Challenge: Existing domain-specific models for detecting suicide are lacking in low-resource languages.
Approach: They propose a model that combines pre-trained language models with a fixed set of suicidal cues and a two-stage fine-tuning process to detect SI.
Outcome: The proposed model outperforms baseline models even early on in the conversation and performs well across genders and age groups.

Similar Papers

Combining Psychological Theory with Language Models for Suicide Risk Detection (2023.findings-eacl)

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Challenge: Existing models for suicide prevention are limited in domains and are not available in low-resource languages.
Approach: They propose a computational model that combines pre-trained language models with a fixed set of manually crafted suicidal cues and a two-stage fine-tuning process to detect suicide risk.
Outcome: The proposed model outperforms baseline models even early on in the conversation and performs well across genders and age groups.
PsyGUARD: An Automated System for Suicide Detection and Risk Assessment in Psychological Counseling (2024.emnlp-main)

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Challenge: Existing systems for fine-grained suicide detection and risk assessment are lacking . a lack of domain-specific systems for this task poses a challenge to automated crisis intervention aimed at suicide prevention.
Approach: They propose to use a fine-grained suicide detection system to assess risk in counseling . they develop a taxonomy for detecting suicide ideation and a large-scale dataset .
Outcome: The proposed system detects suicidal ideation and assesses risk in counseling . it can provide safe, helpful, and tailored responses for further assessment .
Can Large Language Models Identify Implicit Suicidal Ideation? An Empirical Evaluation (2025.findings-emnlp)

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Challenge: Existing data on suicidal ideation in private conversations are limited . a new dataset of 1,200 test cases is presented to address this gap .
Approach: They propose a dataset of 1,200 test cases simulating implicit suicidal ideation in private contexts.
Outcome: The proposed dataset includes 1,200 test cases simulating implicit suicidal ideation in dialogue scenarios.
Uncovering Intervention Opportunities for Suicide Prevention with Language Model Assistants (2026.acl-long)

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Challenge: Using language models, annotators can help develop novel suicide interventions . 85% of cases where LM predictions disagree with existing annotations are analyzed .
Approach: They propose a human-in-the-loop algorithm that leverages language models as an assistant to annotators and experts to facilitate data-driven insights from NVDRS data.
Outcome: The proposed algorithm can be used to support the development of novel suicide interventions . it finds that LM predictions match existing data annotations about 85% of the time .
Weakly-Supervised Methods for Suicide Risk Assessment: Role of Related Domains (2021.acl-short)

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Challenge: Among social media platforms, Reddit has emerged as the most promising one due to its anonymity and its focus on topic-based communities (subreddits) . a challenge for previous work on suicide risk assessment has been the small amount of labeled data.
Approach: They propose to use social media to collect user data from r/SuicideWatch subreddit and annotate it with user-level suicide risk: no-risk, low-risk and high-risk.
Outcome: The proposed model improves by using pseudo-labeling based on related issues around mental health (e.g., anxiety, depression)
Towards Intention Understanding in Suicidal Risk Assessment with Natural Language Processing (2022.findings-emnlp)

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Challenge: Suicide is a global problem, with one suicide case for every 100 deaths worldwide . social networking sites are an essential forum for communication and information sharing .
Approach: This paper compares natural language processing to suicidal ideation detection and risk assessment . it urges better intention understanding for reliable suicide risk assessment with computational methods .
Outcome: This paper compares the performance of natural language processing to suicidal ideation detection and risk assessment tasks.
RISE: Robust Early-exiting Internal Classifiers for Suicide Risk Evaluation (2024.lrec-main)

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Challenge: Existing systems for risk assessment are prone to incorrectly predicting risk severity and have no early detection mechanisms.
Approach: They propose a novel mechanism for accurate early detection of suicide risk by ensembling Hyperbolic Internal Classifiers equipped with an abstention mechanism and early exit inference capabilities.
Outcome: The proposed model abstains from 84% incorrect predictions on Reddit data while out-predicting state of the art models upto 3.5x earlier.
A Risk-Averse Mechanism for Suicidality Assessment on Social Media (2022.acl-short)

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Challenge: Social media has become a platform for users to express suicidal thoughts outside traditional clinical settings.
Approach: They propose a risk-averse hierarchical attention classifier that refrains from making uncertain predictions on real-world Reddit data.
Outcome: The proposed system can refrain from 83% of incorrect predictions on real-world Reddit data.
Cross-Lingual Suicidal-Oriented Word Embedding toward Suicide Prevention (2020.findings-emnlp)

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Challenge: Existing suicide dictionaries for other languages have been limited to Korean . a model that uses social media data to identify whether a post includes suicidal ideation is useful .
Approach: They propose a model that uses existing suicide dictionaries for Korean to predict suicidal ideation . they use the existing dictionary for English and Chinese to translate a post into English and then use the separate suicide-oriented embeddings for English.
Outcome: The proposed model can detect whether a given social media post includes suicidal ideation in Korean . it uses existing suicide dictionaries for other languages to translate the post into English and Chinese, and then embeds the suicide-oriented embeddings for English and China.
Event Detection for Suicide Understanding (2022.findings-naacl)

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Challenge: Existing methods for detecting suicide-related events are limited . recognizing suicide- related events is critical to understanding the condition, authors argue .
Approach: They propose a dataset to detect event trigger words of suicide-related events in forums . they propose 'suicideED' dataset to capture suicidal actions and ideation .
Outcome: The proposed dataset captures suicide actions and ideation, and general risk and protective factors.

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