Large language models (LLMs) often exhibit deficient reasoning or generate hallucinations.
To address these, studies prefixed with" Self-" such as Self-Consistency, Self-Improve, and …

The rise of Multimodal Large Language Models (MLLMs), renowned for their advanced
instruction-following and reasoning capabilities, has significantly propelled the field of visual …

Hallucination is a significant barrier to the utilization of large-scale language models or
multimodal models. In this study, we collected 654 computer science papers with" …

Superalignment, where humans act as weak supervisors for superhuman models, has
become a crucial problem with the rapid development of Large Language Models (LLMs) …

Self-consciousness, the introspection of one's existence and thoughts, represents a high-
level cognitive process. As language models advance at an unprecedented pace, a critical …

In Retrieval-Augmented Generation (RAG), retrieval is not always helpful and applying it to
every instruction is sub-optimal. Therefore, determining whether to retrieve is crucial for …

The generic large Vision-Language Models (VLMs) is rapidly developing, but still perform
poorly in Remote Sensing (RS) domain, which is due to the unique and specialized nature …

Refusal-Aware Instruction Tuning (RAIT) enables Large Language Models (LLMs) to refuse
to answer unknown questions. By modifying responses of unknown questions in the training …

Large language models (LLMs) have shown remarkable performance in various tasks but
often fail to handle queries that exceed their knowledge and capabilities, leading to incorrect …

Role-playing systems powered by large language models (LLMs) have become increasingly
influential in emotional communication applications. However, these systems are …