Knowledge editing has been increasingly adopted to correct the false or outdated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

Test-time interventions for language models can enhance factual accuracy, mitigate harmful
outputs, and improve model efficiency without costly retraining. But despite a flood of new …

The model editing problem concerns how language models should learn new facts about
the world over time. While empirical research on model editing has drawn widespread …

Recently, there has been a growing interest in knowledge editing for Large Language
Models (LLMs). Current approaches and evaluations merely explore the instance-level …

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

This paper investigates using knowledge editing techniques to detoxify Large Language
Models (LLMs). We construct a benchmark, SafeEdit, which covers nine unsafe categories …

Large Language Models (LLMs) often generate incorrect or outdated information, especially
in low-resource settings or when dealing with private data. To address this, Retrieval …

When the world changes, so does the text that humans write about it. How do we build
language models that can be easily updated to reflect these changes? One popular …

Despite their success at many natural language processing (NLP) tasks, large language
models (LLMs) still struggle to effectively leverage knowledge for knowledge-intensive tasks …

Large language models (LLMs) store extensive factual knowledge, but the mechanisms
behind how they store and express this knowledge remain unclear. The Knowledge Neuron …