30 citations · 71 across the 27 of their papers we have counts for
6 papers · 1 filter
Recirculation
Michael C. Mozer, Shoaib Ahmed Siddiqui, Danny Sawyer +2
We describe an inference-time architectural enhancement for off-the-shelf foundation models that markedly reduces perplexity and boosts accuracy across generation and reasoning tas…
Do Real-World Datasets Contain Natural Experiments? An Empirical Study Using Causal Feature Selection
Gautam Gare, John Galeotti, Michael Mozer +2
In nature, events that affect some individuals or groups but not others constitute an implicit intervention and are known as natural experiments. For example, the COVID-19 pandemic…
Context Sensitivity Improves Human-Machine Visual Alignment
Frieda Born, Tom Neuhäuser, Lukas Muttenthaler +6
Modern machine learning models typically represent inputs as fixed points in a high-dimensional embedding space. While this approach has been proven powerful for a wide range of do…
Is your algorithm unlearning or untraining?
Eleni Triantafillou, Ahmed Imtiaz Humayun, Monica Ribero +3
As models are getting larger and are trained on increasing amounts of data, there has been an explosion of interest into how we can ``delete'' specific data points or behaviours fr…
Analysis of Optimality of Large Language Models on Planning Problems
Bernd Bohnet, Michael C. Mozer, Kevin Swersky +4
Classic AI planning problems have been revisited in the Large Language Model (LLM) era, with a focus of recent benchmarks on success rates rather than plan efficiency. We examine t…
The Topological Trouble With Transformers
Michael C. Mozer, Shoaib Ahmed Siddiqui, Rosanne Liu
Transformers encode structure in sequences via an expanding contextual history. However, their purely feedforward architecture fundamentally limits dynamic state tracking. State tr…