SoCRATES: Towards Reliable Automated Evaluation of Proactive LLM Mediation across Domains and Socio-cognitive Variations
arXiv:2606.05563v1 Announce Type: cross Abstract: Evaluating LLM mediators remains challenging, as mediation unfolds as a real-time trajectory shaped by disputants' shifting emotions, intentions, and context. Existing testbeds rely on a few expert-authored domains, vary mainly strategic posture, and score every turn against every topic, introducing off-topic noise. We introduce SoCRATES, a benchmark for evaluating proactive LLM mediators in realistic, multi-domain testbeds. It constructs scenarios from real conflicts through an agentic pipeline across eight domains, probes five socio-cognitive
The rapid advancement and deployment of LLMs necessitate more robust and comprehensive evaluation frameworks, especially for complex tasks like proactive mediation, as current methods are proving insufficient.
A sophisticated reader should care because improved evaluation benchmarks for LLM mediators will accelerate the development of more reliable and trustworthy AI agents capable of handling nuanced human interactions across various domains.
The introduction of a multi-domain, socio-cognitive benchmark like SoCRATES changes how the effectiveness and reliability of proactive LLM mediation are assessed, moving beyond simplistic expert-authored scenarios to real-world complexity.
- · AI researchers and developers
- · Companies deploying LLM-powered mediation tools
- · Users of AI mediation services
- · Ethics and safety standards organizations
- · Developers relying on simplistic LLM evaluation methods
- · Platforms with poorly performing LLM mediators
SoCRATES enables the creation of more robust and unbiased LLM mediators.
Improved mediation capabilities could lead to broader adoption of AI in conflict resolution and complex negotiation scenarios.
The increased reliability of AI mediators might reduce human involvement in certain dispute resolution processes, impacting professional roles.
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Read at arXiv cs.CL