3 papers
cs.CL2026
WaterSearch: Exploring Seed Pooling for Improving the Quality-Detectability Trade-off in LLM Watermarking
Yukang Lin, Jiahao Shao, Shuoran Jiang +5
Watermarking acts as a critical safeguard in text generated by Large Language Models (LLMs). By embedding identifiable signals into model outputs, watermarking enables reliable att…
cs.CL2025
GeRe: Towards Efficient Anti-Forgetting in Continual Learning of LLM via General Samples Replay
Yunan Zhang, Shuoran Jiang, Mengchen Zhao +4
The continual learning capability of large language models (LLMs) is crucial for advancing artificial general intelligence. However, continual fine-tuning LLMs across various domai…
cs.CL2024
Reasoning Graph Enhanced Exemplars Retrieval for In-Context Learning
Yukang Lin, Bingchen Zhong, Shuoran Jiang +2
Large language models (LLMs) have exhibited remarkable few-shot learning capabilities and unified the paradigm of NLP tasks through the in-context learning (ICL) technique. Despite…