3 citations · 6 across the 4 of their papers we have counts for
4 papers
TaP: A Taxonomy-Guided Framework for Automated and Scalable Preference Data Generation
Renren Jin, Tianhao Shen, Xinwei Wu +9
Conducting supervised and preference fine-tuning of large language models (LLMs) requires high-quality datasets to improve their ability to follow instructions and align with human…
ChatSOP: An SOP-Guided MCTS Planning Framework for Controllable LLM Dialogue Agents
Zhigen Li, Jianxiang Peng, Yanmeng Wang +13
Dialogue agents powered by Large Language Models (LLMs) show superior performance in various tasks. Despite the better user understanding and human-like responses, their **lack of…
Automated Progressive Red Teaming
Bojian Jiang, Yi Jing, Tianhao Shen +3
Ensuring the safety of large language models (LLMs) is paramount, yet identifying potential vulnerabilities is challenging. While manual red teaming is effective, it is time-consum…
Attention Focusing for Neural Machine Translation by Bridging Source and Target Embeddings
Shaohui Kuang, Junhui Li, António Branco +2
In neural machine translation, a source sequence of words is encoded into a vector from which a target sequence is generated in the decoding phase. Differently from statistical mac…