Summary of Llms Are Not Just Next Token Predictors, by Stephen M. Downes et al.
LLMs are Not Just Next Token Predictors
by Stephen M. Downes, Patrick Forber, Alex Grzankowski
First submitted to arxiv on: 6 Aug 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 LLMs are statistical models designed to learn language through stochastic gradient descent, focusing on next token prediction. However, critics argue that LLMs are simply next token predictors. This paper challenges this notion, highlighting important aspects of LLM behavior and capabilities that are overlooked when reduced to a single task. By examining the analogy between LLMs and biological research explaining evolution and development from the gene’s perspective, we gain a deeper understanding of LLM capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs are special computer models designed to understand language. Some people think they’re just good at predicting what comes next in a sentence. But this paper says that’s too simple. It shows how these models can do many other things, and why it’s important to look beyond their ability to predict the next word. |
Keywords
» Artificial intelligence » Stochastic gradient descent » Token
