Language features are evolving in real-world social media, resulting in the deteriorating performance of text classification in dynamics. To address this challenge, we study temporal adaptation, where models trained on past data are tested in the future. Most prior work focused on continued pretraining or knowledge updating, which may compromise their performance on noisy social media data. To tackle this issue, we reflect feature change via modeling latent topic evolution and propose a novel model, VIBE: Variational Information Bottleneck for Evolutions. Concretely, we first employ two Information Bottleneck (IB) regularizers to distinguish past and future topics. Then, the distinguished topics work as adaptive features via multi-task training with timestamp and class label prediction. In adaptive learning, VIBE utilizes retrieved unlabeled data from online streams created posterior to training data time. Substantial Twitter experiments on three classification tasks show that our model, with only 3% of data, significantly outperforms previous state-of-the-art continued-pretraining methods.

Language is constantly changing and evolving, leaving language models to become quickly outdated. Consequently, we should continuously update our models with new data to expose them to new events and facts. However, that requires additional computing, which means new carbon emissions. Do any measurable benefits justify this cost? This paper looks for empirical evidence to support continuous training. We reproduce existing benchmarks and extend them to include additional time periods, models, and tasks. Our results show that the downstream task performance of temporally adapted English models for social media data do not improve over time. Pretrained models without temporal adaptation are actually significantly more effective and efficient. However, we also note a lack of suitable temporal benchmarks. Our findings invite a critical reflection on when and how to temporally adapt language models, accounting for sustainability.

Many auditory theorists consider the temporal adaptation of the auditory nerve a key aspect of speech coding in the auditory periph(cid:173) ery. Experiments with models of auditory localization and pitch perception also suggest temporal adaptation is an important ele(cid:173) ment of practical auditory processing. I have designed, fabricated, and successfully tested an analog integrated circuit that models many aspects of auditory nerve response, including temporal adap(cid:173) tation.

Modelling and understanding time remains a challenge in contemporary video understanding models. With language emerging as a key driver towards powerful generalization, it is imperative for foundational video-language models to have a sense of time. In this paper, we consider a specific aspect of temporal understanding: consistency of time order as elicited by before/after relations. We establish that seven existing video-language models struggle to understand even such simple temporal relations. We then question whether it is feasible to equip these foundational models with temporal awareness without re-training them from scratch. Towards this, we propose a temporal adaptation recipe on top of one such model, VideoCLIP, based on post-pretraining on a small amount of video-text data. We conduct a zero-shot evaluation of the adapted models on six datasets for three downstream tasks which require varying degrees of time awareness. We observe encouraging performance gains especially when the task needs higher time awareness. Our work serves as a first step towards probing and instilling a sense of time in existing video-language models without the need for data and compute-intense training from scratch.

Language use changes over time, and this impacts the effectiveness of NLP systems. This phenomenon is even more prevalent in social media data during crisis events where meaning and frequency of word usage may change over the course of days. Contextual language models fail to adapt temporally, emphasizing the need for temporal adaptation in models which need to be deployed over an extended period of time. While existing approaches consider data spanning large periods of time (from years to decades), shorter time spans are critical for crisis data. We quantify temporal degradation for this scenario and propose methods to cope with performance loss by leveraging techniques from domain adaptation. To the best of our knowledge, this is the first effort to explore effects of rapid language change driven by adversarial adaptations, particularly during natural and human-induced disasters. Through extensive experimentation on diverse crisis datasets, we analyze under what conditions our approaches outperform strong baselines while highlighting the current limitations of temporal adaptation methods in scenarios where access to unlabeled data is scarce.

Many auditory theorists consider the temporal adaptation of the auditory nerve a key aspect of speech coding in the auditory periphery. Experiments with models of auditory localization and pitch perception also suggest temporal adaptation is an important element of practical auditory processing. I have designed, fabricated, and successfully tested an analog integrated circuit that models many aspects of auditory nerve response, including temporal adaptation.

Many auditory theorists consider the temporal adaptation of the auditory nerve a key aspect of speech coding in the auditory periphery. Experimentswith models of auditory localization and pitch perception also suggest temporal adaptation is an important element ofpractical auditory processing. I have designed, fabricated, and successfully tested an analog integrated circuit that models many aspects of auditory nerve response, including temporal adaptation. 1. INTRODUCTION We are modeling known and proposed auditory structures in the brain using analog VLSI circuits, with the goal of making contributions both to engineering practice andbiological understanding. Computational neuroscience involves modeling biology at many levels of abstraction. The first silicon auditory models were constructed ata fairly high level of abstraction (Lyon and Mead, 1988; Lazzaro and Mead, 1989ab; Mead et al., 1991; Lyon, 1991). The functional limitations of these silicon systems have prompted a new generation of auditory neural circuits designed at a lower level of abstraction (Watts et al., 1991; Liu et -al., 1991).