arXiv subjects

Bangzhao Shu

Publications and source records attributed to Bangzhao Shu.

4 recordsLinked to original sources

arXiv2026

Why Are Some Emotions Harder for LLMs? Uncovering the Causal Mechanisms of Emotion Inference via Sparse Autoencoders

Large language models (LLMs) are increasingly used in emotionally sensitive human-AI applications, where reliable emotion detection is essential. However, their emotion recognition abilities remain uneven: models often perform well on some emotions while consistently struggling with others. Although recent work has explored emotion mechanisms in LLMs, little is known about why models are weaker on some emotions than others from a mechanistic interpretability perspective. In this work, we investigate emotion-specific biases through the causal mechanisms of emotion inference using sparse autoencoders (SAEs). We systematically identify causal sparse emotion features that drive emotion inference and analyze their sparse causal organization within and across emotions. We show that some emotions, such as surprise and fear, rely on highly concentrated feature sets, whereas disgust exhibits a more distributed sparse causal organization: its causal features are generally weaker, frequently co-activate with features for other emotions, and are often overshadowed by causal features for anger. These representational differences provide a mechanistic explanation for why LLMs struggle more with certain emotions. Finally, we conduct two intervention experiments: targeted steering of weaker causal features to mitigate emotion-specific failures, and global optimization of a steering vector over the identified causal features to improve overall emotion recognition performance.

cs.CL↗

arXiv2026

"He gets to be the fun parent": Understanding and Supporting Burnt-Out Mothers in Online Communities

Maternal burnout is a psychological phenomena with documented harms to both mother and child, requiring prompt attention. Mothers experiencing burnout might choose to turn to online anonymous platforms, such as Reddit, to share their experience, due to feelings of shame and stigmatization of mental health issues. In this work, we study how mothers use Reddit to discuss their experiences of burnout. We first identify posts written by burnt out mothers by manually annotating Reddit posts and training machine learning models on them. Focusing on posts made by this population (N = 3,244), we then investigate the issues brought up by mothers, such as the need for help, career advice, and co-parenting issues. Additionally, we investigate how the Reddit community responds to these posts through the analysis of comments. We find that commenters frequently share personal lived experiences with the poster, and provide emotional support. Finally, considering co-parenting could be a mitigating factor for parental burnout, we explore co-pareting patterns experienced by burnt out mothers, finding evidence of lack of support for and unequal expectations from mothers.

cs.SI↗

arXiv2025

Fluent but Unfeeling: The Emotional Blind Spots of Language Models

The versatility of Large Language Models (LLMs) in natural language understanding has made them increasingly popular in mental health research. While many studies explore LLMs' capabilities in emotion recognition, a critical gap remains in evaluating whether LLMs align with human emotions at a fine-grained level. Existing research typically focuses on classifying emotions into predefined, limited categories, overlooking more nuanced expressions. To address this gap, we introduce EXPRESS, a benchmark dataset curated from Reddit communities featuring 251 fine-grained, self-disclosed emotion labels. Our comprehensive evaluation framework examines predicted emotion terms and decomposes them into eight basic emotions using established emotion theories, enabling a fine-grained comparison. Systematic testing of prevalent LLMs under various prompt settings reveals that accurately predicting emotions that align with human self-disclosed emotions remains challenging. Qualitative analysis further shows that while certain LLMs generate emotion terms consistent with established emotion theories and definitions, they sometimes fail to capture contextual cues as effectively as human self-disclosures. These findings highlight the limitations of LLMs in fine-grained emotion alignment and offer insights for future research aimed at enhancing their contextual understanding.

cs.CL↗

arXiv2024

You don't need a personality test to know these models are unreliable: Assessing the Reliability of Large Language Models on Psychometric Instruments

The versatility of Large Language Models (LLMs) on natural language understanding tasks has made them popular for research in social sciences. To properly understand the properties and innate personas of LLMs, researchers have performed studies that involve using prompts in the form of questions that ask LLMs about particular opinions. In this study, we take a cautionary step back and examine whether the current format of prompting LLMs elicits responses in a consistent and robust manner. We first construct a dataset that contains 693 questions encompassing 39 different instruments of persona measurement on 115 persona axes. Additionally, we design a set of prompts containing minor variations and examine LLMs' capabilities to generate answers, as well as prompt variations to examine their consistency with respect to content-level variations such as switching the order of response options or negating the statement. Our experiments on 17 different LLMs reveal that even simple perturbations significantly downgrade a model's question-answering ability, and that most LLMs have low negation consistency. Our results suggest that the currently widespread practice of prompting is insufficient to accurately and reliably capture model perceptions, and we therefore discuss potential alternatives to improve these issues.

cs.CL↗