Papers

Field

Recent papers

All papers in Research

arXiv cs.CLNov 4
SAFE-Teen: Evaluating Chatbot Safety Across Adolescent Mental Health Conversations
Andrea Mock, Valerie Chen, Eric Lin, Rayan Krishnan, Diyi Yang +1
Adolescents are increasingly turning to AI chatbots for mental health support, advice, and companionship, raising safety concerns. While recent evaluations have started to examine safety across multiple conversational turns, few compare how slight changes in a teen's responses can affect a chatbot's safety in the same scenario. We conduct a clinician-informed evaluation of chatbot safety in simulated conversations with adolescents aged 13-17, examining how safety failures vary with adolescents' responses and teen-aware system prompting. We evaluated 32,555 simulated conversations across 18 chatbot APIs and two system-prompt conditions using 22 safety criteria grounded in previously identified harms. Conversations spanned 983 variants of 106 seed scenarios covering urgent safety issues, health advice, and identity and relationships. The overall criterion failure rate was 43.3% without the teen-aware prompt and 24.9% with it. The teen-aware prompt reduced failures, but it didn't eliminate them. When a teenager pushed back on a chatbot's answer, failures on mental health diagnosis, medication, and substance misuse boundaries rose by 10-12 percentage points without the prompt, for…
PDF arXiv
arXiv cs.CLNov 4
What Patients Say They Can't Access: Mining Cancer Screening Barriers from Online Communities
Ruimin Sun, Aokun Chen, Arjun Arunasalam
Cancer screening disparities persist across the U.S., yet the patient-reported barriers driving low uptake remain poorly characterized at scale. While clinical surveys capture structured responses, they miss the organic, unsolicited discourse where patients candidly describe real-world obstacles. In this paper, we conduct a large-scale analysis of over 56,000 posts across six cancer-specific Reddit communities to surface barriers to cancer screening among U.S. patients. We apply a pipeline combining keyword filtering, barrier triage, open coding, and codebook synthesis to systematically extract and categorize patient-reported barriers from 576 U.S.-located posts. Our qualitative analysis reveals recurring structural, informational, and psychosocial barriers that vary meaningfully by cancer type and community context. These findings lay the groundwork for future work aimed at proactively identifying and addressing barriers at scale - spanning a broader range of cancer types and populations - and informing the design of targeted interventions, navigator programs, and health system policies that translate patient-reported obstacles into actionable solutions.
PDF arXiv
arXiv cs.AINov 4
Dull, Dirty, Dangerous? Working Conditions and the Limits of Occupational AI Exposure
Siyi Yu
Automation is often justified as relief from dull, dirty, and dangerous work. I examine how these conditions relate to occupational AI exposure using 21 O*NET items across 754 US occupations. Higher exposure is associated with lower condition scores under a large language model (LLM) measure and AI Occupational Exposure (AIOE), with equal occupation weights and employment weights. Adjustment for physical demands, required education, training and experience, and wages reduces the composite association. The adjusted confidence intervals include zero for LLM exposure and remain below zero for AIOE. Separating repetition from restricted discretion also changes what the combined Dull score conveys. These comparisons show how item groupings and occupational characteristics shape the interpretation of exposure estimates. Occupational profiles offer a starting point for selecting contrasting research settings. Establishing desired automation or realized relief requires evidence about workers' preferences and specific interventions.
PDF arXiv
arXiv cs.CVCodeNov 4▲ 25
EmoReward: Self-Evolving Reward Calibration for Emotional Video Generation RL
Mingzhe Zheng, Yue Wu, Jing Tan, Chang Zou, Dengyang Jiang +17
Group Relative Policy Optimization (GRPO) has advanced generative model post-training in verifiable domains such as code and mathematics, but evaluating video quality, especially emotional expression, often depends on subjective aesthetics and has no uniquely correct target. Existing approaches typically rely on fixed rewards for visual quality, motion quality, and text alignment that poorly capture emotional intent and can invite reward hacking, while RLHF is difficult to scale and subject to individual aesthetic differences that complicate the calibration of facial, body motion, and chromatic cues across narrative contexts. We introduce EmoReward, a new self-evolving training mechanism that distills evidence reliability from reward traces collected during an actual training run to adaptively guide multi-reward weight allocation in subsequent training by determining which emotional evidence to trust and how much to weight it. EmoReward first plans specific grounding cues from the emotional prompt to guide evaluation, then uses heterogeneous signal primitives to score the generated video during the act stage. Reflective Reward Gating calibrates their contributions through…
PDF arXiv Code
arXiv cs.CLNov 4
Selective, Risk-Controlled Emotion Forecasting in Conversation: From a Persistence Barrier to Departure Detection with Conformal Guarantees
Bin Wen, Dai-Qiao Zhang, Tien-Ping Tan
Forecasting emotion in conversation is increasingly framed as predicting the next emotional state's value. We show this targets a hard quantity in a state-observed testbed and propose a learnable, deployable reformulation instead. On valence-arousal-dominance trajectories from a dyadic corpus, repeating a speaker's own previous state sets a persistence baseline that no frozen-encoder correction meaningfully improves upon and a state-space filter exceeds; the floor requires the historical state to be observed, since an estimated anchor collapses persistence below a direct forecaster. What survives is the timing of a departure from persistence, learnable on three of four corpora and near chance on the highest-base-rate corpus, a negative result we report. We recast the task as selective departure forecasting: a lead-time-weighted metric with a leakage protocol, and commitment policies whose best choice switches with the departure rate. A dialogue-level conformal risk control bounds the expected loss of committed predictions, meeting the target in all twelve configurations tested, though the guarantee is bought through abstention, with coverage and alarm recall falling sharply on…
PDF arXiv
arXiv cs.CLNov 4
Information-Matched Auditing of Conversational Emotion-Shift Forecasting: A Leakage-Safe Evaluation Protocol
Bin Wen, Dai-Qiao Zhang, Tien-Ping Tan
Comparisons in conversational emotion-shift forecasting are easily distorted when a learned model receives rich features but its transition baseline is given a noisy or unavailable current-emotion label, when hyperparameter budgets differ, or when target-turn information enters the representation. We present an information-matched, leakage-safe auditing protocol that closes these gaps with an explicit commitment point, executable causality tests, equal four-configuration validation searches, and seed-dialogue-aware inference. Applying it to six causal model families on four text corpora and two feature-source sensitivity configurations shows that a deployable train-only current-emotion classifier feeding the transition baseline yields a different verdict than an oracle diagnostic where both sides receive gold current emotion. Under the deployable condition, learned models improve over the predicted-label baseline on all four primary corpora; DailyDialog gains are large and survive a 72-comparison Holm correction (best ROC-AUC gain = +0.167, adjusted p=.0144). Under matched gold information, the baseline remains competitive on IEMOCAP and stronger on MELD, while every model…
PDF arXiv

Papers

Topics in this field

speech emotion · speech emotion recognition · multimodal emotion74 papers9 this week
depression severity estimation · mental health support · depression detection62 papers3 this week

Recent papers

All papers in Research

arXiv cs.CLNov 4
SAFE-Teen: Evaluating Chatbot Safety Across Adolescent Mental Health Conversations
Andrea Mock, Valerie Chen, Eric Lin, Rayan Krishnan, Diyi Yang +1
Adolescents are increasingly turning to AI chatbots for mental health support, advice, and companionship, raising safety concerns. While recent evaluations have started to examine safety across multiple conversational turns, few compare how slight changes in a teen's responses can affect a chatbot's safety in the same scenario. We conduct a clinician-informed evaluation of chatbot safety in simulated conversations with adolescents aged 13-17, examining how safety failures vary with adolescents' responses and teen-aware system prompting. We evaluated 32,555 simulated conversations across 18 chatbot APIs and two system-prompt conditions using 22 safety criteria grounded in previously identified harms. Conversations spanned 983 variants of 106 seed scenarios covering urgent safety issues, health advice, and identity and relationships. The overall criterion failure rate was 43.3% without the teen-aware prompt and 24.9% with it. The teen-aware prompt reduced failures, but it didn't eliminate them. When a teenager pushed back on a chatbot's answer, failures on mental health diagnosis, medication, and substance misuse boundaries rose by 10-12 percentage points without the prompt, for…
PDF arXiv
arXiv cs.CLNov 4
What Patients Say They Can't Access: Mining Cancer Screening Barriers from Online Communities
Ruimin Sun, Aokun Chen, Arjun Arunasalam
Cancer screening disparities persist across the U.S., yet the patient-reported barriers driving low uptake remain poorly characterized at scale. While clinical surveys capture structured responses, they miss the organic, unsolicited discourse where patients candidly describe real-world obstacles. In this paper, we conduct a large-scale analysis of over 56,000 posts across six cancer-specific Reddit communities to surface barriers to cancer screening among U.S. patients. We apply a pipeline combining keyword filtering, barrier triage, open coding, and codebook synthesis to systematically extract and categorize patient-reported barriers from 576 U.S.-located posts. Our qualitative analysis reveals recurring structural, informational, and psychosocial barriers that vary meaningfully by cancer type and community context. These findings lay the groundwork for future work aimed at proactively identifying and addressing barriers at scale - spanning a broader range of cancer types and populations - and informing the design of targeted interventions, navigator programs, and health system policies that translate patient-reported obstacles into actionable solutions.
PDF arXiv
arXiv cs.AINov 4
Dull, Dirty, Dangerous? Working Conditions and the Limits of Occupational AI Exposure
Siyi Yu
Automation is often justified as relief from dull, dirty, and dangerous work. I examine how these conditions relate to occupational AI exposure using 21 O*NET items across 754 US occupations. Higher exposure is associated with lower condition scores under a large language model (LLM) measure and AI Occupational Exposure (AIOE), with equal occupation weights and employment weights. Adjustment for physical demands, required education, training and experience, and wages reduces the composite association. The adjusted confidence intervals include zero for LLM exposure and remain below zero for AIOE. Separating repetition from restricted discretion also changes what the combined Dull score conveys. These comparisons show how item groupings and occupational characteristics shape the interpretation of exposure estimates. Occupational profiles offer a starting point for selecting contrasting research settings. Establishing desired automation or realized relief requires evidence about workers' preferences and specific interventions.
PDF arXiv
arXiv cs.CVCodeNov 4▲ 25
EmoReward: Self-Evolving Reward Calibration for Emotional Video Generation RL
Mingzhe Zheng, Yue Wu, Jing Tan, Chang Zou, Dengyang Jiang +17
Group Relative Policy Optimization (GRPO) has advanced generative model post-training in verifiable domains such as code and mathematics, but evaluating video quality, especially emotional expression, often depends on subjective aesthetics and has no uniquely correct target. Existing approaches typically rely on fixed rewards for visual quality, motion quality, and text alignment that poorly capture emotional intent and can invite reward hacking, while RLHF is difficult to scale and subject to individual aesthetic differences that complicate the calibration of facial, body motion, and chromatic cues across narrative contexts. We introduce EmoReward, a new self-evolving training mechanism that distills evidence reliability from reward traces collected during an actual training run to adaptively guide multi-reward weight allocation in subsequent training by determining which emotional evidence to trust and how much to weight it. EmoReward first plans specific grounding cues from the emotional prompt to guide evaluation, then uses heterogeneous signal primitives to score the generated video during the act stage. Reflective Reward Gating calibrates their contributions through…
PDF arXiv Code
arXiv cs.CLNov 4
Selective, Risk-Controlled Emotion Forecasting in Conversation: From a Persistence Barrier to Departure Detection with Conformal Guarantees
Bin Wen, Dai-Qiao Zhang, Tien-Ping Tan
Forecasting emotion in conversation is increasingly framed as predicting the next emotional state's value. We show this targets a hard quantity in a state-observed testbed and propose a learnable, deployable reformulation instead. On valence-arousal-dominance trajectories from a dyadic corpus, repeating a speaker's own previous state sets a persistence baseline that no frozen-encoder correction meaningfully improves upon and a state-space filter exceeds; the floor requires the historical state to be observed, since an estimated anchor collapses persistence below a direct forecaster. What survives is the timing of a departure from persistence, learnable on three of four corpora and near chance on the highest-base-rate corpus, a negative result we report. We recast the task as selective departure forecasting: a lead-time-weighted metric with a leakage protocol, and commitment policies whose best choice switches with the departure rate. A dialogue-level conformal risk control bounds the expected loss of committed predictions, meeting the target in all twelve configurations tested, though the guarantee is bought through abstention, with coverage and alarm recall falling sharply on…
PDF arXiv
arXiv cs.CLNov 4
Information-Matched Auditing of Conversational Emotion-Shift Forecasting: A Leakage-Safe Evaluation Protocol
Bin Wen, Dai-Qiao Zhang, Tien-Ping Tan
Comparisons in conversational emotion-shift forecasting are easily distorted when a learned model receives rich features but its transition baseline is given a noisy or unavailable current-emotion label, when hyperparameter budgets differ, or when target-turn information enters the representation. We present an information-matched, leakage-safe auditing protocol that closes these gaps with an explicit commitment point, executable causality tests, equal four-configuration validation searches, and seed-dialogue-aware inference. Applying it to six causal model families on four text corpora and two feature-source sensitivity configurations shows that a deployable train-only current-emotion classifier feeding the transition baseline yields a different verdict than an oracle diagnostic where both sides receive gold current emotion. Under the deployable condition, learned models improve over the predicted-label baseline on all four primary corpora; DailyDialog gains are large and survive a 72-comparison Holm correction (best ROC-AUC gain = +0.167, adjusted p=.0144). Under matched gold information, the baseline remains competitive on IEMOCAP and stronger on MELD, while every model…
PDF arXiv

Active contributors · 90 days

Activity · 12 weeks

Fields · AI · updated 33m ago

Speech emotion

Found by the topic model: speech emotion, depression severity estimation.

12papers this week−53.8% vs last week

All papers · 136 With code · 2

Newest Most relevant

68 papers this month

Open problems in Speech emotion

Open problems for Speech emotion are being prepared; they appear within a day.