alignment

Prompt Engineering, also known as In-Context Prompting, refers to methods for how to communicate with LLM to steer its behavior for desired outcomes without updating the model weights. It is an empirical science and the effect of prompt engineering methods can vary a lot among models, thus requiring heavy experimentation and heuristics. This post only focuses on prompt engineering for autoregressive language models, so nothing with Cloze tests, image generation or multimodality models....

Large pretrained language models are trained over a sizable collection of online data. They unavoidably acquire certain toxic behavior and biases from the Internet. Pretrained language models are very powerful and have shown great success in many NLP tasks. However, to safely deploy them for practical real-world applications demands a strong safety control over the model generation process. Many challenges are associated with the effort to diminish various types of unsafe content:...

[Updated on 2021-02-01: Updated to version 2.0 with several work added and many typos fixed.] [Updated on 2021-05-26: Add P-tuning and Prompt Tuning in the “prompt design” section.] [Updated on 2021-09-19: Add “unlikelihood training”.] There is a gigantic amount of free text on the Web, several magnitude more than labelled benchmark datasets. The state-of-the-art language models (LM) are trained with unsupervised Web data in large scale. When generating samples from LM by iteratively sampling the next token, we do not have much control over attributes of the output text, such as the topic, the style, the sentiment, etc....