Knowledge editing has been increasingly adopted to correct the false or outdated
knowledge in Large Language Models (LLMs). Meanwhile, one critical but under-explored …

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

Retrieval-Augmented Generation (RAG) offers an effective solution to the issues faced by
Large Language Models (LLMs) in hallucination generation and knowledge obsolescence …

As large language models (LLMs) are rapidly advancing and achieving near-human
capabilities, aligning them with human values is becoming more urgent. In scenarios where …

In recent years, there has been a growing recognition that large language models like GPT-
4 have the capability to store vast amounts of knowledge and possess extremely powerful …

Large language models (LLMs) have significantly impacted many aspects of our lives.
However, assessing and ensuring their chronological knowledge remains challenging …

Large language models (LLMs) have learned vast amounts of factual knowledge through
self-supervised pre-training on large-scale corpora. Meanwhile, LLMs have also …

Existing domain-specific Large Language Models (LLMs) are typically developed by fine-
tuning general-purposed LLMs with large-scale domain-specific corpora. However, training …

Large language models (LLMs) have become increasingly proficient at simulating various
personality traits, an important capability for supporting related applications (eg, role …

The GPT-4 technical report from OpenAI suggests that model performance on specific tasks
can be predicted prior to training, though methodologies remain unspecified. This approach …