Text-based evaluations of Theory of Mind (ToM) in Large Language Models (LLMs) often involve cognitive tests akin to the Sally-Anne task that can be gamed due to exposure to relevantly similar tasks in pre-training and do not obviously test models’ functional ToM abilities in ways that generalize to naturalistic settings. To address these issues, we introduce the Epistemic (A)symmetry Schelling Task (EAST), a two-player dialogue game designed to benchmark robust and generalizable ToM abilities. By requiring LLM-LLM dyads to independently converge on semantic Schelling points under varying states of epistemic transparency, we evaluate whether models can robustly apply ToM to achieve coordination. Our results reveal a significant capability gap in functional social reasoning, with only frontier models successfully navigating the varying epistemic demands of the tasks. Analysis of reasoning traces shows that coordination failures are primarily driven by epistemic tracking errors, such as conflating private knowledge with mutual knowledge. Despite high performance on traditional static benchmarks, our study shows that robust social reasoning and epistemic tracking remain a critical bottleneck, providing concrete targets for future LLM evaluation and development.

Topic models are useful tools for discovering latent semantic structures in large textual corpora. Recent efforts have been oriented at incorporating contextual representations in topic modeling and have been shown to outperform classical topic models. These approaches are typically slow, volatile, and require heavy preprocessing for optimal results. We present Semantic Signal Separation (S³), a theory-driven topic modeling approach in neural embedding spaces. S³ conceptualizes topics as independent axes of semantic space and uncovers these by decomposing contextualized document embeddings using Independent Component Analysis. Our approach provides diverse and highly coherent topics, requires no preprocessing, and is demonstrated to be the fastest contextual topic model, being, on average, 4.5x faster than the runner-up BERTopic. We offer an implementation of S³, and all contextual baselines, in the Turftopic Python package.

The way we talk carries information about who we are. Demographics, personality, clinical conditions, political preferences influence what we speak about and how, suggesting that many individual attributes could be inferred from adequate encodings of linguistic behavior. Conversely, conditioning text representations on author attributes has been shown to improve model performance in many NLP tasks. Previous research on individual differences and language representations has mainly focused on predicting selected attributes from text, or on conditioning text representations on such attributes for author-based contextualization. Here, we present a self-supervised approach to learning language-based user encodings using transformers. Using a large corpus of Reddit submissions, we fine-tune DistilBERT on user-based triplet loss. We show that fine-tuned models can pick up on complex linguistic signatures of users, and that they are able to infer rich information about them. Through a series of intrinsic analyses and probing tasks, we provide evidence that fine-tuning enhances models’ ability to abstract generalizable user information, which yields performance advantages for user-based downstream tasks. We discuss applications in language-based assessment and contextualized and personalized NLP.