Do Vision-Language Models Represent Space and How? Evaluating Spatial Frame of Reference Under Ambiguities

Spatial expressions in situated communication can be ambiguous, as their meanings vary depending on the frames of reference (FoR) adopted by speakers and listeners. While spatial language understanding and reasoning by vision-language models (VLMs) have gained increasing attention, potential ambiguities in these models are still under-explored. To address this issue, we present the COnsistent Multilingual Frame Of Reference Test (COMFORT), an evaluation protocol to systematically assess the spatial reasoning capabilities of VLMs. We evaluate nine state-of-the-art VLMs using COMFORT. Despite showing some alignment with English conventions in resolving ambiguities, our experiments reveal significant shortcomings of VLMs: notably, the models (1) exhibit poor robustness and consistency, (2) lack the flexibility to accommodate multiple FoRs, and (3) fail to adhere to languagespecific or culture-specific conventions in cross-lingual tests, as English tends to dominate other languages. With a growing effort to align vision-language models with human cognitive intuitions, we call for more attention to the ambiguous nature and cross-cultural diversity of spatial reasoning. The recent success of large language models has sparked breakthroughs in multi-modalities, leading to the development of many vision-language models (VLMs; Chen et al., 2023b; OpenAI, 2024; Reid et al., 2024, inter alia). With some benchmarks developed to evaluate the downstream performance of these models (Liu et al., 2023c; Yue et al., 2024), there has been growing excitement around evaluations and analyses inspired by human cognitive capabilities such as referential grounding (Ma et al., 2023a), compositional reasoning (Ma et al., 2023c), visual illusions (Zhang et al., 2023; Guan et al., 2024), and theory of mind (Jin et al., 2024). One direction among them that captures significant attention is spatial language understanding and reasoning, leading to several benchmarks (Kamath et al., 2023; Liu et al., 2023a) and enhanced models (Chen et al., 2024a; Cheng et al., 2024). Indeed, spatial cognition is a crucial part of human cognitive capability, developed since infancy and continuing through the elementary school years (Tommasi & Laeng, 2012; Vasilyeva & Lourenco, 2012). Language is closely intertwined with spatial cognition, with each contributing to the acquisition of the other (Hayward & Tarr, 1995; Regier & Carlson, 2001; Pyers et al., 2010; Pruden et al., 2011; Gentner et al., 2013). While spatial language and non-linguistic spatial representations in memory are closely correlated and share foundational properties, they are, to some extent, divergent-- spatial conventions are not consistently preserved across different languages or tasks, and humans demonstrate flexibility in using multiple coordinate systems for both non-linguistic reasoning and linguistic expressions (Munnich et al., 2001; Shusterman & Li, 2016).