This paper evaluates the language understanding capabilities of various large language models (LLMs) through an analysis of 112 translated and human-edited questions from the Multitask Language Understanding (MMLU) dataset, focusing specifically on two underrepresented languages: Latvian and Giriama. The study compares the performance of six state-of-the-art (SOTA) models, with OpenAI’s o1-preview model demonstrating superior performance across all languages, significantly outperforming non-proprietary models in Latvian and all other models in Giriama. Human editing of automated translations from English to Latvian yielded only a small, statistically insignificant improvement in performance estimates, suggesting that machine-translated benchmarks may be sufficient for comparing model performance in languages with established digital resources like Latvian. However, automated translation to Giriama proved infeasible, and model performance in Giriama remained poor, highlighting the persistent challenges LLMs face with low-resource languages. These findings underscore the need for more comprehensive datasets and improved machine translation capabilities for underrepresented languages, while emphasizing the importance of localized benchmarks and human evaluation in addressing cultural and contextual limitations in AI models.

A personification is a figure of speech that endows inanimate entities with properties and actions typically seen as requiring animacy. In this paper, we explore the task of personification generation. To this end, we propose PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification data for Learning Enhanced generation. We curate a corpus of personifications called PersonifCorp, together with automatically generated de-personified literalizations of these personifications. We demonstrate the usefulness of this parallel corpus by training a seq2seq model to personify a given literal input. Both automatic and human evaluations show that fine-tuning with PersonifCorp leads to significant gains in personification-related qualities such as animacy and interestingness. A detailed qualitative analysis also highlights key strengths and imperfections of PINEAPPLE over baselines, demonstrating a strong ability to generate diverse and creative personifications that enhance the overall appeal of a sentence.

Illicit activity on the Web often uses noisy text to obscure information between client and seller, such as the seller’s phone number. This presents an interesting challenge to language understanding systems; how do we model adversarial noise in a text extraction system? This paper addresses the sex trafficking domain, and proposes some of the first neural network architectures to learn and extract phone numbers from noisy text. We create a new adversarial advertisement dataset, propose several RNN-based models to solve the problem, and most notably propose a visual character language model to interpret unseen unicode characters. We train a CRF jointly with a CNN to improve number recognition by 89% over just a CRF. Through data augmentation in this unique model, we present the first results on characters never seen in training.