Rethinking with Retrieval: Faithful Large Language Model Inference

We propose a novel post-processing approach, rethinking with retrieval (RR), which retrieves relevant external knowledge based on the decomposed reasoning steps obtained from the chain-of-thought (CoT) prompting. This lightweight approach does not require additional training or fine-tuning and is not limited by the input length of LLMs.

This new paper shows the potential of enhancing LLMs by retrieving relevant external knowledge based on decomposed reasoning steps obtained through chain-of-thought prompting.

The proposed method (rethinking with retrieval) seems to consistently outperform CoT (in terms of accuracy and faithfulness of explanations) as model size increases. How would even bigger models perform here?

https://arxiv.org/abs/2301.00303

Prompting GPT-3 to reliably generate text and JSON data in a precise format using Python assertions, f‑strings, and variables declared only in our imaginations.

^ Check out this weird assertions trick. Amazing this works.

GPTZero is a proposed anti-plagiarism tool that claims to be able to detect ChatGPT-generated text. Here's how it did on the first prompt I tried.