In-Context Principle Learning from Mistakes
Authors: name: Tianjun Zhang, name: Aman Madaan, name: Luyu Gao, name: Steven Zheng, name: Swaroop Mishra, name: Yiming Yang, name: Niket Tandon, name: Uri Alon

In-context learning (ICL, also known as few-shot prompting) has been the
standard method of adapting LLMs to downstream tasks, by learning from a few
input-output examples. Nonetheless, all ICL-based approaches only learn from
correct input-output pairs. In this paper, we revisit this paradigm, by
learning more from the few given input-output examples. We introduce Learning
Principles (LEAP): First, we intentionally induce the model to make mistakes on
these few examples; then we reflect on these mistakes, and learn explicit
task-specific "principles" from them, which help solve similar problems and
avoid common mistakes; finally, we prompt the model to answer unseen test
questions using the original few-shot examples and these learned general
principles. We evaluate LEAP on a wide range of benchmarks, including multi-hop
question answering (Hotpot QA), textual QA (DROP), Big-Bench Hard reasoning,
and math problems (GSM8K and MATH); in all these benchmarks, LEAP improves the
strongest available LLMs such as GPT-3.5-turbo, GPT-4, GPT-4 turbo and
Claude-2.1. For example, LEAP improves over the standard few-shot prompting
using GPT-4 by 7.5% in DROP, and by 3.3% in HotpotQA. Importantly, LEAP does
not require any more input or examples than the standard few-shot prompting
settings.