# Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations

arXiv: https://arxiv.org/abs/2403.09704 · HTML: https://mlsanity.com/paper/2403.09704/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 11 of its 17 references that are in the map.
- First posted on arXiv 8 Mar 2024.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Human values and AI feedback alignment](https://mlsanity.com/alignment/human-values-and-ai-feedback-alignment/)

## Builds on (most cited first, 11 of 11)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned](https://mlsanity.com/paper/2209.07858/) (2022)
- [Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor](https://mlsanity.com/paper/2212.09689/) (2022)
- [A Roadmap to Pluralistic Alignment](https://mlsanity.com/paper/2402.05070/) (2024)
- [Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback](https://mlsanity.com/paper/2303.05453/) (2023)
- [Unveiling Safety Vulnerabilities of Large Language Models](https://mlsanity.com/paper/2311.04124/) (2023)
- [Decolonial AI Alignment: Openness, Vi'sedsa-Dharma, and Including Excluded Knowledges](https://mlsanity.com/paper/2309.05030/) (2023)

## Built on by (most cited first, 10 of 10)

- [Towards Enforcing Company Policy Adherence in Agentic Workflows](https://mlsanity.com/paper/2507.16459/) (2025)
- [The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systems](https://mlsanity.com/paper/2507.02097/) (2025)
- [Improving Consistency in Large Language Models through Chain of Guidance](https://mlsanity.com/paper/2502.15924/) (2025)
- [LLM-Based Social Simulations Require a Boundary](https://mlsanity.com/paper/2506.19806/) (2025)
- [Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning](https://mlsanity.com/paper/2505.14585/) (2025)
- [Value Alignment from Unstructured Text](https://mlsanity.com/paper/2408.10392/) (2024)
- [Scopes of Alignment](https://mlsanity.com/paper/2501.12405/) (2025)
- [GEM: Generative Entropy-Guided Preference Modeling for Few-shot Alignment of LLMs](https://mlsanity.com/paper/2511.13007/) (2025)
- [Granite.Trust Policy Tools: Shareable, Actionable Policies for Generative AI Applications](https://mlsanity.com/paper/2608.23870/) (2026)
- [From Alignment to Access Control: A Framework for GenAI Policy Enforcement](https://mlsanity.com/paper/2609.26682/) (2026)

## Abstract (arXiv)

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.