When Empathy Misses the Goal: A Benchmark for Goal Displacement in LLM Advice

Key points

• We conceptualize this failure as $goal displacement$, the substitution of persona-maintenance content for goal-relevant substance.1
• To measure it, we introduce $GDB$ (Goal Displacement Benchmark), comprising 230 scenarios across nine domains and $4{,}134$ human-scored responses from 18 models.1
• A $2 \times 2$ taxonomy crosses hard constraints with emotional distress, distinguishing failures to confront infeasible goals or combine empathy with practical guidance from reverse displacement, where unsolicited problem-solving replaces requested emotional support.1
• Matched OLMo-2 checkpoints show that mixed empathy-with-constraints displacement remains at $52.3%$ after supervised fine-tuning and falls to $13.8%$ after direct preference optimization.1

Sources (1)

• [1]When Empathy Misses the Goal: A Benchmark for Goal Displacement in LLM Advice
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 08:35 PM

  Conversational large language models can respond with warmth and reassurance while failing to advance the user's stated goal.

  We conceptualize this failure as $goal displacement$, the substitution of persona-maintenance content for goal-relevant substance.

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