Summary of Reasoning with Large Language Models, a Survey, by Aske Plaat et al.
Reasoning with Large Language Models, a Survey
by Aske Plaat, Annie Wong, Suzan Verberne, Joost Broekens, Niki van Stein, Thomas Back
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 The paper explores the potential of large language models (LLMs) in reasoning and problem-solving tasks, building on their ability to scale up and generalize knowledge. By leveraging in-context learning and few-shot learning, LLMs can achieve breakthrough performance on various language tasks, such as translation, summarization, and question-answering. The paper reviews the rapidly expanding field of prompt-based reasoning with LLMs, identifying different approaches for generating, evaluating, and controlling multi-step reasoning. It highlights the relation between reasoning and prompt-based learning, and discusses the potential connections to sequential decision processes and reinforcement learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are incredibly smart computers that can learn from a massive amount of text data. They’re so good at understanding language that they can even solve math problems! This paper looks at how LLMs can be used for problem-solving, like doing math word problems or answering tricky questions. It also explores how we can use special prompts to help the models think and reason more critically. |
Keywords
» Artificial intelligence » Few shot » Prompt » Question answering » Reinforcement learning » Summarization » Translation
