Search Results for author:
Found 7 papers, 4 papers with code
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design
We present the sweet spot of quantization configuration of algorithm-system co-design that leads to high accuracy and system efficiency.
BatchLLM: Optimizing Large Batched LLM Inference with Global Prefix Sharing and Throughput-oriented Token Batching
The existing solutions use the LRU-based cache to reuse the KV context of common prefix between requests.
MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels
Recent breakthroughs in large models have highlighted the critical significance of data scale, labels and modals.
SPANN: Highly-efficient Billion-scale Approximate Nearest Neighbor Search
It stores the centroid points of the posting lists in the memory and the large posting lists in the disk.
SPANN: Highly-efficient Billion-scale Approximate Nearest Neighborhood Search
It stores the centroid points of the posting lists in the memory and the large posting lists in the disk.
GLOW : Global Weighted Self-Attention Network for Web Search
Deep matching models aim to facilitate search engines retrieving more relevant documents by mapping queries and documents into semantic vectors in the first-stage retrieval.
MIRA: Leveraging Multi-Intention Co-click Information in Web-scale Document Retrieval using Deep Neural Networks
The common framework is to train two encoding models based on neural embedding which learn the distributed representations of queries and documents separately and match them in the latent semantic space.
