7 papers
Revisiting Federated Fine-Tuning: A Single Communication Round is Enough for Foundation Models
Ziyao Wang, Bowei Tian, Yexiao He +6
The recent advancement of foundation models (FMs) has increased the demand for fine-tuning these models on large-scale cross-domain datasets. To address this, federated fine-tuning…
SymRTLO: Enhancing RTL Code Optimization with LLMs and Neuron-Inspired Symbolic Reasoning
Yiting Wang, Wanghao Ye, Ping Guo +11
Optimizing Register Transfer Level (RTL) code is crucial for improving the power, performance, and area (PPA) of digital circuits in the early stages of synthesis. Manual rewriting…
EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices
Zheyu Shen, Yexiao He, Ziyao Wang +4
Large Language Models (LLMs) have gained significant attention due to their versatility across a wide array of applications. Fine-tuning LLMs with parameter-efficient adapters, suc…
Invisible Tokens, Visible Bills: The Urgent Need to Audit Hidden Operations in Opaque LLM Services
Guoheng Sun, Ziyao Wang, Xuandong Zhao +5
Modern large language model (LLM) services increasingly rely on complex, often abstract operations, such as multi-step reasoning and multi-agent collaboration, to generate high-qua…
CoIn: Counting the Invisible Reasoning Tokens in Commercial Opaque LLM APIs
Guoheng Sun, Ziyao Wang, Bowei Tian +7
As post-training techniques evolve, large language models (LLMs) are increasingly augmented with structured multi-step reasoning abilities, often optimized through reinforcement le…
Prada: Black-Box LLM Adaptation with Private Data on Resource-Constrained Devices
Ziyao Wang, Yexiao He, Zheyu Shen +4
In recent years, Large Language Models (LLMs) have demonstrated remarkable abilities in various natural language processing tasks. However, adapting these models to specialized dom…