7 papers
Token Rankings are Unforgeable Language Model Signatures
Matthew Finlayson, Andreas Grivas, Xiang Ren +1
Language model parameters are known to impose unique (to each model) geometric constraints on their logit outputs, which serves as a signature that identifies the model, but also l…
Believing without Seeing: Quality Scores for Contextualizing Vision-Language Model Explanations
Keyu He, Tejas Srinivasan, Brihi Joshi +3
When people query Vision-Language Models (VLMs) but cannot see the accompanying visual context (e.g. for blind and low-vision users), augmenting VLM predictions with natural langua…
Every Language Model Has a Forgery-Resistant Signature
Matthew Finlayson, Xiang Ren, Swabha Swayamdipta
The ubiquity of closed-weight language models with public-facing APIs has generated interest in forensic methods, both for extracting hidden model details (e.g., parameters) and fo…
Better Language Model Inversion by Compactly Representing Next-Token Distributions
Murtaza Nazir, Matthew Finlayson, John X. Morris +2
Language model inversion seeks to recover hidden prompts using only language model outputs. This capability has implications for security and accountability in language model deplo…
ELI-Why: Evaluating the Pedagogical Utility of Language Model Explanations
Brihi Joshi, Keyu He, Sahana Ramnath +5
Language models today are widely used in education, yet their ability to tailor responses for learners with varied informational needs and knowledge backgrounds remains under-explo…
Improving Language Model Personas via Rationalization with Psychological Scaffolds
Brihi Joshi, Xiang Ren, Swabha Swayamdipta +2
Language models prompted with a user description or persona are being used to predict the user's preferences and opinions. However, existing approaches to building personas mostly…