Current multilingual benchmarks cover only a fraction of the languages represented in modern pretraining corpora and often conflate language proficiency with the ability to answer questions grounded in specific regions and communities. We introduce INCLUDE-2.0, a large-scale benchmark for region-aware multilingual factual evaluation, which covers 91 languages from 86 countries (127 total language–country pairs), comprising over 220K curated samples drawn from native regional sources. Unlike existing benchmarks that often treat language as a proxy for region, IINCLUDE-2.0 distinguishes between questions whose answers are independent of context, vary by country, concern widely documented facts about a place, or depend on knowledge primarily expressed through local linguistic and cultural sources. Each item is annotated with its regional knowledge type and domain, and paired with an English translation, enabling controlled analysis of the relationship between language proficiency and possession of regional knowledge.

Evaluating frontier and open-weight models on IINCLUDE-2.0, we find that aggregate multilingual accuracy masks substantial variation across regional knowledge types, languages, and regions. We find that frontier models perform worse on locally embedded knowledge than on widely documented facts about regions, with an average drop of ~14.5 points. We also show that for open-weight models, scale improves accuracy but does not reliably make performance invariant to whether a question is asked in English or its original language. INCLUDE-2.0 is available at https://huggingface.co/datasets/epfl-nlp/include-2.0

The evaluation of LLMs has so far focused primarily on how well they can perform different tasks such as reasoning, question-answering, paraphrasing, or translating. For most of these tasks, performance can be measured with objective metrics, such as the number of correct answers. However, other language features are not easily quantified. For example, arousal, concreteness, or gender associated with a given word, as well as the extent to which we experience words with senses and relate them to a specific sense. Those features have been studied for many years by psycholinguistics, conducting large-scale experiments with humans to produce ratings for thousands of words. This opens an opportunity to evaluate how well LLMs align with human ratings on these word features, taking advantage of existing studies that cover many different language features in a large number of words. In this paper, we evaluate the alignment of a representative group of LLMs with human ratings on two psycholinguistic datasets: the Glasgow and Lancaster norms. These datasets cover thirteen features over thousands of words. The results show that alignment is significantly better on the Glasgow norms evaluated (arousal, valence, dominance, concreteness, imageability, familiarity, and gender) than on the Lancaster norms evaluated (introceptive, gustatory, olfactory, haptic, auditory, and visual). This suggests a limitation of current LLMs in aligning with human sensory associations for words, which may be due to their lack of embodied cognition present in humans and illustrates the usefulness of evaluating LLMs with psycholinguistic datasets.

Leaderboards showcase the current capabilities and limitations of Large Language Models (LLMs). To motivate the development of LLMs that represent the linguistic and cultural diversity of the Spanish-speaking community, we present La Leaderboard, the first open-source leaderboard to evaluate generative LLMs in languages and language varieties of Spain and Latin America. La Leaderboard is a community-driven project that aims to establish an evaluation standard for everyone interested in developing LLMs for the Spanish-speaking community. This initial version combines 66 datasets in Catalan, Basque, Galician, and different Spanish varieties, showcasing the evaluation results of 50 models. To encourage community-driven development of leaderboards in other languages, we explain our methodology, including guidance on selecting the most suitable evaluation setup for each downstream task. In particular, we provide a rationale for using fewer few-shot examples than typically found in the literature, aiming to reduce environmental impact and facilitate access to reproducible results for a broader research community.