Abstract 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 the codes, data, and a maintained benchmark leaderboard available at https://chenyi99.github.io/ego_plan/ to advance future research.

Abstract Multimodal Large Language Models (MLLMs) has recently demonstrated superior multimodal comprehension abilities, heralding a new era for artificial general intelligence (AGI). However, achieving AGI necessitates more than just comprehension. A crucial capability required is effective planning in diverse scenarios, which involves making reasonable decisions based on complex environments to solve real-world problems. Despite its importance, the planning abilities of current MLLMs in varied scenarios remain underexplored, leaving a significant gap in our understanding of their full potential. In this paper, we introduce EgoPlan-Bench2, a rigorous and comprehensive benchmark designed to assess the planning capabilities of MLLMs across a wide range of real-world scenarios . EgoPlan-Bench2 encompasses everyday tasks spanning 4 major domains and 24 detailed scenarios, closely aligned with human daily life. It is constructed through a semi-automatic process utilizing egocentric videos, complemented by manual verification. Grounded in a first-person perspective, it mirrors the way humans approach problem-solving in everyday life. We evaluate 25 competitive MLLMs and provide an in-depth analysis of their limitations, revealing that they face significant challenges in real-world planning. To diagnose the underlying bottlenecks, we investigate the effectiveness of various prompts via a training-free multimodal prompting method. We find that MLLMs’ planning performance on EgoPlan-Bench2 is critically dependent on temporally structured action sequences in historical task progress and interactions between objects and humans in current observation state. This dependency also underscores the necessity for strong reasoning abilities to integrate diverse multimodal cues and analysis before making final decision. Building on this insight, we demonstrate that EgoPlan-Bench2 is also an effective video reasoning benchmark. Experiments with Gemini-2.5-Flash and a post-trained Qwen-2.5-VL confirm its ability to distinguish between models with and without explicit deliberate reasoning mechanisms, showcasing the tangible impact of DeepSeek-R1 paradigm reasoning on planning tasks. We have made data and code available at https://qiulu66.github.io/egoplanbench2/ .

Yi Chen, Jiayang Cheng, Haiyun Jiang, Lemao Liu, Haisong Zhang, Shuming Shi, Ruifeng Xu. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

Existing approaches for sarcasm detection are mainly based on supervised learning, in which the promising performance largely depends on a considerable amount of labeled data or extra information. In the real world scenario, however, the abundant labeled data or extra information requires high labor cost, not to mention that sufficient annotated data is unavailable in many low-resource conditions. To alleviate this dilemma, we investigate sarcasm detection from an unsupervised perspective, in which we explore a masking and generation paradigm in the context to extract the context incongruities for learning sarcastic expression. Further, to improve the feature representations of the sentences, we use unsupervised contrastive learning to improve the sentence representation based on the standard dropout. Experimental results on six perceived sarcasm detection benchmark datasets show that our approach outperforms baselines. Simultaneously, our unsupervised method obtains comparative performance with supervised methods for the intended sarcasm dataset.

Auxiliary information from multiple sources has been demonstrated to be effective in zeroshot fine-grained entity typing (ZFET).However, there lacks a comprehensive understanding about how to make better use of the existing information sources and how they affect the performance of ZFET.In this paper, we empirically study three kinds of auxiliary information: context consistency, type hierarchy and background knowledge (e.g., prototypes and descriptions) of types, and propose a multi-source fusion model (MSF) targeting these sources.The performance obtains up to 11.42% and 22.84% absolute gains over stateof-the-art baselines on BBN and Wiki respectively with regard to macro F1 scores.More importantly, we further discuss the characteristics, merits and demerits of each information source and provide an intuitive understanding of the complementarity among them.