How the model reads a history, learns from targets, and chooses its next token are different questions. Attention mixes information; the training objective updates parameters; decoding selects output under a fixed model. Keeping them distinct prevents confusing a sampling change with a model update.
L08 goes from BPE to VOLT, a method co-authored by the lecturer Lei Li: vocabulary size has both a cost and a value, and VOLT finds the sweet spot by asking how much normalized entropy each added token removes, then solves it as an optimal transport problem. The second half covers LLaMA 3 growing its vocabulary from 32k to 128k and the cost of byte-level BPE splitting one Chinese character into three tokens. L09 moves from greedy decoding, sampling, and beam search to speculative decoding: a small model guesses N tokens and the big model checks them in one forward pass, because checking is cheaper than generating. It ends with EAGLE, which predicts final-layer features instead of tokens.
A guide to the Sub-word Tokenization unit of Prof. Hung-Yu Kao's NLP course at NTHU (Fall 2025). Splitting on white space only works for Western languages and cannot handle unseen words or German-style compounds. BPE starts from characters and repeatedly merges the most frequent adjacent pair into the vocabulary, so each merge adds one entry; its weakness is that the greedy split is not always the best one. The Unigram LM picks splits by probability and can even sample different ones. The slides give vocabulary sizes of 30522 for BERT, 50257 for GPT-2/GPT-3 and 32,128 for T5.
This lecture has two halves. The first covers tokenization: with whole words as units, unseen words can only become UNK; with characters as units, meaning cannot be put back together. The compromise is subwords, and a common way to build them is BPE: start from characters and repeatedly merge the most frequent adjacent pair. The second half covers BERT: a static word vector gives "apple" one embedding and cannot tell the fruit from the company. ELMo uses a language model to produce contextual representations, and BERT replaces the LSTM with a Transformer, pre-trains with a masked LM that hides 15% of tokens plus next sentence prediction, and then fine-tunes the whole model on downstream tasks.
HW10 is 12 multiple-choice questions answered only on NTU COOL. Section 1 compares three spoken language model architectures: Cascade (ASR → LLM → TTS, with text in the middle), End-to-End (a language model over discrete speech tokens), and Thinker-Talker (an LLM thinks, a separate decoder speaks). In the Colab, two models listen to three clips and guess the speaker's gender, and you work out which one is the cascade. Section 2 takes Mimi apart: tokenize an emotion corpus into 32 RVQ layers, plot UMAP for layers 0, 6, 16, and 31, then encode and decode speech, laughter, and music to hear what breaks. The rest are paper questions on TWIST, AudioLM, LLaMA-Omni 2, Moshi, and GLM-4-Voice, covering initialization, pretraining, interleaving, and realtime/full-duplex behavior. The Colab needs Llama-3.2-3B-Instruct access and an HF token. Questions and Colab are public; outside readers miss only the COOL grading and answers.
CME295 Lecture 1 threads a single sentence, "A cute teddy bear is reading.", through the whole class: split it into tokens, turn them into vectors, see why an RNN can't hold on to long sentences, then translate it into French with self-attention and an encoder-decoder. The 2026 edition drops the entire section on NLP tasks and evaluation metrics and opens instead with a timeline running from 2017 to the agent era.
Models don't read words — they read tokens. A Chinese character is typically 1-2 tokens; an English word is 1-3. The context window is the token limit per request. Inference is using a model; training is teaching one. What you do every day is inference.
You don't need to become a researcher to understand AI models systematically. This series starts from what you can see (tokens, context windows) and works up to self-hosting open-source models — 18 articles covering everything you need to choose models, read benchmarks, and estimate costs.
Models charge by tokens, not characters. The BPE algorithm starts from individual bytes and repeatedly merges the most frequent adjacent pair to build a vocabulary. English 'understanding' might be 1-2 tokens, but Chinese '理解' could take 2-3 — same meaning, higher cost.
Week 2 teaches three things: cutting text into tokens with BPE, computing the edit distance between two strings with dynamic programming, and estimating the next word's probability with n-grams, evaluated by perplexity. Each can be done on paper, and this post works one example of each.
Lecture 7 decomposes pretraining into scalable data, subword tokenization, three model objectives, and in-context learning. A general self-supervised objective yields reusable representations; downstream signals specify their use.
Lecture 14 moves from word, character/byte, and subword segmentation to BPE failures and cross-lingual fairness. A tokenizer determines sequence length, compute cost, and the units a model sees; it is not neutral preprocessing.
CS336's first lecture does not treat building a language model from scratch as reenacting every old technique. It separates mechanics, mindset, and intuitions, then uses BPE to show how raw bytes become trainable tokens.