8 papers · 1 filter
HashAttention: Semantic Sparsity for Faster Inference
Aditya Desai, Shuo Yang, Alejandro Cuadron +3
Leveraging long contexts is crucial for advanced AI systems, but attention computation poses a scalability challenge. While scaled dot-product attention (SDPA) exhibits token spars…
Optimizing LLM Queries in Relational Data Analytics Workloads
Shu Liu, Asim Biswal, Amog Kamsetty +8
Batch data analytics is a growing application for Large Language Models (LLMs). LLMs enable users to perform a wide range of natural language tasks, such as classification, entity…
RouteLLM: Learning to Route LLMs with Preference Data
Isaac Ong, Amjad Almahairi, Vincent Wu +5
Large language models (LLMs) exhibit impressive capabilities across a wide range of tasks, yet the choice of which model to use often involves a trade-off between performance and c…
S*: Test Time Scaling for Code Generation
Dacheng Li, Shiyi Cao, Chengkun Cao +6
Increasing test-time compute for LLMs shows promise across domains but remains underexplored in code generation, despite extensive study in math. In this paper, we propose S*, the…
How to Evaluate Reward Models for RLHF
Evan Frick, Tianle Li, Connor Chen +6
We introduce a new benchmark for reward models that quantifies their ability to produce strong language models through RLHF (Reinforcement Learning from Human Feedback). The gold-s…
From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline
Tianle Li, Wei-Lin Chiang, Evan Frick +5
The rapid evolution of Large Language Models (LLMs) has outpaced the development of model evaluation, highlighting the need for continuous curation of new, challenging benchmarks.…