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.

This paper describes the participation of the SINAI research group at WASSA 2022 (Empathy and Personality Detection and Emotion Classification). Specifically, we participate in Track 1 (Empathy and Distress predictions) and Track 2 (Emotion classification). We conducted extensive experiments developing different machine learning solutions in line with the state of the art in Natural Language Processing. For Track 1, a Transformer multi-output regression model is proposed. For Track 2, we aim to explore recent techniques based on Zero-Shot Learning models including a Natural Language Inference model and GPT-3, using them in an ensemble manner with a fine-tune RoBERTa model. Our team ranked 2nd in the first track and 3rd in the second track.