4 citations · 7 across the 7 of their papers we have counts for
4 papers · 1 filter
LatticeGen: A Cooperative Framework which Hides Generated Text in a Lattice for Privacy-Aware Generation on Cloud
Mengke Zhang, Tianxing He, Tianle Wang +5
In the current user-server interaction paradigm of prompted generation with large language models (LLM) on cloud, the server fully controls the generation process, which leaves zer…
A Benchmark on Extremely Weakly Supervised Text Classification: Reconcile Seed Matching and Prompting Approaches
Zihan Wang, Tianle Wang, Dheeraj Mekala +1
Etremely Weakly Supervised Text Classification (XWS-TC) refers to text classification based on minimal high-level human guidance, such as a few label-indicative seed words or class…
WOT-Class: Weakly Supervised Open-world Text Classification
Tianle Wang, Zihan Wang, Weitang Liu +1
State-of-the-art weakly supervised text classification methods, while significantly reduced the required human supervision, still requires the supervision to cover all the classes…
On the Blind Spots of Model-Based Evaluation Metrics for Text Generation
Tianxing He, Jingyu Zhang, Tianle Wang +4
In this work, we explore a useful but often neglected methodology for robustness analysis of text generation evaluation metrics: stress tests with synthetic data. Basically, we des…