Summary of Towards An Understanding Of Stepwise Inference in Transformers: a Synthetic Graph Navigation Model, by Mikail Khona et al.
Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model
by Mikail Khona, Maya Okawa, Jan Hula, Rahul Ramesh, Kento Nishi, Robert Dick, Ekdeep Singh Lubana, Hidenori Tanaka
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This research paper proposes a new approach to study autoregressive Transformer models on a synthetic task that embodies the multi-step nature of problems where stepwise inference is generally most useful. Specifically, the paper defines a graph navigation problem wherein a model is tasked with traversing a path from a start to a goal node on the graph. The authors empirically reproduce and analyze several phenomena observed at scale, including the stepwise inference reasoning gap, diversity-accuracy tradeoff in model generations as sampling temperature varies, simplicity bias in the model’s output, compositional generalization, and primacy bias with in-context exemplars. This work introduces a grounded, synthetic framework for studying stepwise inference and offers mechanistic hypotheses that can lay the foundation for a deeper understanding of this phenomenon. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research study helps us understand how language models solve complex problems by breaking them down into simpler steps. The scientists create a special task where a model has to navigate a graph from start to finish. They find that some things happen when using these step-by-step protocols, like the model being better at solving problems if it’s trained on certain types of data or if the model is given more freedom to make mistakes. |
Keywords
* Artificial intelligence * Autoregressive * Generalization * Inference * Temperature * Transformer
