SpecAlign: Efficient Specification-Grounded Alignment of Large Language Models via Synthetic Data
As large language models (LLMs) are increasingly deployed in real-world applications, alignment is no longer governed by a single universal notion of safety or helpfulness, but instead by provider- or application-specific model specifications. These specifications are typically long, structured, and frequently updated, yet existing alignment pipelines lack a systematic mechanism to operationalize them as training signals. In this paper, we propose specification-grounded alignment, a new alignmen
Record details
Published: 15 June 2026
Source: arXiv
Category: Research
Topics: Safety & alignment
Retrieved: 14 July 2026
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ethics.ai (15 June 2026), “SpecAlign: Efficient Specification-Grounded Alignment of Large Language Models via Synthetic Data,” evidence record 975, https://ethics.ai/record/975 (originally published by arXiv).
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