EMPA: Evaluating Persona-Aligned Empathy as a Process
This addresses the problem of evaluating latent, sparse feedback in dialogue agents for researchers and developers, though it appears incremental as it builds on existing evaluation methods.
The paper tackles the challenge of evaluating persona-aligned empathy in LLM-based dialogue agents by introducing EMPA, a process-oriented framework that scores trajectories based on directional alignment, cumulative impact, and stability, enabling reproducible comparison and optimization of long-horizon empathic behavior.
Evaluating persona-aligned empathy in LLM-based dialogue agents remains challenging. User states are latent, feedback is sparse and difficult to verify in situ, and seemingly supportive turns can still accumulate into trajectories that drift from persona-specific needs. We introduce EMPA, a process-oriented framework that evaluates persona-aligned support as sustained intervention rather than isolated replies. EMPA distills real interactions into controllable, psychologically grounded scenarios, couples them with an open-ended multi-agent sandbox that exposes strategic adaptation and failure modes, and scores trajectories in a latent psychological space by directional alignment, cumulative impact, and stability. The resulting signals and metrics support reproducible comparison and optimization of long-horizon empathic behavior, and they extend to other agent settings shaped by latent dynamics and weak, hard-to-verify feedback.