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UKP_Psycontrol at SemEval-2026 Task 2: Modeling Valence and Arousal Dynamics from Text

arXiv 2604.21534 ↗First posted 23 Apr 2026cs.CLcited by 0

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Abstract (arXiv)

This paper presents our system developed for SemEval-2026 Task 2. The task requires modeling both current affect and short-term affective change in chronologically ordered user-generated texts. We explore three complementary approaches: (1) LLM prompting under user-aware and user-agnostic settings, (2) a pairwise Maximum Entropy (MaxEnt) model with Ising-style interactions for structured transition modeling, and (3) a lightweight neural regression model incorporating recent affective trajectories and trainable user embeddings. Our findings indicate that LLMs effectively capture static affective signals from text, whereas short-term affective variation in this dataset is more strongly explained by recent numeric state trajectories than by textual semantics. Our system ranked first among participating teams in both Subtask 1 and Subtask 2A based on the official evaluation metric.

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References · 9

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  6. 2024Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspectivecited by 25
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  8. 2023Large Language Models are Miscalibrated In-Context Learnerscited by 12
  9. 2025Towards Objective Fine-tuning: How LLMs' Prior Knowledge Causes Potential Poor Calibration?cited by 11

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