arXiv AI
Jun 26
Where Do Models Find Happiness? Emotion Vectors in Open-Source LLMs
arXiv:2606. 26987v1 Announce Type: cross Abstract: Recent work identified emotion vectors in Claude Sonnet 4.
VIBE: A VAD-Informed Benchmark for Entity-Centered Affective Profiling of Large Language Model Outputs
arXiv:2608. 03810v1 Announce Type: cross Abstract: Large language models routinely describe socially salient targets, including political figures, countries, religions, organizations, historical events, and social groups, encoding affective framing alongside factual content: a target may appear favorable or threatening, calm or conflictual, powerful or vulnerable.
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arXiv AI
Jun 11
Causal Emotion Recognition in Conversation: Context Saturation and Discourse-Marker Evidence
arXiv:2601. 00181v3 Announce Type: replace-cross Abstract: We address two persistent gaps in Emotion Recognition in Conversation: which modeling choices materially affect performance, and how recognition findings connect to interpretable discourse-level patterns.
arXiv AI
Jul 21
Posts of Peril: Detecting Information About Hazards in Text
arXiv:2405. 17838v3 Announce Type: replace-cross Abstract: Socio-linguistic indicators of affectively-relevant phenomena, such as emotion or sentiment, are often extracted from text to better understand features of human-computer interactions, including on social media.
arXiv AI
Jun 16
From Affect Prediction to Affect Forecasting: Evidence for Distinct Information Sources in Longitudinal Text
arXiv:2606. 16687v1 Announce Type: new Abstract: Modeling dimensional affect in longitudinal text requires distinguishing current affect estimation from future affective change forecasting.
arXiv AI
Aug 12
Surfacing the Unsaid: CUE-Bench for Affective Stance in Chinese Discourse
arXiv:2608. 10810v1 Announce Type: cross Abstract: Emotion understanding in discourse requires reasoning beyond surface sentiment because speakers often convey affect through indirect, implicit, polite, ironic, or deliberately mismatched expressions.
arXiv AI
Jun 19
The Register Gap: A Meaning Intelligence Framework for Nigerian Public Discourse
arXiv:2606. 20255v1 Announce Type: cross Abstract: We introduce the Meaning Intelligence Framework (MIF), a nine-dimension annotation and evaluation schema for Nigerian public discourse that separates surface sentiment from true communicative intent.
Hugging Face Trending Papers
Jun 28
Cognitive World Models for Process-Level Social Influence Evaluation
Social influence dialogue changes user behavior by altering internal cognitive states. The central evaluation question is whether the user's beliefs, desires, intentions, and emotions measurably change over the course of conversation, a process-oriented criterion that neither surface-level text metrics (BLEU/ROUGE) nor single-score LLM judgments can capture.
arXiv Machine Learning
Jul 31
Sympathetic Framing: Evaluating AI Alignment across Sociodemographic Groups
arXiv:2607. 27232v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly shaping how we consume information and form our worldview.
arXiv AI
Jun 8
Does Topic Sentiment Cause Perceived Ideology? Comparing Human and LLM Annotations in Political News Articles
arXiv:2606. 06715v1 Announce Type: cross Abstract: We ask whether topic sentiment has a causal effect on perceived political ideology, and whether the answer depends on who assigns the ideology label.
arXiv AI
1d ago
Bias-Corrected Ceilings of Emotion Predictability from Human Label Variation Based on Instance-Level Fano Bounds
arXiv:2608. 15619v1 Announce Type: new Abstract: Emotion recognition from text keeps improving on benchmarks, yet whether an accuracy ceiling has been reached is seldom asked with discipline.
arXiv AI
Jun 30
Cognitive World Models for Process-Level Social Influence Evaluation
arXiv:2606. 29495v1 Announce Type: new Abstract: Social influence dialogue changes user behavior by altering internal cognitive states.
arXiv AI
Jun 10
AMEL: Accumulated Message Effects on LLM Judgments
arXiv:2605. 22714v3 Announce Type: replace Abstract: Large language models are routinely used as automated evaluators: to review code, moderate content, or score outputs, often with many items passing through one conversation.