Summary of In-context Principle Learning From Mistakes, by Tianjun Zhang et al.
In-Context Principle Learning from Mistakes
by Tianjun Zhang, Aman Madaan, Luyu Gao, Steven Zheng, Swaroop Mishra, Yiming Yang, Niket Tandon, Uri Alon
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a new approach to adapting large language models (LLMs) for downstream tasks called Learning Principles (LEAP). Traditionally, LLMs are fine-tuned using in-context learning (ICL), which involves learning from correct input-output pairs. LEAP instead intentionally induces the model to make mistakes on a few examples and then learns task-specific “principles” from these errors. These principles help the model solve similar problems and avoid common mistakes. The authors evaluate LEAP on various benchmarks, including multi-hop question answering, textual QA, Big-Bench Hard reasoning, math problems, and more. They show that LEAP improves the strongest available LLMs such as GPT-3.5-turbo, GPT-4, GPT-4 turbo, and Claude-2.1 on these benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a new way to help computers understand what we want them to do. Right now, computers are good at doing things we tell them exactly how to do. But sometimes we need them to figure out the answer on their own. The new method is called LEAP and it helps computers learn from mistakes. This means they can solve problems better and avoid making the same mistakes again. The researchers tested this method with different kinds of questions and showed that it works well. |
Keywords
» Artificial intelligence » Claude » Gpt » Question answering
