Summary of On Limitations Of the Transformer Architecture, by Binghui Peng et al.
On Limitations of the Transformer Architecture
by Binghui Peng, Srini Narayanan, Christos Papadimitriou
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- 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 This research paper investigates the root causes of hallucinations in large language models (LLMs). The authors employ Communication Complexity to demonstrate that the Transformer layer is incapable of composing functions when the domains are sufficiently large. Empirical examples illustrate this inability even for relatively small domains. Additionally, the study highlights that several fundamental mathematical tasks, central to compositional tasks supposedly challenging for LLMs, may not be solvable by Transformers, assuming widely accepted conjectures in Computational Complexity hold true. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) sometimes make mistakes when understanding things. Scientists want to know why this happens. They found out that a special part of the model called the Transformer can’t solve certain problems if they’re too big or complicated. Even small problems are tricky for it! This means we might need to change how these models work. |
Keywords
* Artificial intelligence * Transformer
