Papers by Florian Valentin Wunderlich

2 papers
BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages (2025.acl-long)

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Challenge: Emotion recognition is an umbrella term for several NLP tasks, but most work on high-resource languages has focused on low-resourced languages.
Approach: They propose to use emotion recognition to describe perceived emotions in 28 different languages and across several domains to identify and annotate the datasets.
Outcome: The proposed datasets cover low-resource languages from Africa, Asia, Eastern Europe, and Latin America, with instances labeled by fluent speakers.
Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling (2026.acl-srw)

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Challenge: Inference methods that prioritize raw performance over cost-effective compute usage are not efficient for real-world applications.
Approach: They evaluate inference scaling strategies to determine their computational efficiency tradeoffs . they find debate and mixture-of-agents outperform self-consistency by 1.3% and 2.7% points .
Outcome: The proposed scaling strategies outperform self-consistency, self-refinement, multi-agent debate and mixture-of-a agents on reasoning tasks.

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