Correlating instruction-tuning (in multimodal models) with vision-language processing (in the brain)

TL;DR

This study investigates how instruction-tuned multimodal language models align with brain activity during visual tasks, revealing that they outperform vision-only models and encode instruction-specific visual concepts, advancing understanding of brain-model correspondence.

Contribution

The paper demonstrates that instruction-tuned multimodal models show improved brain alignment and encode instruction-specific visual concepts, highlighting their potential for modeling neural responses.

Findings

MLLMs outperform vision-only models in brain alignment

MLLMs encode instruction-specific visual concepts

Shared variance between different instruction types in brain encoding

Abstract

Transformer-based language models, though not explicitly trained to mimic brain recordings, have demonstrated surprising alignment with brain activity. Progress in these models-through increased size, instruction-tuning, and multimodality-has led to better representational alignment with neural data. Recently, a new class of instruction-tuned multimodal LLMs (MLLMs) have emerged, showing remarkable zero-shot capabilities in open-ended multimodal vision tasks. However, it is unknown whether MLLMs, when prompted with natural instructions, lead to better brain alignment and effectively capture instruction-specific representations. To address this, we first investigate brain alignment, i.e., measuring the degree of predictivity of neural visual activity using text output response embeddings from MLLMs as participants engage in watching natural scenes. Experiments with 10 different instructions show that MLLMs exhibit significantly better brain alignment than vision-only models and perform comparably to non-instruction-tuned multimodal models like CLIP. We also find that while these MLLMs are effective at generating high-quality responses suitable to the task-specific instructions, not all instructions are relevant for brain alignment. Further, by varying instructions, we make the MLLMs encode instruction-specific visual concepts related to the input image. This analysis shows that MLLMs effectively capture count-related and recognition-related concepts, demonstrating strong alignment with brain activity. Notably, the majority of the explained variance of the brain encoding models is shared between MLLM embeddings of image captioning and other instructions. These results suggest that enhancing MLLMs' ability to capture task-specific information could lead to better differentiation between various types of instructions, and thereby improving their precision in predicting brain responses.