Contextual Moral Value Alignment Through Context-Based Aggregation
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929282276655104 |
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| author | Dognin, Pierre Rios, Jesus Luss, Ronny Padhi, Inkit Riemer, Matthew D Liu, Miao Sattigeri, Prasanna Nagireddy, Manish Varshney, Kush R. Bouneffouf, Djallel |
| author_facet | Dognin, Pierre Rios, Jesus Luss, Ronny Padhi, Inkit Riemer, Matthew D Liu, Miao Sattigeri, Prasanna Nagireddy, Manish Varshney, Kush R. Bouneffouf, Djallel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_12805 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Contextual Moral Value Alignment Through Context-Based Aggregation Dognin, Pierre Rios, Jesus Luss, Ronny Padhi, Inkit Riemer, Matthew D Liu, Miao Sattigeri, Prasanna Nagireddy, Manish Varshney, Kush R. Bouneffouf, Djallel Artificial Intelligence Computation and Language 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. |
| title | Contextual Moral Value Alignment Through Context-Based Aggregation |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2403.12805 |