Subsequently, the generated 2D masks are employed to align mask-level 3D representations with the vision-language feature space, thereby augmenting the open vocabulary capability of 3D geometry embeddings.

This enables more efficient data encoding and precise retrieval, significantly reducing prompt lengths and mitigating information loss. We have developed two new million-token benchmarks from the Arcade and BIRD-SQL datasets to thoroughly evaluate TableRAG's effectiveness at scale.

In this paper, we explore this novel task, which attempts to learn a generator directly from a pre-trained classifier, without the assistance of any training data.

Recently large language models like GPT -3 have shown encouraging performance as implicit knowledge source and revealed planning abilities.