LAVE: Zero-shot VQA Evaluation on Docmatix with LLMs - Do We Still Need Fine-Tuning?

The article presents LAVE, a novel framework for zero-shot visual question answering (VQA) evaluation using large language models (LLMs) on the Docmatix dataset. It demonstrates that LLMs can achieve competitive performance without fine-tuning, leveraging their pre-trained capabilities, which challenges the necessity of fine-tuning for specific tasks. This finding is significant for practitioners as it suggests that LLMs can be effectively utilized in VQA applications with minimal additional training, potentially reducing resource requirements.