3 citations · 4 across the 5 of their papers we have counts for
5 papers
Towards Active Synthetic Data Generation for Finetuning Language Models
Samuel Kessler, Menglin Xia, Daniel Madrigal Diaz +5
A common and effective means for improving language model capabilities involves finetuning a ``student'' language model's parameters on generations from a more proficient ``teacher…
OdysseyBench: Evaluating LLM Agents on Long-Horizon Complex Office Application Workflows
Weixuan Wang, Dongge Han, Daniel Madrigal Diaz +3
Autonomous agents powered by large language models (LLMs) are increasingly deployed in real-world applications requiring complex, long-horizon workflows. However, existing benchmar…
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…
Project Florida: Federated Learning Made Easy
Daniel Madrigal Diaz, Andre Manoel, Jialei Chen +2
We present Project Florida, a system architecture and software development kit (SDK) enabling deployment of large-scale Federated Learning (FL) solutions across a heterogeneous dev…