsic is fast and clean but might not reflect real-world usefulness. Extrinsic is realistic and practical but slow and complicated to run.

Scaling laws relate a model’s log loss (how surprised it is by the next token) to three knobs: number of parameters (N), dataset size (D), and compute budget (C). As you increase N, D, and C, loss usually drops smoothly. But this only holds when you keep many other things steady and consistent.

This session explains how to use a trained language model to produce outputs, a phase called inference. It covers three task types—conditional generation, open-ended generation, and classification—each with different input/output shapes that affect decoding choices. The lecture then dives into decoding methods, which are strategies to choose the next token step by step. Finally, it discusses how to evaluate generated text using human judgments and automatic metrics, along with their trade-offs.

Scaling laws are empirical rules that show how a model’s loss (error) drops as you grow model size, data, or compute. They take a power-law form: Loss = A × N^(-α), where N can be parameters, data tokens, or compute, and α is the scaling exponent. This lets us predict how bigger models might perform without training them.

Modern language models are expensive to run because they perform many matrix multiplications. The main cost comes from both compute and moving data in and out of GPU memory. Optimizing the low-level code that runs these operations can make inference and training much faster and cheaper.

This class explains why data is the most important part of building language models. You learn where text data comes from (books, the web, and human feedback) and what each source is good and bad at. The instructor stresses that most of your time in real projects goes into finding, collecting, cleaning, and filtering data, not model code.

The lecture explains why rare words are a core challenge in language modeling. Most corpora follow Zipf’s law, where a few words appear very often and a huge number appear very rarely. Rare words make probability estimates unreliable and inflate vocabulary size, which increases memory and slows training and inference.