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MANGO: A Benchmark for Evaluating Mapping and Navigation Abilities of Large Language Models


Core Concepts
Large language models like ChatGPT and GPT-4 perform poorly on a benchmark that tests their ability to construct maps and navigate through complex text-based environments, suggesting a need to improve their spatial reasoning and mapping capabilities.
Abstract
The paper introduces MANGO, a benchmark to evaluate the mapping and navigation abilities of large language models. The benchmark includes 53 mazes taken from text-based adventure games, each paired with hundreds of questions that test the model's ability to find destinations and plan routes through the maze. The key highlights and insights are: The benchmark is created by extracting walkthroughs from a suite of text-based games, annotating the locations and movement actions, and generating destination-finding and route-finding questions. The questions involve paths that are not explicitly covered in the walkthroughs, requiring the models to reason about the spatial relationships between locations. Experiments show that even the state-of-the-art language model GPT-4 performs poorly on this benchmark, correctly answering only about half of the route-finding questions. The paper draws a connection between language models and robotics, suggesting that improving mapping and navigation abilities could benefit language models on relevant downstream tasks like playing text-based games. The paper provides a detailed analysis of the performance of GPT-3.5 and GPT-4 on the benchmark, identifying factors that influence their success on easy and hard questions. A case study shows that equipping language models with explicit knowledge about the spatial layout of the environment can significantly improve their performance on a downstream task of playing minigames.
Stats
The benchmark includes 53 mazes taken from text-based adventure games. Each maze is paired with hundreds of destination-finding and route-finding questions. GPT-4 correctly answered only about 50% of the route-finding questions.
Quotes
"Mapping and navigation are fundamental abilities of human intelligence (Spiers & Maguire, 2006; Epstein et al., 2017)." "Do large language models (LLMs) possess such abilities? In this paper, we investigate this research question by creating a benchmark and evaluating several widely used LLMs." "GPT-4 only correctly answered half of the route-finding questions, performing disastrously on the difficult questions (e.g., those involving long and unseen routes)."

Key Insights Distilled From

by Peng Ding,Ji... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2403.19913.pdf
MANGO

Deeper Inquiries

How could the MANGO benchmark be extended to incorporate multimodal inputs (e.g., visual information) to better reflect real-world navigation scenarios?

To incorporate multimodal inputs into the MANGO benchmark, we can introduce visual information alongside textual descriptions of the environments. This integration would better reflect real-world navigation scenarios where individuals rely on both visual cues and textual instructions to navigate. Here are some ways to extend the benchmark: Visual Representations: Include images or maps of the environments along with textual descriptions. This would require models to interpret both visual and textual information to navigate accurately. Multimodal Questioning: Pose questions that require models to integrate information from both modalities. For example, asking about the spatial relationship between objects in an image and locations mentioned in the text. Interactive Navigation: Implement interactive elements where models can make decisions based on both visual and textual inputs, simulating real-time navigation challenges. Evaluation Metrics: Develop metrics that assess the model's performance in integrating and utilizing information from multiple modalities effectively. By incorporating multimodal inputs, the MANGO benchmark can provide a more comprehensive evaluation of language models' mapping and navigation abilities in real-world scenarios.

What other cognitive abilities beyond mapping and navigation could be tested using text-based environments, and how could these be incorporated into future benchmarks?

Text-based environments offer a versatile platform to test a wide range of cognitive abilities beyond mapping and navigation. Some additional cognitive abilities that could be tested include: Problem-Solving: Present complex scenarios or puzzles that require logical reasoning and problem-solving skills to navigate through the environment successfully. Memory and Recall: Design tasks that assess the model's ability to remember and recall spatial information, locations, and sequences of actions. Planning and Decision-Making: Create challenges that require the model to plan routes, make decisions based on limited information, and adapt strategies in dynamic environments. Spatial Reasoning: Test the model's spatial reasoning skills by asking questions that involve understanding spatial relationships, distances, and orientations. To incorporate these cognitive abilities into future benchmarks, tasks can be designed to specifically target each skill set. Questions and challenges can be structured to evaluate the model's performance in these areas, with clear evaluation criteria and metrics to measure success.

Given the connection between language models and robotics highlighted in this paper, how could techniques developed for text-based navigation be applied to improve the navigation capabilities of physical robots?

The techniques developed for text-based navigation can be applied to enhance the navigation capabilities of physical robots in the following ways: Semantic Mapping: Use language models to create semantic maps of environments based on textual descriptions. These maps can help robots understand spatial relationships and navigate more efficiently. Natural Language Instructions: Enable robots to interpret natural language instructions for navigation, similar to how language models process textual walkthroughs. This can enhance human-robot interaction and task execution. Multimodal Integration: Integrate visual and textual inputs to provide robots with a comprehensive understanding of their surroundings, enabling them to navigate effectively in complex environments. Adaptive Navigation: Implement adaptive navigation strategies that allow robots to adjust their paths based on real-time feedback and changing environmental conditions, similar to how language models adapt their responses to new information. By leveraging techniques from text-based navigation, physical robots can improve their spatial awareness, decision-making capabilities, and overall navigation efficiency in diverse real-world scenarios.
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