Search Results for author:
Found 6 papers, 4 papers with code
UDA: A Benchmark Suite for Retrieval Augmented Generation in Real-world Document Analysis
The use of Retrieval-Augmented Generation (RAG) has improved Large Language Models (LLMs) in collaborating with external data, yet significant challenges exist in real-world scenarios.
ReaLHF: Optimized RLHF Training for Large Language Models through Parameter Reallocation
Based on this formulation, ReaLHF employs a tailored search algorithm with a lightweight cost estimator to discover an efficient execution plan.
Computing in the Era of Large Generative Models: From Cloud-Native to AI-Native
In this paper, we investigate the intersection of large generative AI models and cloud-native computing architectures.
SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores
Following this abstraction, we develop a scalable, efficient, and extensible distributed RL system called ReaLlyScalableRL, which allows efficient and massively parallelized training and easy development of customized algorithms.
LeCo: Lightweight Compression via Learning Serial Correlations
Lightweight data compression is a key technique that allows column stores to exhibit superior performance for analytical queries.
Proteus: A Self-Designing Range Filter
We introduce Proteus, a novel self-designing approximate range filter, which configures itself based on sampled data in order to optimize its false positive rate (FPR) for a given space requirement.
