8 papers
Model Directions, Not Words: Mechanistic Topic Models Using Sparse Autoencoders
Carolina Zheng, Nicolas Beltran-Velez, Sweta Karlekar +5
Traditional topic models are effective at uncovering latent themes in large text collections. However, due to their reliance on bag-of-words representations, they struggle to captu…
Duel-Evolve: Reward-Free Test-Time Scaling via LLM Self-Preferences
Sweta Karlekar, Carolina Zheng, Magnus Saebo +5
Many applications seek to optimize LLM outputs at test time by iteratively proposing, scoring, and refining candidates over a discrete output space. Existing methods use a calibrat…
Estimating the Hallucination Rate of Generative AI
Andrew Jesson, Nicolas Beltran-Velez, Quentin Chu +5
This paper presents a method for estimating the hallucination rate for in-context learning (ICL) with generative AI. In ICL, a conditional generative model (CGM) is prompted with a…
Extremely Greedy Equivalence Search
Achille Nazaret, David Blei
The goal of causal discovery is to learn a directed acyclic graph from data. One of the most well-known methods for this problem is Greedy Equivalence Search (GES). GES searches fo…
Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective
Andrew Jesson, Nicolas Beltran-Velez, David Blei
This work is about estimating when a conditional generative model (CGM) can solve an in-context learning (ICL) problem. An in-context learning (ICL) problem comprises a CGM, a data…
Multi-environment Topic Models
Dominic Sobhani, Amir Feder, David Blei
Probabilistic topic models are a powerful tool for extracting latent themes from large text datasets. In many text datasets, we also observe per-document covariates (e.g., source,…