3 papers
cs.AI2025
CitySeeker: How Do VLMS Explore Embodied Urban Navigation With Implicit Human Needs?
Siqi Wang, Chao Liang, Yunfan Gao +5
Vision-Language Models (VLMs) have made significant progress in explicit instruction-based navigation; however, their ability to interpret implicit human needs (e.g., "I am thirsty…
cs.CL2025
OASIS: Order-Augmented Strategy for Improved Code Search
Zuchen Gao, Zizheng Zhan, Xianming Li +6
Code embeddings capture the semantic representations of code and are crucial for various code-related large language model (LLM) applications, such as code search. Previous trainin…
cs.CL2024
CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue Coreference
Erxin Yu, Jing Li, Ming Liao +4
As large language models (LLMs) constantly evolve, ensuring their safety remains a critical research problem. Previous red-teaming approaches for LLM safety have primarily focused…