11 papers · 1 filter
Forking Fast: Efficiently Estimating Uncertainty Dynamics in Text Generation
Eric Bigelow, Amir Zur, Satchel Grant +7
LLM reasoning is stochastic, and so understanding a model requires grappling with the distribution of reasoning chains that it might produce for a given question, i.e., its uncerta…
Reasoning Theater: Disentangling Model Beliefs from Chain-of-Thought
Siddharth Boppana, Annabel Ma, Max Loeffler +5
We provide evidence of performative chain-of-thought (CoT) in reasoning models, where a model becomes strongly confident in its final answer, but continues generating tokens withou…
Stories in Space: In-Context Learning Trajectories in Conceptual Belief Space
Eric Bigelow, Raphaël Sarfati, Daniel Wurgaft +5
Large Language Models (LLMs) update their behavior in context, which can be viewed as a form of Bayesian inference. However, the structure of the latent hypothesis space over which…
The Shape of Beliefs: Geometry, Dynamics, and Interventions along Representation Manifolds of Language Models' Posteriors
Raphaël Sarfati, Eric Bigelow, Daniel Wurgaft +6
Large language models (LLMs) form implicit beliefs (posteriors over latent variables) from prompts, but we lack a mechanistic account of how these beliefs are encoded in representa…
Transferring Linear Features Across Language Models With Model Stitching
Alan Chen, Jack Merullo, Alessandro Stolfo +1
In this work, we demonstrate that affine mappings between residual streams of language models is a cheap way to effectively transfer represented features between models. We apply t…
From Memorization to Reasoning in the Spectrum of Loss Curvature
Jack Merullo, Srihita Vatsavaya, Lucius Bushnaq +1
We characterize how memorization is represented in transformer models and show that it can be disentangled in the weights of both language models (LMs) and vision transformers (ViT…