Summary of Thinking Tokens For Language Modeling, by David Herel et al.
Thinking Tokens for Language Modeling
by David Herel, Tomas Mikolov
First submitted to arxiv on: 14 May 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 A new approach to improving the capabilities of large language models is proposed. Currently, these models struggle with complex calculations, such as multiplying 56 and 37. This limitation is attributed to their reliance on memorization rather than reasoning. However, humans also require time to solve complex problems, suggesting that a more nuanced understanding of human problem-solving behavior can inform the development of language models. The proposed solution involves introducing “thinking tokens” that enable language models to perform more calculations when faced with complex problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has found a way to make language models better at doing math. Right now, these computer programs are not very good at solving tricky problems, like multiplying 56 and 37. This is because they rely on remembering lots of information rather than actually understanding how to solve the problem. But humans don’t do well with complex math problems either – it takes us a while to figure them out! The researchers think that by studying how humans solve problems, they can make language models more like us. They’re proposing a new way for these models to “think” about math problems and come up with the correct answer. |
