We present BabyBabelLM, a multilingual collection of datasets modeling the language a person observes from birth until they acquire a native language. We curate developmentally plausible pretraining data aiming to cover the equivalent of 100M English words of content in each of 45 languages. We compile evaluation suites and train baseline models in each language. BabyBabelLM aims to facilitate multilingual pretraining and cognitive modeling.

Multi-turn dialogues between a child and caregiver are characterized by a property called contingency – prompt, direct, and meaningful exchanges between interlocutors. We introduce ContingentChat, a Teacher–Student framework that benchmarks and improves multi-turn contingency in a BabyLM trained on 100M words. Using a novel alignment dataset for post-training, BabyLM generates responses that are more grammatical and cohesive. Experiments with adaptive Teacher decoding strategies show limited additional gains. ContingentChat highlights the positive benefits of targeted post-training on dialogue quality and presents contingency as a challenging goal for BabyLMs.

The performance and usability of Large-Language Models (LLMs) are driving their use in explanation generation tasks. However, despite their widespread adoption, LLM explanations have been found to be unreliable, making it difficult for users to distinguish good from bad explanations. To address this issue, we present Rubrik’s CUBE–an education-inspired rubric and a dataset of 26k explanations, written and later quality-annotated using the rubric by both humans and six open- and closed-source LLMs. The CUBE dataset focuses on two reasoning and two language tasks, providing the necessary diversity for us to effectively test our proposed rubric. Using Rubrik, we find that explanations are influenced by both task and perceived difficulty. Low quality stems primarily from a lack of conciseness in LLM-generated explanations, rather than cohesion and word choice. The full dataset, rubric, and code are available at https://github.com/RubriksCube/rubriks_cube.

Despite recent achievements in natural language understanding, reasoning over commonsense knowledge still represents a big challenge to AI systems. As the name suggests, common sense is related to perception and as such, humans derive it from experience rather than from literary education. Recent works in the NLP and the computer vision field have made the effort of making such knowledge explicit using written language and visual inputs, respectively. Our premise is that the latter source fits better with the characteristics of commonsense acquisition. In this work, we explore to what extent the descriptions of real-world scenes are sufficient to learn common sense about different daily situations, drawing upon visual information to answer script knowledge questions.

Reading comprehension (RC) through question answering is a useful method for evaluating if a reader understands a text. Standard accuracy metrics are used for evaluation, where high accuracy is taken as indicative of a good understanding. However, literature in quality learning suggests that task performance should also be evaluated on the undergone process to answer. The Question-Answer Relationship (QAR) is one of the strategies for evaluating a reader’s understanding based on their ability to select different sources of information depending on the question type. We propose the creation of a dataset to learn the QAR strategy with weak supervision. We expect to complement current work on reading comprehension by introducing a new setup for evaluation.