Summary of Uncovering Uncertainty in Transformer Inference, by Greyson Brothers et al.
Uncovering Uncertainty in Transformer Inference
by Greyson Brothers, Willa Mannering, Amber Tien, John Winder
First submitted to arxiv on: 8 Dec 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 Transforming transformer-based language models’ latent representations through iterative inference is investigated in this study. The Iterative Inference Hypothesis (IIH) is explored, focusing on how the model’s residual stream tokens are refined and whether observable differences emerge between correct and incorrect generations. Empirical support is found for the IIH, showing that token embeddings follow a trajectory of decreasing loss. Additionally, uncertainty in the generation process is reflected by the rate at which residual embeddings converge to a stable output representation. A method using cross-entropy is introduced to detect this uncertainty, demonstrating its potential to distinguish between correct and incorrect token generations on an idiom dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language models are analyzed to see how they get better at understanding language as they generate text. The study finds that the model’s representations of words change in a way that shows it’s getting more confident in what it’s saying. This confidence is measured by looking at how similar the model’s predictions are to the actual correct answers. The researchers also develop a new way to measure this confidence, which can be used to spot when the model is generating text that isn’t actually correct. |
Keywords
» Artificial intelligence » Cross entropy » Inference » Token » Transformer
