Contextual Moral Value Alignment Through Context-Based Aggregation
Pierre Dognin Jesus Rios Ronny Luss Inkit Padhi Matthew D Riemer Miao Liu Prasanna Sattigeri Manish Nagireddy Kush R. Varshney Djallel Bouneffouf
Developing value-aligned AI agents is a complex undertaking and an ongoing
challenge in the field of AI. Specifically within the domain of Large Language
Models (LLMs), the capability to consolidate multiple independently trained
dialogue agents, each aligned with a distinct moral value, into a unified
system that can adapt to and be aligned with multiple moral values is of
paramount importance. In this paper, we propose a system that does contextual
moral value alignment based on contextual aggregation. Here, aggregation is
defined as the process of integrating a subset of LLM responses that are best
suited to respond to a user input, taking into account features extracted from
the user's input. The proposed system shows better results in term of alignment
to human value compared to the state of the art.