238 citations · 253 across the 10 of their papers we have counts for
10 papers
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…
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…
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…
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…
From job titles to jawlines: Using context voids to study generative AI systems
Shahan Ali Memon, Soham De, Sungha Kang +5
In this paper, we introduce a speculative design methodology for studying the behavior of generative AI systems, framing design as a mode of inquiry. We propose bridging seemingly…
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…
Gemma: Open Models Based on Gemini Research and Technology
Gemma Team, Thomas Mesnard, Cassidy Hardin +105
This work introduces Gemma, a family of lightweight, state-of-the art open models built from the research and technology used to create Gemini models. Gemma models demonstrate stro…
RecurrentGemma: Moving Past Transformers for Efficient Open Language Models
Aleksandar Botev, Soham De, Samuel L Smith +59
We introduce RecurrentGemma, a family of open language models which uses Google's novel Griffin architecture. Griffin combines linear recurrences with local attention to achieve ex…
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
Soham De, Samuel L. Smith, Anushan Fernando +14
Recurrent neural networks (RNNs) have fast inference and scale efficiently on long sequences, but they are difficult to train and hard to scale. We propose Hawk, an RNN with gated…