Jing-Bo Zhu

Findings show that configuration changes can affect how models use information they can still read, and attention interventions in LLaVA-NeXT suggest that configuration changes can weaken the use of readable information during answering.

Deferring translation in multilingual question answering can significantly reduce errors and improve efficiency, leading to more accurate results across diverse languages.

Uncover the core principles of LLMs, from pre-training to reasoning, in a structured, foundational guide.