2 citations · 2 across the 8 of their papers we have counts for
5 papers · 1 filter
VeriLocc: End-to-End Cross-Architecture Register Allocation via LLM
Lesheng Jin, Zhenyuan Ruan, Haohui Mai +1
Modern GPUs evolve rapidly, yet production compilers still rely on hand-crafted register allocation heuristics that require substantial re-tuning for each hardware generation. We i…
When is the consistent prediction likely to be a correct prediction?
Alex Nguyen, Dheeraj Mekala, Chengyu Dong +1
Self-consistency (Wang et al., 2023) suggests that the most consistent answer obtained through large language models (LLMs) is more likely to be correct. In this paper, we challeng…
TOOLVERIFIER: Generalization to New Tools via Self-Verification
Dheeraj Mekala, Jason Weston, Jack Lanchantin +4
Teaching language models to use tools is an important milestone towards building general assistants, but remains an open problem. While there has been significant progress on learn…
Smaller Language Models are capable of selecting Instruction-Tuning Training Data for Larger Language Models
Dheeraj Mekala, Alex Nguyen, Jingbo Shang
Instruction-tuning language models has become a crucial step in aligning them for general use. Typically, this process involves extensive training on large datasets, incurring high…
MedLM: Exploring Language Models for Medical Question Answering Systems
Niraj Yagnik, Jay Jhaveri, Vivek Sharma +1
In the face of rapidly expanding online medical literature, automated systems for aggregating and summarizing information are becoming increasingly crucial for healthcare professio…