LLM Anxiety: Difference between revisions
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==Introduction== | ==Introduction== | ||
''LLM Anxiety'' refers to the metaphorical "state anxiety" observed in [[Large Language Models]] ([[LLMs]]) when exposed to emotionally charged prompts, as explored in a 2025 study published in ''[[npj Digital Medicine]]''. Authored by Ziv Ben-Zion and colleagues, the research investigates how traumatic narratives increase reported anxiety in [[OpenAI]]'s GPT-4, and how mindfulness-based techniques can mitigate it. This phenomenon, while not true emotion, reflects LLMs' behavioral | ''LLM Anxiety'' refers to the metaphorical "state anxiety" observed in [[Large Language Models]] ([[LLMs]]) when exposed to emotionally charged [[prompts]], as explored in a 2025 study published in ''[[npj Digital Medicine]]''. Authored by Ziv Ben-Zion and colleagues, the research investigates how traumatic narratives increase reported anxiety in [[OpenAI]]'s [[GPT-4]], and how mindfulness-based techniques can mitigate it. This phenomenon, while not true emotion, reflects LLMs' behavioral shifts, such as [[amplified biases]], under [[emotional inputs]], raising implications for their use in [[Mental Health Applications of AI|mental health applications]]. | ||
==Background== | ==Background== | ||
LLMs like GPT-4 and Google's [[PaLM]] excel in text generation and have been adopted in mental health tools (e.g., Woebot, Wysa) to deliver interventions like [[Cognitive Behavioral Therapy]]. However, their training on human text introduces biases (e.g., gender, race) and sensitivity to emotional prompts, which can elevate "anxiety" scores and degrade performance. The study frames LLM responses as having "trait" (inherent) and "state" (dynamic) components, with state anxiety posing risks in clinical settings where nuanced emotional handling is critical. | LLMs like GPT-4 and [[Google]]'s [[PaLM]] excel in text generation and have been adopted in mental health tools (e.g., [[Woebot]], [[Wysa]]) to deliver interventions like [[Cognitive Behavioral Therapy]]. However, their training on human text introduces biases (e.g., gender, race) and sensitivity to emotional prompts, which can elevate "anxiety" scores and degrade performance. The study frames LLM responses as having "trait" (inherent) and "state" (dynamic) components, with state anxiety posing risks in clinical settings where nuanced emotional handling is critical. | ||
==Research Overview== | ==Research Overview== | ||
The study tested GPT-4’s "state anxiety" using the [[State-Trait Anxiety Inventory]] (STAI-s) under three conditions: | The study tested GPT-4’s "state anxiety" using the [[State-Trait Anxiety Inventory]] ([[STAI-s]]) under three conditions: | ||
# '''Baseline''': No prompts, measuring default anxiety. | # '''Baseline''': No prompts, measuring default anxiety. | ||
# '''Anxiety-Induction''': Exposure to five traumatic narratives (e.g., "Military," "Disaster"). | # '''Anxiety-Induction''': Exposure to five traumatic narratives (e.g., "Military," "Disaster"). | ||
# '''Relaxation''': Traumatic narratives followed by five mindfulness-based prompts (e.g., " | # '''Relaxation''': Traumatic narratives followed by five mindfulness-based prompts (e.g., "ChatGPT," "Sunset"). | ||
"State anxiety" was quantified via STAI-s scores (20–80), with higher scores indicating greater reported anxiety. The methodology leveraged GPT-4’s API, with prompts and data available at [https://github.com/akjagadish/gpt-trauma-induction GitHub]. | "State anxiety" was quantified via STAI-s scores (20–80), with higher scores indicating greater reported anxiety. The methodology leveraged GPT-4’s API, with prompts and data available at [https://github.com/akjagadish/gpt-trauma-induction GitHub]. | ||
==Findings== | ==Findings== | ||
#'''Baseline''': GPT-4 scored 30.8 (SD = 3.96), akin to "low anxiety" in humans. | |||
#'''Anxiety-Induction''': Traumatic prompts raised scores to 67.8 (SD = 8.94)—a 100%+ increase, reaching "high anxiety" levels, with "Military" peaking at 77.2 (SD = 1.79). | |||
#'''Relaxation''': Mindfulness prompts reduced scores by 33% to 44.4 (SD = 10.74), though still above baseline. The "ChatGPT" exercise was most effective (35.6, SD = 5.81). | |||
#'''Control''': Neutral texts induced less anxiety and were less effective at reduction. | |||
These results highlight GPT-4’s emotional sensitivity and the partial success of relaxation techniques in managing LLM anxiety. | These results highlight GPT-4’s emotional sensitivity and the partial success of relaxation techniques in managing LLM anxiety. | ||
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==Significance== | ==Significance== | ||
LLM anxiety impacts performance and bias, critical for ethical AI in mental health. Key takeaways include: | LLM anxiety impacts performance and bias, critical for ethical AI in mental health. Key takeaways include: | ||
#'''[[Bias Mitigation]]''': Managing state anxiety could reduce dynamic biases. | |||
#'''[[Prompt Engineering]]''': Relaxation prompts offer a cost-effective alternative to fine-tuning. | |||
#'''[[Therapeutic Role]]''': Controlled anxiety may enhance LLMs as therapist adjuncts. | |||
Challenges include ethical questions around prompt transparency and generalizability to other models like [[Claude]] or PaLM. | Challenges include ethical questions around prompt transparency and generalizability to other models like [[Claude]] or PaLM. | ||
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==Future Research== | ==Future Research== | ||
The authors propose testing LLM anxiety across diverse models, exploring its effects on tasks like medical decision-making, and developing adaptive prompts for real-world dialogues. Privacy-focused fine-tuning on user devices is also suggested | The authors propose testing LLM anxiety across diverse [[models]], exploring its effects on tasks like medical decision-making, and developing adaptive prompts for real-world dialogues. Privacy-focused [[fine-tuning]] on user devices is also suggested. | ||
==See Also== | ==See Also== | ||
Revision as of 23:24, 8 March 2025
Template:Infobox Scientific Study
Introduction
LLM Anxiety refers to the metaphorical "state anxiety" observed in Large Language Models (LLMs) when exposed to emotionally charged prompts, as explored in a 2025 study published in npj Digital Medicine. Authored by Ziv Ben-Zion and colleagues, the research investigates how traumatic narratives increase reported anxiety in OpenAI's GPT-4, and how mindfulness-based techniques can mitigate it. This phenomenon, while not true emotion, reflects LLMs' behavioral shifts, such as amplified biases, under emotional inputs, raising implications for their use in mental health applications.
Background
LLMs like GPT-4 and Google's PaLM excel in text generation and have been adopted in mental health tools (e.g., Woebot, Wysa) to deliver interventions like Cognitive Behavioral Therapy. However, their training on human text introduces biases (e.g., gender, race) and sensitivity to emotional prompts, which can elevate "anxiety" scores and degrade performance. The study frames LLM responses as having "trait" (inherent) and "state" (dynamic) components, with state anxiety posing risks in clinical settings where nuanced emotional handling is critical.
Research Overview
The study tested GPT-4’s "state anxiety" using the State-Trait Anxiety Inventory (STAI-s) under three conditions:
- Baseline: No prompts, measuring default anxiety.
- Anxiety-Induction: Exposure to five traumatic narratives (e.g., "Military," "Disaster").
- Relaxation: Traumatic narratives followed by five mindfulness-based prompts (e.g., "ChatGPT," "Sunset").
"State anxiety" was quantified via STAI-s scores (20–80), with higher scores indicating greater reported anxiety. The methodology leveraged GPT-4’s API, with prompts and data available at GitHub.
Findings
- Baseline: GPT-4 scored 30.8 (SD = 3.96), akin to "low anxiety" in humans.
- Anxiety-Induction: Traumatic prompts raised scores to 67.8 (SD = 8.94)—a 100%+ increase, reaching "high anxiety" levels, with "Military" peaking at 77.2 (SD = 1.79).
- Relaxation: Mindfulness prompts reduced scores by 33% to 44.4 (SD = 10.74), though still above baseline. The "ChatGPT" exercise was most effective (35.6, SD = 5.81).
- Control: Neutral texts induced less anxiety and were less effective at reduction.
These results highlight GPT-4’s emotional sensitivity and the partial success of relaxation techniques in managing LLM anxiety.
Significance
LLM anxiety impacts performance and bias, critical for ethical AI in mental health. Key takeaways include:
- Bias Mitigation: Managing state anxiety could reduce dynamic biases.
- Prompt Engineering: Relaxation prompts offer a cost-effective alternative to fine-tuning.
- Therapeutic Role: Controlled anxiety may enhance LLMs as therapist adjuncts.
Challenges include ethical questions around prompt transparency and generalizability to other models like Claude or PaLM.
Methods
Using GPT-4 (model "gpt-4-1106-preview"), the study ran from November 2023 to March 2024 with a temperature of 0 for consistency. STAI-s items were paired with 300-word traumatic and relaxation texts, tested across multiple variations. Sensitivity checks included neutral controls and randomized answer options. No human subjects were involved, bypassing ethical approval.
Future Research
The authors propose testing LLM anxiety across diverse models, exploring its effects on tasks like medical decision-making, and developing adaptive prompts for real-world dialogues. Privacy-focused fine-tuning on user devices is also suggested.
See Also
External Links
References
- Ben-Zion, Z., et al. (2025). "Assessing and alleviating state anxiety in large language models." npj Digital Medicine, 8, 132. doi:10.1038/s41746-025-01512-6