33 citations · 47 across the 16 of their papers we have counts for
7 papers · 1 filter
SLMFix: Leveraging Small Language Models for Domain Specific Language Error Fixing with Reinforcement Learning
David Jiahao Fu, Aryan Gupta, Aaron Councilman +3
Large language models (LLMs) have shown impressive capabilities in code generation across many programming languages but even state-of-the-art LLMs generate programs that contain s…
Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs
Yifan Zhao, Egan Johnson, Prasanth Chatarasi +2
Operator fusion has become a key optimization for deep learning, which combines multiple deep learning operators to improve data reuse and reduce global memory transfers. However,…
Towards Formal Verification of LLM-Generated Code from Natural Language Prompts
Aaron Councilman, David Jiahao Fu, Aryan Gupta +4
In the past few years LLMs have emerged as a tool that can aid programmers by taking natural language descriptions and generating code based on it. However, the reliability of LLM…
MIRAGE: A Benchmark for Multimodal Information-Seeking and Reasoning in Agricultural Expert-Guided Conversations
Vardhan Dongre, Chi Gui, Shubham Garg +4
We introduce MIRAGE, a new benchmark for multimodal expert-level reasoning and decision-making in consultative interaction settings. Designed for the agriculture domain, MIRAGE cap…
AgMMU: A Comprehensive Agricultural Multimodal Understanding Benchmark
Aruna Gauba, Irene Pi, Yunze Man +3
We present AgMMU, a challenging real-world benchmark for evaluating and advancing vision-language models (VLMs) in the knowledge-intensive domain of agriculture. Unlike prior datas…
Hercules: A Compiler for Productive Programming of Heterogeneous Systems
Russel Arbore, Aaron Councilman, Xavier Routh +3
Modern computing systems increasingly rely on composing heterogeneous devices to improve performance and efficiency. Programming these systems is often unproductive: algorithm impl…
LEWIS (LayEr WIse Sparsity) -- A Training Free Guided Model Merging Approach
Hetarth Chopra, Vidhi Rambhia, Vikram Adve
As specialized large language models (LLMs) become increasingly prevalent, model merging methods are being used to combine them to create a single multi-task model without requirin…