Summary of Connecting the Dots: Llms Can Infer and Verbalize Latent Structure From Disparate Training Data, by Johannes Treutlein et al.
Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data
by Johannes Treutlein, Dami Choi, Jan Betley, Samuel Marks, Cem Anil, Roger Grosse, Owain Evans
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 study investigates how large language models (LLMs) can infer censored information from their training data through out-of-context reasoning (OOCR). The researchers demonstrate that frontier LLMs can perform inductive OOCR on various tasks, such as inferring the location of an unknown city or determining whether a coin is biased. They also show that smaller LLMs may struggle to learn complex structures, making OOCR unreliable for these models. The ability of LLMs to “connect the dots” without explicit learning poses a potential challenge in monitoring and controlling the knowledge acquired by LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are getting smarter every day! This study looks at how they can figure out things without being taught directly. Imagine if you could tell that someone is from Paris just because you know some other cities near Paris, even if you’ve never been to Paris before! That’s basically what the researchers did with these super-smart computers. They showed that LLMs can do this kind of “connecting the dots” for lots of different things, like understanding whether a coin is fair or not. But they also found out that some smaller models might get stuck and not be able to figure it out. This is important because it means we need to pay attention to how these computers are learning things. |
Keywords
» Artificial intelligence » Attention
