4 citations · 4 across the 2 of their papers we have counts for
2 papers
cs.LG2026
Mixture of Complementary Agents for Robust LLM Ensemble
Yichi Zhang, Kevin Lu, Yuang Zhang +3
Multi-AI collaboration, such as ensembling or debating large language models (LLMs), is a promising paradigm for aggregating information and boosting performance. A foundational st…
cs.CL2023★ 4 cited
LLM-augmented Preference Learning from Natural Language
Inwon Kang, Sikai Ruan, Tyler Ho +4
Finding preferences expressed in natural language is an important but challenging task. State-of-the-art(SotA) methods leverage transformer-based models such as BERT, RoBERTa, etc.…