1 citations · 2 across the 6 of their papers we have counts for
5 papers · 1 filter
Self-Questioning Language Models
Lili Chen, Mihir Prabhudesai, Katerina Fragkiadaki +2
Can large language models improve without external data -- by generating their own questions and answers? We hypothesize that a pre-trained language model can improve its reasoning…
Can LLMs Lie? Investigation beyond Hallucination
Haoran Huan, Mihir Prabhudesai, Mengning Wu +2
Large language models (LLMs) have demonstrated impressive capabilities across a variety of tasks, but their increasing autonomy in real-world applications raises concerns about the…
Diffusion Beats Autoregressive in Data-Constrained Settings
Mihir Prabhudesai, Mengning Wu, Amir Zadeh +2
Autoregressive (AR) models have long dominated the landscape of large language models, driving progress across a wide range of tasks. Recently, diffusion-based language models have…
Maximizing Confidence Alone Improves Reasoning
Mihir Prabhudesai, Lili Chen, Alex Ippoliti +3
Reinforcement learning (RL) has enabled machine learning models to achieve significant advances in many fields. Most recently, RL has empowered frontier language models to solve ch…
Unified Multimodal Discrete Diffusion
Alexander Swerdlow, Mihir Prabhudesai, Siddharth Gandhi +2
Multimodal generative models that can understand and generate across multiple modalities are dominated by autoregressive (AR) approaches, which process tokens sequentially from lef…