Summary of Towards Generalist Prompting For Large Language Models by Mental Models, By Haoxiang Guan et al.
Towards Generalist Prompting for Large Language Models by Mental Models
by Haoxiang Guan, Jiyan He, Shuxin Zheng, En-Hong Chen, Weiming Zhang, Nenghai Yu
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models (LLMs) have achieved impressive performance on various tasks, but optimal performance often requires specially designed prompting methods. These methods typically rely on task-specific few-shot examples or are simple yet limited to a few types of tasks. This work introduces the concept of generalist prompting, which targets optimal or near-optimal performance on a wide range of tasks without manual prompt customization. The proposed MeMo (Mental Models) method distills various prompting methods into individual mental models and enables LLMs to autonomously select the most suitable model for the problem. This approach achieves state-of-the-art results on diverse tasks, including STEM, logical reasoning, and commonsense reasoning in zero-shot settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language models better at understanding many types of questions without needing special help. Right now, these models do well when given some examples of the type of question they’re trying to answer, but this can be tricky to set up and only works for certain types of questions. The researchers are working on a new way to ask these questions that lets language models figure out what kind of question it is and how to answer it without needing special help. They call this “generalist prompting.” They also came up with a new method called MeMo that helps language models do better on many types of questions, including science, math, and common sense problems. |
Keywords
» Artificial intelligence » Few shot » Prompt » Prompting » Zero shot
