13 citations · 19 across the 7 of their papers we have counts for
7 papers
Probing Perceptual Priors of MLLMs via Gibbs Sampling with Interpretable Generative Controls
Manuel Cherep, Pattie Maes, Nikhil Singh
A model's behavior on a task is jointly determined by the input it receives and the prior it brings in, i.e. the distribution over stimuli it implicitly expects. Interpretability r…
LLM Agents Are Hypersensitive to Nudges
Manuel Cherep, Pattie Maes, Nikhil Singh
LLMs are being set loose in complex, real-world environments involving sequential decision-making and tool use. Often, this involves making choices on behalf of human users. Howeve…
Discovering and Steering Interpretable Concepts in Large Generative Music Models
Nikhil Singh, Manuel Cherep, Pattie Maes
The fidelity with which neural networks can now generate content such as music presents a scientific opportunity: these systems appear to have learned implicit theories of such con…
Bridging the Data Provenance Gap Across Text, Speech and Video
Shayne Longpre, Nikhil Singh, Manuel Cherep +40
Progress in AI is driven largely by the scale and quality of training data. Despite this, there is a deficit of empirical analysis examining the attributes of well-established data…
Consent in Crisis: The Rapid Decline of the AI Data Commons
Shayne Longpre, Robert Mahari, Ariel Lee +46
General-purpose artificial intelligence (AI) systems are built on massive swathes of public web data, assembled into corpora such as C4, RefinedWeb, and Dolma. To our knowledge, we…
Contrastive Learning from Synthetic Audio Doppelgängers
Manuel Cherep, Nikhil Singh
Learning robust audio representations currently demands extensive datasets of real-world sound recordings. By applying artificial transformations to these recordings, models can le…
Creative Text-to-Audio Generation via Synthesizer Programming
Manuel Cherep, Nikhil Singh, Jessica Shand
Neural audio synthesis methods now allow specifying ideas in natural language. However, these methods produce results that cannot be easily tweaked, as they are based on large late…