168 citations · 503 across the 25 of their papers we have counts for
25 papers
Gemma 3 Technical Report
Gemma Team, Aishwarya Kamath, Johan Ferret +209
We introduce Gemma 3, a multimodal addition to the Gemma family of lightweight open models, ranging in scale from 1 to 27 billion parameters. This version introduces vision underst…
Automata-based constraints for language model decoding
Terry Koo, Frederick Liu, Luheng He
Language models (LMs) are often expected to generate strings in some formal language; for example, structured data, API calls, or code snippets. Although LMs can be tuned to improv…
"We Need Structured Output": Towards User-centered Constraints on Large Language Model Output
Michael Xieyang Liu, Frederick Liu, Alexander J. Fiannaca +4
Large language models can produce creative and diverse responses. However, to integrate them into current developer workflows, it is essential to constrain their outputs to follow…
SARA-RT: Scaling up Robotics Transformers with Self-Adaptive Robust Attention
Isabel Leal, Krzysztof Choromanski, Deepali Jain +11
We present Self-Adaptive Robust Attention for Robotics Transformers (SARA-RT): a new paradigm for addressing the emerging challenge of scaling up Robotics Transformers (RT) for on-…
UT5: Pretraining Non autoregressive T5 with unrolled denoising
Mahmoud G. Salem, Jiayu Ye, Chu-Cheng Lin +1
Recent advances in Transformer-based Large Language Models have made great strides in natural language generation. However, to decode K tokens, an autoregressive model needs K sequ…
Non-Intrusive Adaptation: Input-Centric Parameter-efficient Fine-Tuning for Versatile Multimodal Modeling
Yaqing Wang, Jialin Wu, Tanmaya Dabral +8
Large language models (LLMs) and vision language models (VLMs) demonstrate excellent performance on a wide range of tasks by scaling up parameter counts from O(10^9) to O(10^{12})…