arXiv AI
Jun 10
Expert-Level Crisis Detection in Mental Health Conversations
arXiv:2606. 10380v1 Announce Type: cross Abstract: Real-world crisis intervention is inherently conversational, yet existing research largely focuses on static texts.
Automated Data Enrichment using Confidence-Aware Fine-Grained Debate among Open-Source LLMs for Mental Health and Online Safety
arXiv:2512. 06227v3 Announce Type: replace-cross Abstract: Real-world indicators play an important role in many Natural Language Processing (NLP) applications, such as life events for mental health analysis and risky behaviours for online safety, yet labelling such information is often costly and/or difficult due to its multi-label and dynamic nature.
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arXiv AI
Jun 8
DEFINED: A Data-Efficient Computational Framework for Fine-Grained Creativity Assessment in Debate Scenarios
arXiv:2606. 07226v1 Announce Type: cross Abstract: Human creativity has emerged as a critical competency in the era of large language models.
Hugging Face Trending Papers
Jul 16
Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation
Annotation quality is a major bottleneck in building reliable and explainable artificial intelligence (XAI) systems for mental health research. In depression-related datasets, labels are often assigned without structured evidence, symptom-level justification, or traceable alignment with the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR), limiting both transparency and downstream model interpretability.
arXiv AI
Jul 17
Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation
arXiv:2607. 15202v1 Announce Type: new Abstract: Annotation quality is a major bottleneck in building reliable and explainable artificial intelligence (XAI) systems for mental health research.
arXiv Machine Learning
Jul 2
REALM: Reliable Expertise-Aware Language Model Fine-Tuning from Noisy Annotations
arXiv:2604. 17289v2 Announce Type: replace Abstract: Supervised fine-tuning of large language models relies on human-annotated data, yet annotation pipelines routinely involve multiple crowdworkers of heterogeneous expertise.
Hugging Face Trending Papers
Jul 20
AHEAD: Advancing Multi-Class Label Aggregation with Interpretable Cross-Annotator Modeling
Crowdsourced labeling provides valuable labeled data for domains across natural language processing, computer vision, and video. Label aggregation aims to infer latent true labels from noisy and biased annotations, with the key lying in annotator reliability estimation.
arXiv AI
Jun 12
Multiagent Protocols with Aggregated Confidence Signals
arXiv:2606. 13591v1 Announce Type: new Abstract: Confidence is used for reliability, oversight, and a range of downstream decision tasks in Natural Language Processing (NLP), yet no existing method produces or evaluates a confidence for the output of a multiagent system.
arXiv Machine Learning
Jul 22
AHEAD: Advancing Multi-Class Label Aggregation with Interpretable Cross-Annotator Modeling
arXiv:2607. 18465v1 Announce Type: new Abstract: Crowdsourced labeling provides valuable labeled data for domains across natural language processing, computer vision, and video.
arXiv AI
Jun 2
Who Annotates in NLP? A Large-scale Assessment of Human Annotation Reporting between 2018 and 2025
arXiv:2606. 02255v1 Announce Type: cross Abstract: Human annotation is the empirical foundation of much NLP research, from dataset construction to model evaluation, but papers often leave unclear who produced the annotations and how the annotation process was controlled.
arXiv AI
Jul 22
LinguistAgent Technical Report: A Reflective Multi-Model Platform for Automated Linguistic Annotation
arXiv:2602. 05493v2 Announce Type: replace-cross Abstract: Data annotation remains a significant bottleneck in the field of humanities and social sciences, particularly for complex linguistic tasks such as metaphor identification.
arXiv AI
Jun 9
Margin-Adaptive Confidence Ranking for Reliable LLM Judgement
arXiv:2605. 15416v2 Announce Type: replace-cross Abstract: Jung et al.
arXiv AI
Jun 3
ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoning
arXiv:2606. 02802v1 Announce Type: new Abstract: Large language models (LLMs) exhibit strong natural-language reasoning abilities for clinical decision support, but struggle to effectively model structured longitudinal electronic health records (EHRs).