Spatial Interpretation and LLMs

The research work delves into the Spatial Reasoning capabilities of Large Language Models (LLMs) within the framework of Visual Question Answering (VQA). By employing rigorous methodologies, we assess LLM proficiency in discerning spatial relationships from visual stimuli. We highlight the models’ strengths and pinpoint areas of improvement, emphasizing ethical dimensions encompassing bias, privacy, and transparency. Our findings provide valuable insights into LLM potential and pave the way for enhancements in Natural Language Processing (NLP) research.