arXiv subjects

Pierre Hanna

Publications and source records attributed to Pierre Hanna.

4 recordsLinked to original sources

arXiv2026

Stability and Political Orientation of International LLMs: An Exploratory Multi-Run Study Conducted in French

This study explores the stability and ideological orientation of political responses produced by various large language models (LLMs) in French. We designed a standardised experimental protocol based on a questionnaire inspired by the Political Compass, aimed at measuring the economic and socio-cultural positions of each model across 62 political statements. Eleven models from various organisations and countries were tested, each subjected to twenty independent runs to assess intra-model variability. The analysis focuses on response consistency, inter-model differences, and the presence of implicit political orientations. The results show that, despite a general degree of stability, significant variations appear from one run to the next and between models, reflecting the impact of architectures, training data, and moderation mechanisms. This study proposes a comparative evaluation protocol for LLMs in the context of political information and underscores the importance of accounting for implicit biases in the use of these systems.

cs.CY↗

arXiv2017

Toward Faultless Content-Based Playlists Generation for Instrumentals

This study deals with content-based musical playlists generation focused on Songs and Instrumentals. Automatic playlist generation relies on collaborative filtering and autotagging algorithms. Autotagging can solve the cold start issue and popularity bias that are critical in music recommender systems. However, autotagging remains to be improved and cannot generate satisfying music playlists. In this paper, we suggest improvements toward better autotagging-generated playlists compared to state-of-the-art. To assess our method, we focus on the Song and Instrumental tags. Song and Instrumental are two objective and opposite tags that are under-studied compared to genres or moods, which are subjective and multi-modal tags. In this paper, we consider an industrial real-world musical database that is unevenly distributed between Songs and Instrumentals and bigger than databases used in previous studies. We set up three incremental experiments to enhance automatic playlist generation. Our suggested approach generates an Instrumental playlist with up to three times less false positives than cutting edge methods. Moreover, we provide a design of experiment framework to foster research on Songs and Instrumentals. We give insight on how to improve further the quality of generated playlists and to extend our methods to other musical tags. Furthermore, we provide the source code to guarantee reproducible research.

cs.SD↗

arXiv2017

Considering Durations and Replays to Improve Music Recommender Systems

The consumption of music has its specificities in comparison with other media, especially in relation to listening durations and replays. Music recommendation can take these properties into account in order to predict the behaviours of the users. Their impact is investigated in this paper. A large database was thus created using logs collected on a streaming platform, notably collecting the listening times. The proposed study shows that a high proportion of the listening events implies a skip action, which may indicate that the user did not appreciate the track listened. Implicit like and dislike can be deduced from this information of durations and replays and can be taken into account for music recommendation and for the evaluation of music recommendation engines. A quantitative study as usually found in the literature confirms that neighborhood-based systems considering binary data give the best results in terms of MAP@k. However, a more qualitative evaluation of the recommended tracks shows that many tracks recommended, usually evaluated in a positive way, lead to skips or thus are actually not appreciated. We propose the consideration of implicit like/dislike as recommendation engine inputs. Evaluations show that neighbourhood-based engines remain the most precise, but filtering inputs according to durations and/or replays have a significant positive impact on the objective of the recommendation engine. The recommendation process can thus be improved by taking account of listening durations and replays. We also study the possibility of post-filtering a list of recommended tracks so as to limit the number of tracks that will be unpleasantly listened (skip and implicit dislike) and to increase the proportion of tracks appreciated (implicit like). Several simple algorithms show that this post-filtering operation leads to an improvement of the quality of the music recommendations.

cs.IR↗

arXiv2017

Influence des mécanismes dissociés de ludifications sur l'apprentissage en support numérique de la lecture en classe primaire

The introduction of serious games as pedagogical supports in the field of education is a process gaining in popularity amongst the teaching community. This article creates a link between the integration of new pedagogical solutions in first-year primary class and the fundamental research on the motivation of the players/learners, detailing an experiment based on a game specifically developed, named QCM. QCM considers the learning worksheets issued from the Freinet pedagogy using various gameplay mechanisms. The main contribution of QCM in relation to more traditional games is the dissociation of immersion mechanisms, in order to improve the understanding of the user experience. This game also contains a system of gameplay metrics, the analysis of which shows a relative increase in the motivation of students using QCM instead of paper worksheets, while revealing large differences in students behavior in conjunction with the mechanisms of gamification employed. Keywords : Serious games, learning analytics, gamification, flow.

cs.CY↗