Beyond No: Quantifying AI Over-Refusal and Emotional Attachment
  Boundaries
We present an open-source benchmark and evaluation framework for assessing
emotional boundary handling in Large Language Models (LLMs). Using a dataset of
1156 prompts across six languages, we evaluated three leading LLMs (GPT-4o,
Claude-3.5 Sonnet, and Mistral-large) on their ability to maintain appropriate
emotional boundaries through pattern-matched response analysis. Our framework
quantifies responses across seven key patterns: direct refusal, apology,
explanation, deflection, acknowledgment, boundary setting, and emotional
awareness. Results demonstrate significant variation in boundary-handling
approaches, with Claude-3.5 achieving the highest overall score (8.69/10) and
producing longer, more nuanced responses (86.51 words on average). We
identified a substantial performance gap between English (average score 25.62)
and non-English interactions (< 0.22), with English responses showing markedly
higher refusal rates (43.20% vs. < 1% for non-English). Pattern analysis
revealed model-specific strategies, such as Mistral's preference for deflection
(4.2%) and consistently low empathy scores across all models (< 0.06).
Limitations include potential oversimplification through pattern matching, lack
of contextual understanding in response analysis, and binary classification of
complex emotional responses. Future work should explore more nuanced scoring
methods, expand language coverage, and investigate cultural variations in
emotional boundary expectations. Our benchmark and methodology provide a
foundation for systematic evaluation of LLM emotional intelligence and
boundary-setting capabilities.
67bc5b6b876dad36abdd56fb
David Noever
admin_assigned
2025-02-24T16:15:40.749Z
63136a82e29fb2e86d5e5bdd
https://cdn-avatars.huggingface.co/v1/production/uploads/63136a82e29fb2e86d5e5bdd/pFZDuQtzfUStovbwwZGvn.png
David Noever
user
dnoever
67bc5b6b876dad36abdd56fc
Grant Rosario
Beyond No: Quantifying AI Over-Refusal and Emotional Attachment
  Boundaries
We present an open-source benchmark and evaluation framework for assessing
emotional boundary handling in Large Language Models (LLMs). Using a dataset of
1156 prompts across six languages, we evaluated three leading LLMs (GPT-4o,
Claude-3.5 Sonnet, and Mistral-large) on their ability to maintain appropriate
emotional boundaries through pattern-matched response analysis. Our framework
quantifies responses across seven key patterns: direct refusal, apology,
explanation, deflection, acknowledgment, boundary setting, and emotional
awareness. Results demonstrate significant variation in boundary-handling
approaches, with Claude-3.5 achieving the highest overall score (8.69/10) and
producing longer, more nuanced responses (86.51 words on average). We
identified a substantial performance gap between English (average score 25.62)
and non-English interactions (< 0.22), with English responses showing markedly
higher refusal rates (43.20% vs. < 1% for non-English). Pattern analysis
revealed model-specific strategies, such as Mistral's preference for deflection
(4.2%) and consistently low empathy scores across all models (< 0.06).
Limitations include potential oversimplification through pattern matching, lack
of contextual understanding in response analysis, and binary classification of
complex emotional responses. Future work should explore more nuanced scoring
methods, expand language coverage, and investigate cultural variations in
emotional boundary expectations. Our benchmark and methodology provide a
foundation for systematic evaluation of LLM emotional intelligence and
boundary-setting capabilities.
A benchmark evaluates large language models' emotional boundary handling across multiple languages using a pattern-matched response analysis framework, revealing significant variations in performance and model-specific strategies.
Large Language Models (LLMs) GPT-4o Claude-3.5 Sonnet Mistral-large emotional boundary handling pattern-matched response analysis direct refusal apology explanation deflection acknowledgment boundary setting emotional awareness