7 citations · 11 across the 10 of their papers we have counts for
5 papers · 1 filter
Serving Heterogeneous LoRA Adapters in Distributed LLM Inference Systems
Shashwat Jaiswal, Shrikara Arun, Anjaly Parayil +8
Low-Rank Adaptation (LoRA) has become the de facto method for parameter-efficient fine-tuning of large language models (LLMs), enabling rapid adaptation to diverse domains. In prod…
BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute
Dujian Ding, Ankur Mallick, Shaokun Zhang +7
Large language models (LLMs) are powerful tools but are often expensive to deploy at scale. LLM query routing mitigates this by dynamically assigning queries to models of varying c…
Enhancing Reasoning Capabilities of Small Language Models with Blueprints and Prompt Template Search
Dongge Han, Menglin Xia, Daniel Madrigal Diaz +7
Small language models (SLMs) offer promising and efficient alternatives to large language models (LLMs). However, SLMs' limited capacity restricts their reasoning capabilities and…
Exploring How LLMs Capture and Represent Domain-Specific Knowledge
Mirian Hipolito Garcia, Camille Couturier, Daniel Madrigal Diaz +5
We study whether Large Language Models (LLMs) inherently capture domain-specific nuances in natural language. Our experiments probe the domain sensitivity of LLMs by examining thei…
SageServe: Optimizing LLM Serving on Cloud Data Centers with Forecast Aware Auto-Scaling
Shashwat Jaiswal, Kunal Jain, Yogesh Simmhan +9
Global cloud service providers handle inference workloads for Large Language Models (LLMs) that span latency-sensitive (e.g., chatbots) and insensitive (e.g., report writing) tasks…