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Found 9 papers, 8 papers with code
Manifold Identification for Ultimately Communication-Efficient Distributed Optimization
The expensive inter-machine communication is the bottleneck of distributed optimization.
Mélange: Cost Efficient Large Language Model Serving by Exploiting GPU Heterogeneity
Within this space, we show that there is not a linear relationship between GPU cost and performance, and identify three key LLM service characteristics that significantly affect which GPU type is the most cost effective: model request size, request rate, and latency service-level objective (SLO).
Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference
To address this issue, we introduce Chatbot Arena, an open platform for evaluating LLMs based on human preferences.
LLM-Assisted Code Cleaning For Training Accurate Code Generators
In this work, we investigate data quality for code and find that making the code more structured and readable leads to improved code generation performance of the system.
Rethinking Benchmark and Contamination for Language Models with Rephrased Samples
Many have raised concerns about the trustworthiness of public benchmarks due to potential contamination in pre-training or fine-tuning datasets.
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)
Balsa: Learning a Query Optimizer Without Expert Demonstrations
Query optimizers are a performance-critical component in every database system.
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
Furthermore, Cluster-GCN allows us to train much deeper GCN without much time and memory overhead, which leads to improved prediction accuracy---using a 5-layer Cluster-GCN, we achieve state-of-the-art test F1 score 99. 36 on the PPI dataset, while the previous best result was 98. 71 by [16].
Ranked #1 on
Node Classification
on Amazon2M
