Prompting & Current LMs

Stay tuned for slides from the fourth lecture!

Additional materials

If you want to dig a bit deeper, here are (optional!) supplementary readings introducing the prompting strategies and the mentioned LMs that were covered in class:

• Wei et al. (2023) Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

• Kojima et al. (2023) Large Language Models are Zero-Shot Reasoners

• Webson & Pavlick (2022) Do Prompt-Based Models Really Understand the Meaning of Their Prompts?

• Nye et al. (2022) Show Your Work: Scratchpads for Intermediate Computation with Language Models

• Reynolds & McDonnell (2021) Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm

• Wang et al. (2023) Self-Consistency Improves Chain of Thought Reasoning in Language Models

• Liu et al. (2022) Generated Knowledge Prompting for Commonsense Reasoning

• Yao et al. (2023) Tree of Thoughts: Deliberate Problem Solving with Large Language Models

• Xie et al. (2022) An Explanation of In-context Learning as Implicit Bayesian Inference

• Min et al. (2022) Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

• Lampinen et al. (2022) Can language models learn from explanations in context?

• Touvron et al. (2023) LLaMA: Open and Efficient Foundation Language Models

• Touvron et al. (2023) Llama 2: Open Foundation and Fine-Tuned Chat Models