Contextual Moral Value Alignment Through Context-Based Aggregation
<|reference_start|>Contextual Moral Value Alignment Through Context-Based Aggregation: 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.<|reference_end|>
@article{dognin2024contextual,
    title={Contextual Moral Value Alignment Through Context-Based Aggregation},
    author={Pierre Dognin, Jesus Rios, Ronny Luss, Inkit Padhi, Matthew D Riemer,
  Miao Liu, Prasanna Sattigeri, Manish Nagireddy, Kush R. Varshney and Djallel
  Bouneffouf},
    journal={arXiv preprint arXiv:2403.12805},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12805},
    primaryClass={cs.AI cs.CL}
}