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Found 7 papers, 6 papers with code
DataMix: Efficient Privacy-Preserving Edge-Cloud Inference
Third, our solution is extit{efficient} on the edge since the majority of the workload is delegated to the cloud, and our mixing and de-mixing processes introduce very few extra computations.
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset
Studying how people interact with large language models (LLMs) in real-world scenarios is increasingly important due to their widespread use in various applications.
Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
Evaluating large language model (LLM) based chat assistants is challenging due to their broad capabilities and the inadequacy of existing benchmarks in measuring human preferences.
Ranked #3 on
Long-Context Understanding
on Ada-LEval (TSort)
Representing Long-Range Context for Graph Neural Networks with Global Attention
Inspired by recent computer vision results that find position-invariant attention performant in learning long-range relationships, our method, which we call GraphTrans, applies a permutation-invariant Transformer module after a standard GNN module.
RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem
Researchers and practitioners in the field of reinforcement learning (RL) frequently leverage parallel computation, which has led to a plethora of new algorithms and systems in the last few years.
HAT: Hardware-Aware Transformers for Efficient Natural Language Processing
To enable low-latency inference on resource-constrained hardware platforms, we propose to design Hardware-Aware Transformers (HAT) with neural architecture search.
Ranked #21 on
Machine Translation
on WMT2014 English-French
Lite Transformer with Long-Short Range Attention
For language modeling, Lite Transformer achieves 1. 8 lower perplexity than the transformer at around 500M MACs.
Ranked #36 on
Machine Translation
on WMT2014 English-French
