Detecting out-of-distribution (OOD) samples is crucial for robust NLP models. Recent works observe two OOD types: background shifts (style change) and semantic shifts (content change), but existing detection methods vary in effectiveness for each type. To this end, we propose Meta-Crafting, a unified OOD detection method by constructing a new discriminative feature space utilizing 7 model-driven metadata chosen empirically that well detects both types of shifts. Our experimental results demonstrate state-of-the-art robustness to both shifts and significantly improved detection on stress datasets.

Textual style expresses a diverse set of information, including interpersonal dynamics (e.g., formality) and the author's emotions or attitudes (e.g., disgust).An open question is how language models can be explicitly controlled so that they weave together target styles when generating text: for example, to produce text that is both negative and non-toxic.One approach to such controlled generation is multiobjective reinforcement learning (RL), but how to best combine multiple objectives in a reward function is an open question.In this paper, we investigate various formulations of multi-style rewards, including calibrated outputs from discriminators and dynamic weighting by discriminator gradient magnitudes.We find that our proposed dynamic weighting outperforms static weighting approaches with respect style control while maintaining linguistic quality, and we explore its effectiveness in 2-and 3-style control.All code and data for the RL pipelines will be publicly available.1