Shourya Jain | Searcharxiv

arXiv2026

Language Models Entangle Language and Culture

Users should not be systemically disadvantaged by the language they use for interacting with LLMs; i.e. users across languages should get responses of similar quality irrespective of language used. In this work, we create a set of real-world open-ended questions based on our analysis of the WildChat dataset and use it to evaluate whether responses vary by language, specifically, whether answer quality depends on the language used to query the model. We also investigate how language and culture are entangled in LLMs such that choice of language changes the cultural information and context used in the response by using LLM-as-a-Judge to identify the cultural context present in responses. To further investigate this, we evaluate LLMs on a translated subset of the CulturalBench benchmark across multiple languages. Our evaluations reveal that LLMs consistently provide lower quality answers to open-ended questions in low resource languages. We find that language significantly impacts the cultural context used by the model. This difference in context impacts the quality of the downstream answer.

cs.LG↗

arXiv2025

ART: Adaptive Resampling-based Training for Imbalanced Classification

Traditional resampling methods for handling class imbalance typically uses fixed distributions, undersampling the majority or oversampling the minority. These static strategies ignore changes in class-wise learning difficulty, which can limit the overall performance of the model. This paper proposes an Adaptive Resampling-based Training (ART) method that periodically updates the distribution of the training data based on the class-wise performance of the model. Specifically, ART uses class-wise macro F1 scores, computed at fixed intervals, to determine the degree of resampling to be performed. Unlike instance-level difficulty modeling, which is noisy and outlier-sensitive, ART adapts at the class level. This allows the model to incrementally shift its attention towards underperforming classes in a way that better aligns with the optimization objective. Results on diverse benchmarks, including Pima Indians Diabetes and Yeast dataset demonstrate that ART consistently outperforms both resampling-based and algorithm-level methods, including Synthetic Minority Oversampling Technique (SMOTE), NearMiss Undersampling, and Cost-sensitive Learning on binary as well as multi-class classification tasks with varying degrees of imbalance. In most settings, these improvements are statistically significant. On tabular datasets, gains are significant under paired t-tests and Wilcoxon tests (p < 0.05), while results on text and image tasks remain favorable. Compared to training on the original imbalanced data, ART improves macro F1 by an average of 2.64 percentage points across all tested tabular datasets. Unlike existing methods, whose performance varies by task, ART consistently delivers the strongest macro F1, making it a reliable choice for imbalanced classification.

cs.LG↗