Mental health professionals and clinicians have observed the upsurge of
mental disorders due to Interpersonal Risk Factors (IRFs). To simulate the
human-in-the-loop triaging scenario for early detection of mental health
disorders, we recognized textual indications to ascertain these IRFs : Thwarted
Belongingness (TBe) and Perceived Burdensomeness (PBu) within personal
narratives. In light of this, we use N-shot learning with GPT-3 model on the
IRF dataset, and underscored the importance of fine-tuning GPT-3 model to
incorporate the context-specific sensitivity and the interconnectedness of
textual cues that represent both IRFs.
In this paper, we introduce an Interpretable Prompting (InterPrompt)} method
to boost the attention mechanism by fine-tuning the GPT-3 model. This allows a
more sophisticated level of language modification by adjusting the pre-trained
weights. Our model learns to detect usual patterns and underlying connections
across both the IRFs, which leads to better system-level explainability and
trustworthiness. The results of our research demonstrate that all four variants
of GPT-3 model, when fine-tuned with InterPrompt, perform considerably better
as compared to the baseline methods, both in terms of classification and
explanation generation.

通过使用 N-shot 学习方法和 GPT-3 模型进行细化调整，本论文介绍了一种可解释的提示方法来增强 GPT-3 模型的注意机制，提供更高级别的语言修改，从而检测出个人叙述中表示两个人际风险因素的文本线索，取得更好的系统级可解释性和可信度。研究结果表明，在使用 InterPrompt 调整后，四个 GPT-3 模型的变种在分类和解释生成方面相较于基准方法都有显著的提升。

InterPrompt: Reddit 帖子中互相关联的人际风险因素的可解释提示

InterPrompt: Interpretable Prompting for Interrelated Interpersonal Risk  Factors in Reddit Posts

With a surge in identifying suicidal risk and its severity in social media
posts, we argue that a more consequential and explainable research is required
for optimal impact on clinical psychology practice and personalized mental
healthcare. The success of computational intelligence techniques for inferring
mental illness from social media resources, points to natural language
processing as a lens for determining Interpersonal Risk Factors (IRF) in human
writings. Motivated with limited availability of datasets for social NLP
research community, we construct and release a new annotated dataset with
human-labelled explanations and classification of IRF affecting mental
disturbance on social media: (i) Thwarted Belongingness (TBe), and (ii)
Perceived Burdensomeness (PBu). We establish baseline models on our dataset
facilitating future research directions to develop real-time personalized AI
models by detecting patterns of TBe and PBu in emotional spectrum of user's
historical social media profile.

本研究构建并发布一个新的标注数据集，针对社交媒体上影响精神健康的人际风险因素进行分类和解释，以自然语言处理为切入点，建立模型并探讨以此为基础的 AI 个性化模型的未来研究方向。

社交媒体帖子中可解释人际风险因素的注释数据集

An Annotated Dataset for Explainable Interpersonal Risk Factors of  Mental Disturbance in Social Media Posts

The World Health Organization (WHO) has emphasized the importance of
significantly accelerating suicide prevention efforts to fulfill the United
Nations' Sustainable Development Goal (SDG) objective of 2030. In this paper,
we present an end-to-end multitask system to address a novel task of detection
of two interpersonal risk factors of suicide, Perceived Burdensomeness (PB) and
Thwarted Belongingness (TB) from suicide notes. We also introduce a manually
translated code-mixed suicide notes corpus, CoMCEASE-v2.0, based on the
benchmark CEASE-v2.0 dataset, annotated with temporal orientation, PB and TB
labels. We exploit the temporal orientation and emotion information in the
suicide notes to boost overall performance. For comprehensive evaluation of our
proposed method, we compare it to several state-of-the-art approaches on the
existing CEASE-v2.0 dataset and the newly announced CoMCEASE-v2.0 dataset.
Empirical evaluation suggests that temporal and emotional information can
substantially improve the detection of PB and TB.

本文提出一种全面的多任务系统来检测自杀中的两个人际风险因素，并利用情感信息和时间定向提高了检测的性能。