Large language models (LLMs) have demonstrated impressive generative capabilities with
the potential to innovate in medicine. However, the application of LLMs in real clinical …

Knowledge editing techniques have been increasingly adopted to efficiently correct the false
or outdated knowledge in Large Language Models (LLMs), due to the high cost of retraining …

As the modern tool of choice for question answering, large language models (LLMs) are
expected to deliver answers with up-to-date knowledge. To achieve such ideal question …

Exploring the application of large language models (LLMs) to graph learning is an emerging
endeavor. However, the vast amount of information inherent in large graphs poses …

Large Language Models (LLMs) suffer from hallucinations, referring to the non-factual
information in generated content, despite their superior capacities across tasks. Meanwhile …

As Large Language Models (LLMs) grow increasingly powerful, ensuring their safety and
alignment with human values remains a critical challenge. Ideally, LLMs should provide …

Large Language Models (LLMs) excel in fluency but risk producing inaccurate content,
called" hallucinations." This paper outlines a standardized process for categorizing fine …

The parametric knowledge memorized by large language models (LLMs) becomes outdated
quickly. In-context editing (ICE) is currently the most effective method for updating the …