MANGO: A Benchmark for Evaluating Mapping and Navigation Abilities of Large Language Models
This work addresses the need for better evaluation of spatial reasoning in language models, though it is incremental as it introduces a new benchmark rather than a novel method.
The authors tackled the problem of evaluating mapping and navigation abilities in large language models by proposing MANGO, a benchmark with 53 text-based mazes, and found that even GPT-4 performs poorly on these tasks, with potential benefits for downstream applications like textgames.
Large language models such as ChatGPT and GPT-4 have recently achieved astonishing performance on a variety of natural language processing tasks. In this paper, we propose MANGO, a benchmark to evaluate their capabilities to perform text-based mapping and navigation. Our benchmark includes 53 mazes taken from a suite of textgames: each maze is paired with a walkthrough that visits every location but does not cover all possible paths. The task is question-answering: for each maze, a large language model reads the walkthrough and answers hundreds of mapping and navigation questions such as "How should you go to Attic from West of House?" and "Where are we if we go north and east from Cellar?". Although these questions are easy to humans, it turns out that even GPT-4, the best-to-date language model, performs poorly at answering them. Further, our experiments suggest that a strong mapping and navigation ability would benefit large language models in performing relevant downstream tasks, such as playing textgames. Our MANGO benchmark will facilitate future research on methods that improve the mapping and navigation capabilities of language models. We host our leaderboard, data, code, and evaluation program at https://mango.ttic.edu and https://github.com/oaklight/mango/.