Committee

arXiv:2504.19406v2 Announce Type: replace Abstract: Educational question generation (EQG) is a crucial component of intelligent educational systems, significantly aiding self-assessment, active learning, and personalized education. While EQG systems have emerged, existing datasets typically rely on predefined, carefully edited texts, failing to represent real-world classroom content, including lecture speech with a set of complementary slides. To bridge this gap, we collect a dataset of educational questions based on lectures from real-world classrooms. On this realistic dataset, we find that current methods for EQG struggle with accurately generating questions from educational videos, particularly in aligning with specific timestamps and target answers. Common challenges include selecting informative contexts from extensive transcripts and ensuring generated questions meaningfully incorporate the target answer. To address the challenges, we introduce a novel framework utilizing large language models for dynamically selecting and rewriting contexts based on target timestamps and answers. First, our framework selects contexts from both lecture transcripts and video keyframes based on answer relevance and temporal proximity. Then, we integrate the contexts selected from both modalities and rewrite them into answer-containing knowledge statements, to enhance the logical connection between the contexts and the desired answer. This approach significantly improves the quality and relevance of the generated questions. Our dataset and code are released in https://github.com/mengxiayu/COSER.

arXiv:2504.20049v1 Announce Type: new Abstract: In recent years, large language models (LLMs) have demonstrated a high capacity for understanding and generating text in Spanish. However, with five hundred million native speakers, Spanish is not a homogeneous language but rather one rich in diatopic variations spanning both sides of the Atlantic. For this reason, in this study, we evaluate the ability of nine language models to identify and distinguish the morphosyntactic and lexical peculiarities of seven varieties of Spanish (Andean, Antillean, Continental Caribbean, Chilean, Peninsular, Mexican and Central American and Rioplatense) through a multiple-choice test. The results indicate that the Peninsular Spanish variety is the best identified by all models and that, among them, GPT-4o is the only model capable of recognizing the variability of the Spanish language. — En los ‘ultimos a~nos, los grandes modelos de lenguaje (LLMs, por sus siglas en ingl’es) han demostrado una alta capacidad para comprender y generar texto en espa~nol. Sin embargo, con quinientos millones de hablantes nativos, la espa~nola no es una lengua homog’enea, sino rica en variedades diat’opicas que se extienden a ambos lados del Atl’antico. Por todo ello, evaluamos en este trabajo la capacidad de nueve modelos de lenguaje de identificar y discernir las peculiaridades morfosint’acticas y l’exicas de siete variedades de espa~nol (andino, antillano, caribe~no continental, chileno, espa~nol peninsular, mexicano y centroamericano y rioplatense) mediante un test de respuesta m’ultiple. Los resultados obtenidos indican que la variedad de espa~nol peninsular es la mejor identificada por todos los modelos y que, de entre todos, GPT-4o es el ‘unico modelo capaz de identificar la variabilidad de la lengua espa~nola.

arXiv:2504.20094v1 Announce Type: cross Abstract: In this paper, we propose a multi-agent collaboration framework called MATCHA for conversational recommendation system, leveraging large language models (LLMs) to enhance personalization and user engagement. Users can request recommendations via free-form text and receive curated lists aligned with their interests, preferences, and constraints. Our system introduces specialized agents for intent analysis, candidate generation, ranking, re-ranking, explainability, and safeguards. These agents collaboratively improve recommendations accuracy, diversity, and safety. On eight metrics, our model achieves superior or comparable performance to the current state-of-the-art. Through comparisons with six baseline models, our approach addresses key challenges in conversational recommendation systems for game recommendations, including: (1) handling complex, user-specific requests, (2) enhancing personalization through multi-agent collaboration, (3) empirical evaluation and deployment, and (4) ensuring safe and trustworthy interactions.

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