26 citations · 27 across the 4 of their papers we have counts for
7 papers
GiFT: Gibbs Fine-Tuning for Code Generation
Haochen Li, Wanjin Feng, Xin Zhou +1
Training Large Language Models (LLMs) with synthetic data is a prevalent practice in code generation. A key approach is self-training, where LLMs are iteratively trained on self-ge…
LampMark: Proactive Deepfake Detection via Training-Free Landmark Perceptual Watermarks
Tianyi Wang, Mengxiao Huang, Harry Cheng +2
Deepfake facial manipulation has garnered significant public attention due to its impacts on enhancing human experiences and posing privacy threats. Despite numerous passive algori…
Beyond Similarity: Personalized Federated Recommendation with Composite Aggregation
Honglei Zhang, Haoxuan Li, Jundong Chen +6
Federated recommendation aims to collect global knowledge by aggregating local models from massive devices, to provide recommendations while ensuring privacy. Current methods mainl…
Enabling Patient-side Disease Prediction via the Integration of Patient Narratives
Zhixiang Su, Yinan Zhang, Jiazheng Jing +2
Disease prediction holds considerable significance in modern healthcare, because of its crucial role in facilitating early intervention and implementing effective prevention measur…
Are ID Embeddings Necessary? Whitening Pre-trained Text Embeddings for Effective Sequential Recommendation
Lingzi Zhang, Xin Zhou, Zhiwei Zeng +1
Recent sequential recommendation models have combined pre-trained text embeddings of items with item ID embeddings to achieve superior recommendation performance. Despite their eff…
Towards Goal-oriented Prompt Engineering for Large Language Models: A Survey
Haochen Li, Jonathan Leung, Zhiqi Shen
Large Language Models (LLMs) have shown prominent performance in various downstream tasks and prompt engineering plays a pivotal role in optimizing LLMs' performance. This paper, n…