Julie Skoven Hinge | Searcharxiv

arXiv2026

How to Value Open Source Contributions? An Institutional Perspective from CERN

We present a methodology to systematically assess the scale and impact of an organization's contributions to open source software (OSS). The methodology combines the archival data of Software Heritage with usage metrics, dependency analysis, economic valuation models, and interviews to comprehensively understand institutional OSS involvement. We then apply the methodology to the European Organisation for Nuclear Physics (CERN). Despite using mostly commit data, we obtain a thorough overview of CERN's OSS engagement. We identify over six million commits made to over 50,000 projects and highlight the most impactful projects led by CERN. Beyond CERN, the methodology offers a reusable framework for organizations seeking to measure and evaluate their OSS contributions.

cs.SE↗

arXiv2025

Don't Get Too Excited -- Eliciting Emotions in LLMs

This paper investigates the challenges of affect control in large language models (LLMs), focusing on their ability to express appropriate emotional states during extended dialogues. We evaluated state-of-the-art open-weight LLMs to assess their affective expressive range in terms of arousal and valence. Our study employs a novel methodology combining LLM-based sentiment analysis with multiturn dialogue simulations between LLMs. We quantify the models' capacity to express a wide spectrum of emotions and how they fluctuate during interactions. Our findings reveal significant variations among LLMs in their ability to maintain consistent affect, with some models demonstrating more stable emotional trajectories than others. Furthermore, we identify key challenges in affect control, including difficulties in producing and maintaining extreme emotional states and limitations in adapting affect to changing conversational contexts. These findings have important implications for the development of more emotionally intelligent AI systems and highlight the need for improved affect modelling in LLMs.

cs.AI↗