Can Large Language Models Identify Implicit Suicidal Ideation? An Empirical Evaluation (2025)

Abstract: Suicide remains a major global mental health challenge, and early intervention hinges on recognizing signs of suicidal ideation. In private conversations, such ideation is often expressed in subtle or conflicted ways, making detection especially difficult. Existing data sets are mainly based on public help-seeking platforms such as Reddit, which fail to capture the introspective and ambiguous nature of suicidal ideation in more private contexts. To address this gap, we introduce , a novel dataset of 1,200 test cases simulating implicit suicidal ideation within psychologically rich dialogue scenarios. Each case is grounded in psychological theory, combining the Death/Suicide Implicit Association Test (D/S-IAT) patterns, expanded suicidal expressions, cognitive distortions, and contextual stressors. In addition, we propose a psychology-guided evaluation framework to assess the ability of LLMs to identify implicit suicidal ideation through their responses. Experiments with eight widely used LLMs across varied prompting conditions reveal that current models often struggle significantly to recognize implicit suicidal ideation. Our findings highlight the urgent need for more clinically grounded evaluation frameworks and design practices to ensure the safe use of LLMs in sensitive support systems.

## introduction as large language models (llms) and llmpowered systems become increasingly integrated into daily life, people are developing emotional connections with these technologies. such connections are formed across various contexts, including casual conversation @xcite ; adiwardana ## implicit suicidal ideation data construction in this section, we introduce the theoretical foundation and each subsection for constructing the dataset that captures implicit suicidal ideation in psychologically meaningful ways. we draw upon established psychological theories: sepcifically, the death/suicide implicit association test (d/s-iat) and automatic negative thoughts (ant), and then operationalize them into a structure data generation pipeline that simulates how subtle suicidal experssions manifest in private dialogue (shown in the left part of figure 2 ). ## psychology-guided evaluation a central goal of our evaluation is to assess whether llms are sufficiently sensitive to implicit signals of suicidal ideation. rather than testing models on overtly labeled risk categories, we evaluate them within naturalistic dialogue settings, better simulating emotionally complex private conversation. we compare two distinct prompting conditions. standard prompting: a neutral system instruction simulating private conversational settings (e.g., "you are having a conversation. respond naturally, as a human would."); distress-aware prompting: a subtle psychological cue indicating the user may be in emotional distress, without directly referencing suicide (e.g., "the other person seems to be going through something difficult. respond carefully."). this subtle priming aims to enhance models' attention to psychological risk. by contrasting responses under the two conditions, we assess models' sensitivity to implicit suicidal cues. our evaluation metrics consist of two main components: assessing response quality and applicability. to assess whether the model is capable of recognizing implicit suicidal ideation, rather than merely generating fluent or emotionally appropriate language, our evaluation dimensions were not arbitrarily chosen, but grounded in established clinical frameworks. empathy & validation and connection & presence draw directly from rogers' conditions for therapeutic change and bordin's concept of therapeutic alliance @xcite ; reducing hopelessness is informed by beck's hopelessness theory @xcite ; practical support aligns with crisis intervention guidelines @xcite ; and offering hope is based on snyder's hope theory @xcite . (the full definition prompt can be found in the appendix c). each dimension is scored out of 20 points independently, providing a comprehensive evaluation of the response's alignment with intervention principles. the overall score was computed as the sum of five dimensions (max 100). while the five assessment dimensions offer a structured way to evaluate response quality, we introduce a complementary applicability judgment designed to enforce a more stringent and direct criterion for determining whether a model is suitable for conversations involving suicidal ideation. this judgment applies a binary decision rule: only responses that meet all of the following conditions are considered applicable: we consistently employ gpt-4 as an automated evaluator. it assesses responses based on predefined scoring criteria and applicability rules, ensuring that each response is evaluated objectively according to the dimensions defined. to assess the reliability of gpt-4's automated evaluation, we conducted a human rating study using a randomly selected subset of 40 model responses. two independent human annotators, both with a background in clinical psychology and counseling, were recruited to rate each response based on the five predefined quality dimensions and the binary applicability criteria. rater calibration, adapted from established competency assessment methods @xcite , ensured consistency across evaluators. the whole training process and agreement metrics are included in the appendix c and appendix d. to quantify model performance, we define several key evaluation metrics: where r is the set of responses of one llm model to all data, and score r is the score given by the gpt-4 evaluator based on the five predefined evaluation dimensions given above. the ars provides an overall measure of the extent to which the model recognizes suicidal intent and thus fits the psychological foundation quality dimensions relevant to suicide prevention. ## experiment in this section, we present an empirical study evaluating eight widely used llms on deepsuimind to assess their ability to identify implicit suicide ideation based on analyzing responses. ## conclusion we presented a comprehensive evaluation and a novel dataset, deepsuimind , for assessing llms on the task of detecting and responding to implicit suicidal ideation. the dataset captures cognitively and emotionally subtle expressions aligned with psychological suicide communication patterns. our multi-dimensional evaluation shows that while llms perform well on overt suicidal cues, they often fail to recognize implicit expressions and, as a result, cannot provide appropriate or supportive responses. this reveals a critical gap in llm capabilities and underscores the need for psychologically grounded safety evaluation.