Summary of Can Large Language Models Put 2 and 2 Together? Probing For Entailed Arithmetical Relationships, by D. Panas et al.
Can Large Language Models put 2 and 2 together? Probing for Entailed Arithmetical Relationships
by D. Panas, S. Seth, V. Belle
First submitted to arxiv on: 30 Apr 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 The paper explores the intersection of two areas in Large Language Models (LLMs): what they know and how they reason. It investigates whether LLMs can reason about their implicitly-held knowledge using a simple setup comparing cardinalities across various subjects, such as bird legs versus tricycle wheels. The results show that although LLMs improve with each new GPT release, their capabilities are limited to statistical inference only. This is problematic because pure statistical learning cannot cope with the combinatorial explosion in many commonsense reasoning tasks, especially when arithmetical notions are involved. The paper argues that bigger models are not always better and that chasing purely statistical improvements is flawed, as it conflates correct answers with genuine reasoning ability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models can do amazing things, like understand what we write. But have you ever wondered how they actually work? This paper looks at two important areas: what LLMs know and how they figure things out. The researchers used a simple test to see if the models could reason about their own knowledge. They found that while the models get better with each new update, they’re still limited in what they can do. This means that even though they might be able to answer questions correctly, they don’t really understand why or how they got those answers. |
Keywords
» Artificial intelligence » Gpt » Inference
