arXiv subjects

Yanying Zhou

Publications and source records attributed to Yanying Zhou.

3 recordsLinked to original sources

arXiv2025

Reverse Modeling in Large Language Models

Humans are accustomed to reading and writing in a forward manner, and this natural bias extends to text understanding in auto-regressive large language models (LLMs). This paper investigates whether LLMs, like humans, struggle with reverse modeling, specifically with reversed text inputs. We found that publicly available pre-trained LLMs cannot understand such inputs. However, LLMs trained from scratch with both forward and reverse texts can understand them equally well during inference across multiple languages. Our case study shows that different-content texts result in different losses if input (to LLMs) in different directions -- some get lower losses for forward while some for reverse. This leads us to a simple and nice solution for data selection based on the loss differences between forward and reverse directions. Using our selected data in continued pretraining can boost LLMs' performance by a large margin across different language understanding benchmarks.

cs.CL↗

arXiv2024

Foresight Social-aware Reinforcement Learning for Robot Navigation

When robots handle navigation tasks while avoiding collisions, they perform in crowded and complex environments not as good as in stable and homogeneous environments. This often results in a low success rate and poor efficiency. Therefore, we propose a novel Foresight Social-aware Reinforcement Learning (FSRL) framework for mobile robots to achieve collision-free navigation. Compared to previous learning-based methods, our approach is foresighted. It not only considers the current human-robot interaction to avoid an immediate collision, but also estimates upcoming social interactions to still keep distance in the future. Furthermore, an efficiency constraint is introduced in our approach that significantly reduces navigation time. Comparative experiments are performed to verify the effectiveness and efficiency of our proposed method under more realistic and challenging simulated environments.

cs.RO↗

arXiv2024

Learning Crowd Behaviors in Navigation with Attention-based Spatial-Temporal Graphs

Safe and efficient navigation in dynamic environments shared with humans remains an open and challenging task for mobile robots. Previous works have shown the efficacy of using reinforcement learning frameworks to train policies for efficient navigation. However, their performance deteriorates when crowd configurations change, i.e. become larger or more complex. Thus, it is crucial to fully understand the complex, dynamic, and sophisticated interactions of the crowd resulting in proactive and foresighted behaviors for robot navigation. In this paper, a novel deep graph learning architecture based on attention mechanisms is proposed, which leverages the spatial-temporal graph to enhance robot navigation. We employ spatial graphs to capture the current spatial interactions, and through the integration with RNN, the temporal graphs utilize past trajectory information to infer the future intentions of each agent. The spatial-temporal graph reasoning ability allows the robot to better understand and interpret the relationships between agents over time and space, thereby making more informed decisions. Compared to previous state-of-the-art methods, our method demonstrates superior robustness in terms of safety, efficiency, and generalization in various challenging scenarios.

cs.RO↗