Graph Descriptive Order Improves Reasoning with Large Language Model

In recent years, large language models have achieved state-of-the-art performance across multiple domains. However, the progress in the field of graph reasoning with LLM remains limited. Our work delves into this gap by thoroughly investigating graph reasoning with LLMs. In this work, we reveal the impact of the order of graph description on LLMs' graph reasoning performance, which significantly affects LLMs' reasoning abilities. By altering this order, we enhance the performance of LLMs from 42.22\% to 70\%. Furthermore, we introduce the Scaled Graph Reasoning benchmark for assessing LLMs' performance across various graph sizes and evaluate the relationship between LLMs' graph reasoning abilities and graph size. We discover that the graph reasoning performance of LLMs does not monotonically decrease with the increase in graph size. The experiments span several mainstream models, including GPT-3.5, LLaMA-2-7B, and LLaMA-2-13B, to offer a comprehensive evaluation.

Sequential Ordering in Textual Descriptions: Impact on Spatial Perception Abilities of Large Language Models

In recent years, Large Language Models have reached state-of-the-art performance across multiple domains. However, the progress in the field of graph reasoning remains limited. Our work delves into this gap by thoroughly investigating graph reasoning with LLM. In this work, we reveal the impact of text sequence on LLM spatial understanding, finding that graph-descriptive text sequences significantly affect LLM reasoning performance on graphs. By altering the graph-descriptive text sequences, we enhance the performance of LLM from 42.22\% to 70\%. Furthermore, we evaluate the relationship between LLM performance and graph size, discovering that the reasoning performance of LLM does not monotonically decrease with the increase in graph size. Conclusively, we introduce the Scaled Graph Reasoning benchmark for assessing LLM performance across varied graph sizes.

Self-supervised Spatiotemporal Representation Learning by Exploiting Video Continuity

Recent self-supervised video representation learning methods have found significant success by exploring essential properties of videos, e.g. speed, temporal order, etc. This work exploits an essential yet under-explored property of videos, the video continuity, to obtain supervision signals for self-supervised representation learning. Specifically, we formulate three novel continuity-related pretext tasks, i.e. continuity justification, discontinuity localization, and missing section approximation, that jointly supervise a shared backbone for video representation learning. This self-supervision approach, termed as Continuity Perception Network (CPNet), solves the three tasks altogether and encourages the backbone network to learn local and long-ranged motion and context representations. It outperforms prior arts on multiple downstream tasks, such as action recognition, video retrieval, and action localization. Additionally, the video continuity can be complementary to other coarse-grained video properties for representation learning, and integrating the proposed pretext task to prior arts can yield much performance gains.