After completely excluding the temporal dimension from the model parameter space, we introduced the temporal conditioning operation to handle the temporal information. In particular, this operation enables memory-dependent processing as in biological coding circuits. Figure 6: Performances under di erent hidden state and latent space dimension settings on Movie 2 Retina 2 data. For hidden state experiments, the latent space dimension is set to 32. And for latent space experiments, the hidden state dimension is 64.

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Problem(7)isNP-complete. Weshow that there exists apolynomial time reduction from the set cover problem to(7). We construct theM matrix according to the sets A1,...,Am (thei-thcolumnof M isthenonzeropatternof Ai).

Whenlabelsaresemantically complex and multi-modal, it is more natural to assign a label to multiple semantic clusters. In product categorization, for instance, the tag "belt" can be related to a vehicle belt (under "vehicle accessories" category),oraman'sbelt(under "clothing" category).

However,aspointedoutin[8],themodel performance would deteriorate due to the information loss from label aggregation.

CascadeXML significantly outperforms allexisting approaches with non-trivial gains obtained on benchmark datasets consisting of up to three millionlabels.

Large Language Models (LLMs) are trained to refuse to respond to harmful content. However, systematic analyses of whether this behavior is truly a reflection of its safety policies or an indication of political censorship, that is practiced globally by countries, is lacking. Differentiating between safety influenced refusals or politically motivated censorship is hard and unclear. For this purpose we introduce PSP, a dataset built specifically to probe the refusal behaviors in LLMs from an explicitly political context. PSP is built by formatting existing censored content from two data sources, openly available on the internet: sensitive prompts in China generalized to multiple countries, and tweets that have been censored in various countries. We study: 1) impact of political sensitivity in seven LLMs through data-driven (making PSP implicit) and representation-level approaches (erasing the concept of politics); and, 2) vulnerability of models on PSP through prompt injection attacks (PIAs). Associating censorship with refusals on content with masked implicit intent, we find that most LLMs perform some form of censorship. We conclude with summarizing major attributes that can cause a shift in refusal distributions across models and contexts of different countries.