4 accepted papers
Large Reasoning Models (LRMs) achieve promising results on complex reasoning tasks but remain susceptible to hallucinations. Existing hallucination detection methods based on Large Language Models (LLMs) often focus solely on final answers, overlooking inconsistencies between the answer and reasonin
Large language models (LLMs) have achieved significant advances but can potentially generate harmful content such as social biases, extremism, and misinformation. Red teaming is a promising approach to enhance model safety by creating adversarial prompts to test and improve model robustness. However…
Large language models (LLMs) have achieved groundbreaking progress in Natural Language Processing (NLP). Despite the numerous advantages of LLMs, they also pose significant safety risks. Self-evaluation mechanisms have gained increasing attention as a key safeguard to ensure safe and controllable co
In conventional core decompression of osteonecrosis, surgeons cannot successfully reach the whole area of the femoral head due to rigidity of the instruments currently used. To address this issue, we present design and fabrication of a novel steerable drill using a continuum dexterous manipulator (C