A language model does one thing: given the preceding text, output a distribution over the next word. At large enough scale, the way to control it becomes changing the input: a few examples (in-context learning), or asking it to write its reasoning first (chain-of-thought; the example cited in class is one sentence, Let's think step by step, taking an arithmetic task from 18% to 80%). But following instructions and saying 'I don't know' do not come from next-word prediction alone; instruction tuning and RLHF add them.
A prompt with one demonstrated answer is not the same data as a prompt with a preferred and rejected response. SFT learns demonstrated tokens. A typical RLHF pipeline learns reward and then updates a constrained policy. DPO forms a loss directly from preference pairs and reference-relative ratios.
A pre-trained model continues text well and still may not do what an instruction asks. The first half of this lecture covers the two steps of post-training: instruction tuning uses many different tasks to teach the model to read task descriptions, and RLHF then moves the model toward whole responses that people prefer, with a reward model and pairwise comparisons handling the cost of human labor and inconsistent scores, while DPO and KTO drop the reinforcement-learning part. InstructGPT and ChatGPT are these two steps chained together. The second half covers LLM adaptation: models are too large to fine-tune in full, so only a small number of parameters are trained, with Adapter, LoRA, and prompt tuning, and no single method is best on every task.
2022 marked NLP’s shift from demonstrating model capabilities toward aligning and controlling them. InstructGPT brought RLHF into the mainstream, Chain-of-Thought showed that prompts could unlock reasoning, and Flan 2022 matured instruction-tuning methodology. ACL and NAACL adopted ARR as their sole review path, exposing infrastructure and reviewer-load problems. ChatGPT launched at year-end and rewrote the rules of NLP research.