4 citations · 13 across the 11 of their papers we have counts for
12 papers
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…
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…
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…
Speculate, then Collaborate: Fusing Knowledge of Language Models during Decoding
Ziyao Wang, Muneeza Azmat, Ang Li +2
Large Language Models (LLMs) often excel in specific domains but fall short in others due to the limitations of their training. Thus, enabling LLMs to solve problems collaborativel…
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…
Moderator: Moderating Text-to-Image Diffusion Models through Fine-grained Context-based Policies
Peiran Wang, Qiyu Li, Longxuan Yu +3
We present Moderator, a policy-based model management system that allows administrators to specify fine-grained content moderation policies and modify the weights of a text-to-imag…
FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations
Ziyao Wang, Zheyu Shen, Yexiao He +4
The rapid development of Large Language Models (LLMs) has been pivotal in advancing AI, with pre-trained LLMs being adaptable to diverse downstream tasks through fine-tuning. Feder…
SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning
Yexiao He, Ziyao Wang, Zheyu Shen +5
The pre-trained Large Language Models (LLMs) can be adapted for many downstream tasks and tailored to align with human preferences through fine-tuning. Recent studies have discover…
FedHyper: A Universal and Robust Learning Rate Scheduler for Federated Learning with Hypergradient Descent
Ziyao Wang, Jianyu Wang, Ang Li
The theoretical landscape of federated learning (FL) undergoes rapid evolution, but its practical application encounters a series of intricate challenges, and hyperparameter optimi…
UPTON: Preventing Authorship Leakage from Public Text Release via Data Poisoning
Ziyao Wang, Thai Le, Dongwon Lee
Consider a scenario where an author-e.g., activist, whistle-blower, with many public writings wishes to write "anonymously" when attackers may have already built an authorship attr…