5.7k citations · 8.2k across the 48 of their papers we have counts for
9 papers · 1 filter
IG Captioner: Information Gain Captioners are Strong Zero-shot Classifiers
Chenglin Yang, Siyuan Qiao, Yuan Cao +4
Generative training has been demonstrated to be powerful for building visual-language models. However, on zero-shot discriminative benchmarks, there is still a performance gap betw…
RoboVQA: Multimodal Long-Horizon Reasoning for Robotics
Pierre Sermanet, Tianli Ding, Jeffrey Zhao +18
We present a scalable, bottom-up and intrinsically diverse data collection scheme that can be used for high-level reasoning with long and medium horizons and that has 2.2x higher t…
SLM: Bridge the thin gap between speech and text foundation models
Mingqiu Wang, Wei Han, Izhak Shafran +15
We present a joint Speech and Language Model (SLM), a multitask, multilingual, and dual-modal model that takes advantage of pretrained foundational speech and language models. SLM…
Speech-to-Text Adapter and Speech-to-Entity Retriever Augmented LLMs for Speech Understanding
Mingqiu Wang, Izhak Shafran, Hagen Soltau +4
Large Language Models (LLMs) have been applied in the speech domain, often incurring a performance drop due to misaligned between speech and language representations. To bridge thi…
Grammar Prompting for Domain-Specific Language Generation with Large Language Models
Bailin Wang, Zi Wang, Xuezhi Wang +3
Large language models (LLMs) can learn to perform a wide range of natural language tasks from just a handful of in-context examples. However, for generating strings from highly str…
Can Public Large Language Models Help Private Cross-device Federated Learning?
Boxin Wang, Yibo Jacky Zhang, Yuan Cao +5
We study (differentially) private federated learning (FL) of language models. The language models in cross-device FL are relatively small, which can be trained with meaningful form…
Tree of Thoughts: Deliberate Problem Solving with Large Language Models
Shunyu Yao, Dian Yu, Jeffrey Zhao +4
Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making proce…
Binarized Neural Machine Translation
Yichi Zhang, Ankush Garg, Yuan Cao +4
The rapid scaling of language models is motivating research using low-bitwidth quantization. In this work, we propose a novel binarization technique for Transformers applied to mac…
MUX-PLMs: Data Multiplexing for High-throughput Language Models
Vishvak Murahari, Ameet Deshpande, Carlos E. Jimenez +4
The widespread adoption of large language models such as ChatGPT and Bard has led to unprecedented demand for these technologies. The burgeoning cost of inference for ever-increasi…