![]() ![]() In space there are more components of the weapon that has the power to destroy the Earth and the evil Skullmaster. After you collect the pieces, jump into the warp and see how good you did on the level. There are things on the level to help you, like balloons and magnets. Find the pieces of the weapon and then take it to the warp so that you can send it back. The parts of the super weapon are all over this level. ![]() This game has a unique type of game play. You can play alone or you can use the head-to-head two-player feature. Mighty Max has to search the 50 cities that the pieces are scattered through. The evil Skullmaster, who disassembled the weapon, and hid it has sent his minions to stop Max because the weapon is the only thing that can destroy the Skullmaster. Results, indicating that the self-trained entailment models are more efficientĪnd trustworthy than large language models on language understanding tasks.Mighty Max is coming from the Saturday morning cartoon to your Super NES! Mighty Max is a futuristic 11-year-old superhero who, with the help of time travel, spans the globe on a quest to assemble the scattered pieces of a weapon that has the power to destroy the Earth. Experiments on binary and multi-classĬlassification tasks show that SimPLE leads to more robust self-training We also found that both pretrainedĮntailment-based models and the self-trained models are robust againstĪdversarial evaluation data. Pseudo-labeling quality in self-training. We propose the Simple Pseudo-Label Editing (SimPLE) algorithm for better ![]() Secondly, we notice that self-trainingĮntailment-based models with unlabeled data can significantly improve theĪdaptation performance on downstream tasks. This approach improves the zero-shot adaptation of In this work, weĭesign a prompting strategy that formulates a number of different NLU tasks asĬontextual entailment. Language understanding (NLU) models, and recent studies have found thatĮntailment pretraining benefits weakly supervised fine-tuning. Download a PDF of the paper titled Entailment as Robust Self-Learner, by Jiaxin Ge and 3 other authors Download PDF Abstract: Entailment has been recognized as an important metric for evaluating natural ![]()
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