6 citations · 6 across the 2 of their papers we have counts for
6 papers
Bilevel Data Curation for LLM Fine-tuning: Offline Selection and Online Self-Refining Generation
Quan Xiao, Yutong Xuan, Gaowen Liu +2
Supervised fine-tuning (SFT) datasets are critical to the downstream performance of large language models, yet they often contain low-quality or harmful question-response pairs. To…
A Survey on Large Language Model-Based Game Agents
Sihao Hu, Tiansheng Huang, Gaowen Liu +6
Game environments provide rich, controllable settings that stimulate many aspects of real-world complexity. As such, game agents offer a valuable testbed for exploring capabilities…
QUOTA: Quantifying Objects with Text-to-Image Models for Any Domain
Wenfang Sun, Yingjun Du, Gaowen Liu +2
We tackle the problem of quantifying the number of objects by a generative text-to-image model. Rather than retraining such a model for each new image domain of interest, which lea…
FedHFT: Efficient Federated Finetuning with Heterogeneous Edge Clients
Fatih Ilhan, Selim Furkan Tekin, Tiansheng Huang +6
Fine-tuning pre-trained large language models (LLMs) has become a common practice for personalized natural language understanding (NLU) applications on downstream tasks and domain-…
A First-order Generative Bilevel Optimization Framework for Diffusion Models
Quan Xiao, Hui Yuan, A F M Saif +4
Diffusion models, which iteratively denoise data samples to synthesize high-quality outputs, have achieved empirical success across domains. However, optimizing these models for do…
Prompt Diffusion Robustifies Any-Modality Prompt Learning
Yingjun Du, Gaowen Liu, Yuzhang Shang +3
Foundation models enable prompt-based classifiers for zero-shot and few-shot learning. Nonetheless, the conventional method of employing fixed prompts suffers from distributional s…