Skin-in-the-Game: Decision Making via Multi-Stakeholder Alignment in LLMs

Autor:	Sel, Bilgehan, Shanmugasundaram, Priya, Kachuee, Mohammad, Zhou, Kun, Jia, Ruoxi, Jin, Ming
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Large Language Models (LLMs) have shown remarkable capabilities in tasks such as summarization, arithmetic reasoning, and question answering. However, they encounter significant challenges in the domain of moral reasoning and ethical decision-making, especially in complex scenarios with multiple stakeholders. This paper introduces the Skin-in-the-Game (SKIG) framework, aimed at enhancing moral reasoning in LLMs by exploring decisions' consequences from multiple stakeholder perspectives. Central to SKIG's mechanism is simulating accountability for actions, which, alongside empathy exercises and risk assessment, is pivotal to its effectiveness. We validate SKIG's performance across various moral reasoning benchmarks with proprietary and opensource LLMs, and investigate its crucial components through extensive ablation analyses. Comment: ACL 2024, long paper
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.12933 Zobrazit plný text záznamu View this record from Arxiv