Y et, within the domain of video understanding, long video sequences pose a formidable challenge. Work done during an internship in Shanghai AI Laboratory.

Our findings reveal that even small collectives (controlling less than 0.01% of the training data) can achieve up to

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"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.

However, when faced with prompts in different sizes of solution spaces, L VLMs fail to always give consistent answers regarding the same knowledge point.

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.

One concerning capability of these models is their ability to alter small attributes of an original image.

Nevertheless, it is unclear how improved representation learning can benefit reinforcement learning from human feedback on language models.

Despite their strong performances on many generative tasks, diffusion models require a large number of sampling steps in order to generate realistic samples.