A Context Reasoner pipeline that cold-starts LLMs on distilled legal reasoning and applies PPO with a rule-based compliance reward improves performance on CI-based legal compliance benchmarks and transfers to general reasoning benchmarks.
Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations
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abstract
The alignment of large language models is usually done by model providers to add or control behaviors that are common or universally understood across use cases and contexts. In contrast, in this article, we present an approach and architecture that empowers application developers to tune a model to their particular values, social norms, laws and other regulations, and orchestrate between potentially conflicting requirements in context. We lay out three main components of such an Alignment Studio architecture: Framers, Instructors, and Auditors that work in concert to control the behavior of a language model. We illustrate this approach with a running example of aligning a company's internal-facing enterprise chatbot to its business conduct guidelines.
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cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning
A Context Reasoner pipeline that cold-starts LLMs on distilled legal reasoning and applies PPO with a rule-based compliance reward improves performance on CI-based legal compliance benchmarks and transfers to general reasoning benchmarks.