5 papers · 1 filter
Auditing Information Disclosure During Large-Scale Gradient-Based Training via Gradient Uniqueness
Sleem Abdelghafar, Maryam Aliakbarpour, Chris Jermaine +1
Auditing information disclosure across every datapoint during the training of LLMs is challenging. We propose a principled, attack-agnostic approach that uses mutual information to…
MHA-RAG: Improving Efficiency, Accuracy, and Consistency by Encoding Exemplars as Soft Prompts
Abhinav Jain, Xinyu Yao, Thomas Reps +1
Adapting Foundation Models to new domains with limited training data is challenging and computationally expensive. While prior work has demonstrated the effectiveness of using doma…
SIMCOPILOT: Evaluating Large Language Models for Copilot-Style Code Generation
Mingchao Jiang, Abhinav Jain, Sophia Zorek +1
We introduce SIMCOPILOT, a benchmark that simulates the role of large language models (LLMs) as interactive, "copilot"-style coding assistants. Targeting both completion (finishing…
DOPPLER: Dual-Policy Learning for Device Assignment in Asynchronous Dataflow Graphs
Xinyu Yao, Daniel Bourgeois, Abhinav Jain +5
We study the problem of assigning operations in a dataflow graph to devices to minimize execution time in a work-conserving system, with emphasis on complex machine learning worklo…
Resource-efficient Inference with Foundation Model Programs
Lunyiu Nie, Zhimin Ding, Kevin Yu +3
The inference-time resource costs of large language and vision models present a growing challenge in production deployments. We propose the use of foundation model programs, i.e.,…