Wang et al., 2024) is a widely used technique for customizing large language models (LLMs) to handle

This highlights the importance of selecting an appropriate schedule for each dataset.

Recently powerful Large Language Models (LLMs) have become easily accessible to hundreds of millions of users world-wide.

For example, in the Pandemic Response Challenge experiment, the context consisted of data about the geographic region for which the predictions were made, e.g., historical data of COVID-19 cases and intervention policies; actions were future schedules of intervention policies for the region; and outcomes were predicted future cases of COVID-19 along with the stringency

"Didn't know you were famous," the rapper Juliani, an old friend and musical collaborator, texted me from his studio in Nairobi. I didn't have a clue what he was referring to, but then he forwarded me the link to a tweet by Elon Musk that included a screenshot of a 2019 Al Jazeera column of mine, " Abolishing whiteness has never been more urgent ." The original post was circulating on Twitter/X, courtesy of a white nationalist poster who obviously wasn't too happy with the headline. Neither was Elon, who retweeted it with the comment, "It's not okay to say this about any group!" Although the post was only a few hours old, it already had five million views.

In this paper, we delve into the computation graph of the language model to uncover the knowledge circuits that are instrumental in articulating specific knowledge.

Instead of relying on the LLM itself to unlearn, we enforce an unlearned state during inference by employing a prompt classifier to identify and safeguard prompts to forget.