Outcome prediction with a social cognitive battery: a multicenter longitudinal study

The interest in social cognition in schizophrenia is justified by the relationship between deficits in these skills and negative functional outcomes. Although assessment batteries have already been described, there is no consensus about which measures are useful in predicting patient functioning or quality of life (QoL). We investigated a set of five measures of recognition of facial emotions, theory of mind (ToM), and empathy in a cohort of 143 patients with schizophrenia or schizoaffective disorder at inclusion and, amongst whom 79 were reassessed 1 year later. The distribution was satisfactory for the TREF (Facial Emotion Recognition Task), V-SIR (Versailles-Situational Intention Reading), and QCAE (Questionnaire of Cognitive and Affective Empathy). Internal consistency was satisfactory for the TREF, V-SIR, V-Comics (Versailles Intention Attribution Task), and QCAE. Sensitivity to change was acceptable for the TREF. The TREF and V-SIR showed a cross-sectional relationship with functioning beyond the clinical symptoms of schizophrenia but not beyond neurocognition. Moreover, the TREF and V-SIR at inclusion could not predict functioning one year later, whereas most neurocognitive and clinical dimensions at inclusion could. Finally, only affective QCAE showed a significant cross-sectional, but not longitudinal, association with QoL. In conclusion, the TREF had satisfactory psychometric properties and showed a cross-sectional, but not longitudinal, association with objective outcome measures, thus appearing to be reliable in clinical practice and research. The V-SIR also showed promising psychometric properties, despite a possible weakness to detect change. However, these measures should be interpreted within the context of the good predictive power of the neurocognitive and clinical status on the outcome.Sorbonne Universités à Paris pour l'Enseignement et la RechercheFondaMental-Cohorte

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Feelings generated by threat appeals in social marketing: text and emoji analysis of user reactions to anorexia nervosa campaigns in social media

Threat appeals in social marketing have been widely researched regarding their effects in behaviour change. However, little is known about their emotional effects in individuals. Feelings generated by threat appeals have proved to be ambiguous. Considering that understanding the emotional effects of message frames has implications in long-term behaviour change, this paper aims at understanding the feelings generated by threat appeals, considering the inconsistent findings in the literature. The research analyses the feelings produced by threat appeals in two social networks - Facebook and YouTube. A sentiment analysis of forty non-governmental campaigns regarding anorexia nervosa awareness was conducted through two methodological forms. First, we have analysed the content of the comments made by users by text analysis; second, we have coded the emoji expressing feelings from the users in the same campaigns and have quantified their interactions. Results indicate that feelings generated by threat appeals regarding anorexia nervosa campaigns in social media may be both positive and negative, with a great expression of fear, sadness and empathy, corroborating the ambiguous findings. Positive feelings are most prominent in emoji and reveal support, compassion and admiration both for campaign messages and for people suffering from anorexia. Negative feelings, such as fear and sadness, arise especially as a consequence of awareness and concerns. The paper contributes to the discussion of this ambivalent topic of research and also experiments two different sentiment analysis techniques – text and emoji analysis -, with different result outcomes.- (undefined

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License