Large Language Models (LLMs) have demonstrated impressive performance in various tasks, including In-Context Learning (ICL), where the model performs new tasks by conditioning solely on the examples provided in the context, without updating the model's weights. While prior research has explored the roles of pretraining data and model architecture, the key mechanism behind ICL remains unclear. In this work, we systematically uncover properties present in LLMs that support the emergence of ICL. To disambiguate these factors, we conduct a study with a controlled dataset and data sequences using a deep autoregressive model. We show that conceptual repetitions in the data sequences are crucial for ICL, more so than previously indicated training data properties like burstiness or long-tail distribution. Conceptual repetitions could refer to $n$-gram repetitions in textual data or exact image copies in image sequence data. Such repetitions also offer other previously overlooked benefits such as reduced transiency in ICL performance. Furthermore, we show that the emergence of ICL depends on balancing the in-weight learning objective with the in-context solving ability during training.

Vision-language modeling has enabled open-vocabulary tasks where predictions can be queried using any text prompt in a zero-shot manner. Existing open-vocabulary tasks focus on object classes, whereas research on object attributes is limited due to the lack of a reliable attribute-focused evaluation benchmark. This paper introduces the Open-Vocabulary Attribute Detection (OVAD) task and the corresponding OVAD benchmark. The objective of the novel task and benchmark is to probe object-level attribute information learned by vision-language models. To this end, we created a clean and densely annotated test set covering 117 attribute classes on the 80 object classes of MS COCO. It includes positive and negative annotations, which enables open-vocabulary evaluation. Overall, the benchmark consists of 1.4 million annotations. For reference, we provide a first baseline method for open-vocabulary attribute detection. Moreover, we demonstrate the benchmark's value by studying the attribute detection performance of several foundation models. Project page https://ovad-benchmark.github.io

Emotion recognition is an important field of research in Brain Computer Interactions. As technology and the understanding of emotions are advancing, there are growing opportunities for automatic emotion recognition systems. Neural networks are a family of statistical learning models inspired by biological neural networks and are used to estimate functions that can depend on a large number of inputs that are generally unknown. In this paper we seek to use this effectiveness of Neural Networks to classify user emotions using EEG signals from the DEAP (Koelstra et al (2012)) dataset which represents the benchmark for Emotion classification research. We explore 2 different Neural Models, a simple Deep Neural Network and a Convolutional Neural Network for classification. Our model provides the state-of-the-art classification accuracy, obtaining 4.51 and 4.96 percentage point improvements over (Rozgic et al (2013)) classification of Valence and Arousal into 2 classes (High and Low) and 13.39 and 6.58 percentage point improvements over (Chung and Yoon(2012)) classification of Valence and Arousal into 3 classes (High, Normal and Low). Moreover our research is a testament that Neural Networks could be robust classifiers for brain signals, even outperforming traditional learning techniques.