Wethen comprehensively study detoxifying LMswithparameter sizesranging from126Mupto530B(3 largerthanGPT3), a scale that has never been studied before. We find thati) large LMs have similar toxicity levels as smaller ones given the same pre-training corpus, and ii) large LMs require more endeavor to unlearn the toxic content seen at pretraining. Wealso explore parameter-efficient training methods fordetoxification.

However, autoregressive sampling generates tokens one at a time making it slow, and even prohibitive in certain tasks.

The emergence of large language models (LLMs) has revolutionized the way we interact with graphs, leading to a new paradigm called GraphLLM.

Using a single projection vector, we then project these individual sub-tokens onto a one-dimensional subspace. Importantly, we notice that we can initialize this projection vector cheaply using first-order batch statistics andthen keepitfixedthroughout training. Wethen reconstruct the original tokens using the same vector during the backward pass.

Existing approaches to addressing shifts, such as concept drift adaptation, are limited by theirreason-agnosticnature.

We propose the new task'open-world video instance segmentation and captioning'.

In this supplementary material, Section 1 first presents the additional experimental analysis of our HQ-SAM, including more zero-shot transfer comparisons to SAM on both image and video benchmarks. SAM vs. HQ-SAM on V arious Backbones In Table 1, we provide a comprehensive comparison Table 2: Results on Y ouTubeVIS 2019 validation set and HQ-YTVIS test set using ViT -L based SAM. In Table 2, HQ-SAM achieves consistent gains of 1.4 points in Tube Mask AP, Robustness to Input Box Prompts In Table 4, we compare HQ-SAM to SAM by adding various scales of noises to the input ground truth box prompts. "center" point of Ground Truth (GT) masks, which is at a maximal value location in a mask's interior Results not obtained in a zero-shot manner (i.e. the training HQ-SAM improves over SAM, but still cannot achieve fully correct mask prediction. HQ-SAM produces significantly more accurate boundaries.