Building LLM applications for production
[Hacker News discussion, LinkedIn discussion, Twitter thread]| Chip Huyen
A Comprehensive Overview of Prompt Engineering| www.promptingguide.ai
nonethewiser on April 11, 2023 | next [–]| news.ycombinator.com
Chain-of-thought prompting combined with pre-trained large language models has achieved encouraging results on complex reasoning tasks. In this paper, we propose a new decoding strategy, self-consistency, to replace the naive greedy decoding used in chain-of-thought prompting. It first samples a diverse set of reasoning paths instead of only taking the greedy one, and then selects the most consistent answer by marginalizing out the sampled reasoning paths. Self-consistency leverages the intui...| arXiv.org
GPT-3 can perform numerous tasks when provided a natural language prompt that contains a few training examples. We show that this type of few-shot learning can be unstable: the choice of prompt format, training examples, and even the order of the training examples can cause accuracy to vary from near chance to near state-of-the-art. We demonstrate that this instability arises from the bias of language models towards predicting certain answers, e.g., those that are placed near the end of the p...| arXiv.org
A Comprehensive Overview of Prompt Engineering| www.promptingguide.ai
[Hacker News discussion, LinkedIn discussion, Twitter thread]| Chip Huyen
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.| lilianweng.github.io
