Summary of Exploring the Zero-shot Capabilities Of Llms Handling Multiple Problems at Once, by Zhengxiang Wang et al.
Exploring the Zero-Shot Capabilities of LLMs Handling Multiple Problems at once
by Zhengxiang Wang, Jordan Kodner, Owen Rambow
First submitted to arxiv on: 16 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 This paper explores the use of Multiple Problem Prompting (MPP) in Large Language Models (LLMs), where multiple problems are presented to improve inference efficiency. In contrast to Single Problem Prompting (SPP), which prompts LLMs with a single problem at a time, MPP has shown promise in few-shot settings but its zero-shot performance is underexplored. The authors study the zero-shot MPP performance of various LLMs on 18 benchmarks and find that they are competent multi-problem solvers. However, they also observe that LLMs perform worse when selecting indices of texts by class label and with mixed-source reasoning problems, indicating a lack of true understanding. Instruction tuning is found to be an important factor in enhancing MPP. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well Large Language Models (LLMs) can solve multiple problems at once without any training. Right now, most LLMs are taught one problem at a time, but some researchers think it would be more efficient to teach them many problems at once. The authors of this paper tested several different LLMs on 18 different tasks and found that they were all pretty good at solving multiple problems. However, the models didn’t always understand what they were doing, especially when selecting certain information or mixing different types of reasoning. To do better, the models need to be taught how to follow instructions. |
Keywords
» Artificial intelligence » Few shot » Inference » Instruction tuning » Prompting » Zero shot
