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cs.CL2024
Improving Factuality with Explicit Working Memory
Mingda Chen, Yang Li, Karthik Padthe +5
Large language models can generate factually inaccurate content, a problem known as hallucination. Recent works have built upon retrieved-augmented generation to improve factuality…
cs.LG2024
Characterizing and Efficiently Accelerating Multimodal Generation Model Inference
Yejin Lee, Anna Sun, Basil Hosmer +27
Generative artificial intelligence (AI) technology is revolutionizing the computing industry. Not only its applications have broadened to various sectors but also poses new system…
cs.CL2024★ 18 cited
Chameleon: Mixed-Modal Early-Fusion Foundation Models
Chameleon Team
We present Chameleon, a family of early-fusion token-based mixed-modal models capable of understanding and generating images and text in any arbitrary sequence. We outline a stable…