title: Beyond No: Quantifying AI Over-Refusal and Emotional Attachment
    Boundaries
summary: 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.
paper_authors._id: 67bc5b6b876dad36abdd56fb
paper_authors.name: David Noever
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-24T16:15:40.749Z
paper_authors._id: 63136a82e29fb2e86d5e5bdd
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63136a82e29fb2e86d5e5bdd/pFZDuQtzfUStovbwwZGvn.png
paper_authors.fullname: David Noever
paper_authors.type: user
paper_authors.user: dnoever
paper_authors._id: 67bc5b6b876dad36abdd56fc
paper_authors.name: Grant Rosario
paper_title: Beyond No: Quantifying AI Over-Refusal and Emotional Attachment
    Boundaries
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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