7 papers
The Illusion of Stochasticity in LLMs
Xiangming Gu, Soham De, Michalis Titsias +3
In this work, we demonstrate that reliable stochastic sampling is a fundamental yet unfulfilled requirement for Large Language Models (LLMs) operating as agents. Agentic systems ar…
Understanding Performance Gap Between Parallel and Sequential Sampling in Large Reasoning Models
Xiangming Gu, Soham De, Larisa Markeeva +2
Large Reasoning Models (LRMs) have shown remarkable performance on challenging questions, such as math and coding. However, to obtain a high quality solution, one may need to sampl…
Question the Questions: Auditing Representation in Online Deliberative Processes
Soham De, Lodewijk Gelauff, Ashish Goel +3
A central feature of many deliberative processes, such as citizens' assemblies and deliberative polls, is the opportunity for participants to engage directly with experts. While pa…
How do language models learn facts? Dynamics, curricula and hallucinations
Nicolas Zucchet, Jörg Bornschein, Stephanie Chan +3
Large language models accumulate vast knowledge during pre-training, yet the dynamics governing this acquisition remain poorly understood. This work investigates the learning dynam…
Optimizers Qualitatively Alter Solutions And We Should Leverage This
Razvan Pascanu, Clare Lyle, Ionut-Vlad Modoranu +6
Due to the nonlinear nature of Deep Neural Networks (DNNs), one can not guarantee convergence to a unique global minimum of the loss when using optimizers relying only on local inf…
Scaling Human Judgment in Community Notes with LLMs
Haiwen Li, Soham De, Manon Revel +6
This paper argues for a new paradigm for Community Notes in the LLM era: an open ecosystem where both humans and LLMs can write notes, and the decision of which notes are helpful e…