arXiv:2503.00069v2 Announce Type: replace-cross Abstract: Recent progress in large language models (LLMs) has focused on producing responses that meet human expectations and align with shared values - a process coined alignment. However, aligning LLMs remains challenging due to the inherent disconnect between the complexity of human values and the narrow nature of the technological approaches designed to address them. Current alignment methods often lead to misspecified objectives, reflecting the broader issue of incomplete contracts, the impracticality of specifying a contract between a model
The rapid advancement of LLMs, coupled with increasing public scrutiny and regulatory discussions, highlights the urgent need for more robust alignment methodologies.
Improving LLM alignment through societal frameworks is critical for ensuring AI systems operate beneficially, responsibly, and ethically, fostering trust and mitigating risks as they become more integrated into society.
The focus shifts from purely technical alignment fixes to incorporating broader societal and ethical considerations, potentially leading to more nuanced and context-aware AI behavior.
- · AI ethicists and social scientists
- · Developers of robust AI governance frameworks
- · Society at large due to safer AI
- · Developers neglecting ethical considerations
- · AI systems with poor alignment that face public rejection
- · Black-box AI models without transparent alignment processes
More sophisticated and human-centric alignment techniques will be integrated into LLM development pipelines.
This could lead to new regulatory standards and certification processes for AI models based on their alignment with societal values.
Increased societal trust in AI might accelerate its adoption across sensitive sectors, but also amplify the ethical stakes if frameworks fail imperfectly.
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Read at arXiv cs.AI