Eight LLMs show measurably different emotional tones in mental-health answers: anxiety prompts produced near-saturated fear scores, depression prompts the most sadness, and stress prompts the most optimism.
SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research
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abstract
Despite its relevance, the maturity of NLP for social media pales in comparison with general-purpose models, metrics and benchmarks. This fragmented landscape makes it hard for the community to know, for instance, given a task, which is the best performing model and how it compares with others. To alleviate this issue, we introduce a unified benchmark for NLP evaluation in social media, SuperTweetEval, which includes a heterogeneous set of tasks and datasets combined, adapted and constructed from scratch. We benchmarked the performance of a wide range of models on SuperTweetEval and our results suggest that, despite the recent advances in language modelling, social media remains challenging.
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AI in Mental Health: Emotional and Sentiment Analysis of Large Language Models' Responses to Depression, Anxiety, and Stress Queries
Eight LLMs show measurably different emotional tones in mental-health answers: anxiety prompts produced near-saturated fear scores, depression prompts the most sadness, and stress prompts the most optimism.