Deep neural networks (DNNs) struggle at systematic generalization (SG). Several studies have evaluated the possibility of promoting SG through the proposal of novel architectures, loss functions, or training methodologies. Few studies, however, have focused on the role of training data properties in promoting SG. In this work, we investigate the impact of certain data distributional properties, as inductive biases for the SG ability of a multi-modal language model. To this end, we study three different properties. First, data diversity, instantiated as an increase in the possible values a latent property in the training distribution may take. Second, burstiness, where we probabilistically restrict the number of possible values of latent factors on particular inputs during training. Third, latent intervention, where a particular latent factor is altered randomly during training. We find that all three factors significantly enhance SG, with diversity contributing an 89% absolute increase in accuracy in the most affected property. Through a series of experiments, we test various hypotheses to understand why these properties promote SG. Finally, we find that Normalized Mutual Information (NMI) between latent attributes in the training distribution is strongly predictive of out-of-distribution generalization. We find that a mechanism by which lower NMI induces SG is in the geometry of representations. In particular, we find that NMI induces more parallelism in neural representations (i.e., input features coded in parallel neural vectors) of the model, a property related to the capacity of reasoning by analogy.

Fine-tuning foundation models is a key step in adapting them to a particular task. In the case of Geospatial Foundation Models (GFMs), fine-tuning can be particularly challenging given data scarcity both in terms of the amount of labeled data and, in the case of Satellite Image Time Series (SITS), temporal context. Under these circumstances, the optimal GFM fine-tuning strategy across different labeled data regimes remains poorly understood. In this paper, we thoroughly assess and study the performances of two different GFMs given several combinations of two data scarcity factors: the number of labeled samples and the sequence length. Specifically, we analyze the performances on a crop classification task, particularly, semantic segmentation of the Sentinel-2 images contained in the PASTIS-HD dataset. We compare GFMs to U-TAE, as a fully supervised baseline, across varying amounts of labeled data (1%, 10%, 50%, 100%) and temporal input lengths (1, 6, 15, 25 and 35). Among these explorations, we find that using a smaller learning rate for the pre-trained encoders improves performance in moderate and high data regimes (50%-100%). In contrast, full fine-tuning outperforms partial fine-tuning in very low-label settings (1%-10%). This behavior suggests a nuanced trade-off between feature reuse and adaptation that defies the intuition of standard transfer learning.

Automatic Short Answer Grading (ASAG) refers to automated scoring of open-ended textual responses to specific questions, both in natural language form. In this paper, we propose a method to tackle this task in a setting where annotated data is unavailable. Crucially, our method is competitive with the state-of-theart while being lighter and interpretable. We crafted a unique dataset containing a highly diverse set of questions and a small amount of answers to these questions; making it more challenging compared to previous tasks. Our method uses weak labels generated from other methods proven to be effective in this task, which are then used to train a white-box (linear) regression based on a few interpretable features. The latter are extracted expert features and learned representations that are interpretable per se and aligned with manual labeling. We show the potential of our method by evaluating it on a small annotated portion of the dataset, and demonstrate that its ability compares with that of strong baselines and state-of-the-art methods, comprising an LLM that in contrast to our method comes with a high computational price and an opaque reasoning process. We further validate our model on a public Automatic Essay Scoring dataset in English, and obtained competitive results compared to other unsupervised baselines, outperforming the LLM. To gain further insights of our method, we conducted an interpretability analysis revealing sparse weights in our linear regression model, and alignment between our features and human ratings.