In-Context Principle Learning from Mistakes
<|reference_start|>In-Context Principle Learning from Mistakes: 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.<|reference_end|>
@article{zhang2024in-context,
    title={In-Context Principle Learning from Mistakes},
    author={Tianjun Zhang, Aman Madaan, Luyu Gao, Steven Zheng, Swaroop Mishra,
  Yiming Yang, Niket Tandon, Uri Alon},
    journal={arXiv preprint arXiv:2402.05403},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.05403},
    primaryClass={cs.CL cs.AI}
}