EgoPlan-Bench: Benchmarking Multimodal Large Language Models for Human-Level Planning
This work addresses the need for better benchmarks to assess planning abilities in MLLMs, which is crucial for advancing artificial general intelligence, but it is incremental as it focuses on evaluation and dataset creation rather than a new planning method.
The authors tackled the problem of evaluating how far current Multimodal Large Language Models (MLLMs) are from achieving human-level planning by introducing EgoPlan-Bench, a comprehensive benchmark for real-world egocentric scenarios, and found that it poses significant challenges, indicating substantial room for improvement.
The pursuit of artificial general intelligence (AGI) has been accelerated by Multimodal Large Language Models (MLLMs), which exhibit superior reasoning, generalization capabilities, and proficiency in processing multimodal inputs. A crucial milestone in the evolution of AGI is the attainment of human-level planning, a fundamental ability for making informed decisions in complex environments, and solving a wide range of real-world problems. Despite the impressive advancements in MLLMs, a question remains: How far are current MLLMs from achieving human-level planning? To shed light on this question, we introduce EgoPlan-Bench, a comprehensive benchmark to evaluate the planning abilities of MLLMs in real-world scenarios from an egocentric perspective, mirroring human perception. EgoPlan-Bench emphasizes the evaluation of planning capabilities of MLLMs, featuring realistic tasks, diverse action plans, and intricate visual observations. Our rigorous evaluation of a wide range of MLLMs reveals that EgoPlan-Bench poses significant challenges, highlighting a substantial scope for improvement in MLLMs to achieve human-level task planning. To facilitate this advancement, we further present EgoPlan-IT, a specialized instruction-tuning dataset that effectively enhances model performance on EgoPlan-Bench. We have made all codes, data, and a maintained benchmark leaderboard available to advance future research.