Summary of The Pitfalls Of Next-token Prediction, by Gregor Bachmann et al.
The pitfalls of next-token prediction
by Gregor Bachmann, Vaishnavh Nagarajan
First submitted to arxiv on: 11 Mar 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 A novel study proposes that a next-token predictor can accurately mimic human intelligence by introducing a multi-token objective. This approach is distinct from prevailing methods, which are often based on sequence-to-sequence learning or language models. The researchers highlight the importance of recognizing and addressing misconceptions surrounding this issue. By focusing on a simple, yet effective, prediction task, the study aims to provide a more realistic assessment of machine intelligence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Can artificial intelligence truly be like human intelligence? Researchers are trying to figure out if a simple computer program can predict what comes next in a conversation or text. They think that by making the program look at multiple words at once, it might get better at understanding us. The study wants people to know that this isn’t just about language models or writing stories, but about how we understand intelligence. |
Keywords
* Artificial intelligence * Token
