TroL: Traversal of Layers for Large Language and Vision Models
This addresses the problem of high computational costs for training and inference in vision-language models, making them more accessible, though it is incremental as it builds on existing model architectures.
The paper tackles the issue of large, resource-intensive open-source large language and vision models by introducing TroL, a family of efficient models with 1.8B to 7B parameters that uses layer traversal to simulate more layers without adding them physically, achieving performance comparable to larger open-source and closed-source models.
Large language and vision models (LLVMs) have been driven by the generalization power of large language models (LLMs) and the advent of visual instruction tuning. Along with scaling them up directly, these models enable LLVMs to showcase powerful vision language (VL) performances by covering diverse tasks via natural language instructions. However, existing open-source LLVMs that perform comparably to closed-source LLVMs such as GPT-4V are often considered too large (e.g., 26B, 34B, and 110B parameters), having a larger number of layers. These large models demand costly, high-end resources for both training and inference. To address this issue, we present a new efficient LLVM family with 1.8B, 3.8B, and 7B LLM model sizes, Traversal of Layers (TroL), which enables the reuse of layers in a token-wise manner. This layer traversing technique simulates the effect of looking back and retracing the answering stream while increasing the number of forward propagation layers without physically adding more layers. We demonstrate that TroL employs a simple layer traversing approach yet efficiently outperforms the open-source LLVMs with larger model sizes and rivals the performances of the closed-source LLVMs with substantial sizes.