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cs.CL2026
ERNIE 5.0 Technical Report
Haifeng Wang, Hua Wu, Tian Wu +432
In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…
cs.CL2024
Retrieving, Rethinking and Revising: The Chain-of-Verification Can Improve Retrieval Augmented Generation
Bolei He, Nuo Chen, Xinran He +4
Recent Retrieval Augmented Generation (RAG) aims to enhance Large Language Models (LLMs) by incorporating extensive knowledge retrieved from external sources. However, such approac…