mlsanityData to 7 Oct 2026

Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations

arXiv 2403.09704 ↗First posted 8 Mar 2024cs.CL cs.AI cs.LGcited by 10

On the map: LLM alignment / RLHF / preference optimisation → Human values and AI feedback alignment

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.

Key facts. Cited by 10 papers in the map (data through 7 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.

Builds on

11 of 17 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024A Roadmap to Pluralistic Alignment7 Feb
  2. 2023Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback9 Mar
  3. 2022Constitutional AI: Harmlessness from AI Feedback15 Dec

Other references · 8

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  2. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
  3. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  4. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  5. 2022Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learnedcited by 702
  6. 2022Unnatural Instructions: Tuning Language Models with (Almost) No Human Laborcited by 395
  7. 2023Unveiling Safety Vulnerabilities of Large Language Modelscited by 26
  8. 2023Decolonial AI Alignment: Openness, Vi'sedsa-Dharma, and Including Excluded Knowledgescited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 7; 2026: 2.

10 papers.

  1. 2025Towards Enforcing Company Policy Adherence in Agentic Workflowscited by 17
  2. 2025The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systemscited by 14
  3. 2025Improving Consistency in Large Language Models through Chain of Guidancecited by 13
  4. 2025LLM-Based Social Simulations Require a Boundarycited by 11
  5. 2025Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learningcited by 7
  6. 2024Value Alignment from Unstructured Textcited by 4
  7. 2025Scopes of Alignmentcited by 3
  8. 2025GEM: Generative Entropy-Guided Preference Modeling for Few-shot Alignment of LLMscited by 0
  9. 2026Granite.Trust Policy Tools: Shareable, Actionable Policies for Generative AI Applicationscited by 0
  10. 2026From Alignment to Access Control: A Framework for GenAI Policy Enforcementcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Societal Alignment Frameworks Can Improve LLM Alignment
  2. 2026SpecAlign: Efficient Specification-Grounded Alignment of Large Language Models via Synthetic Data
  3. 2023From Instructions to Intrinsic Human Values -- A Survey of Alignment Goals for Big Models
  4. 2025Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges
  5. 2025A Survey on Personalized Alignment -- The Missing Piece for Large Language Models in Real-World Applications
  6. 2025SPRI: Aligning Large Language Models with Context-Situated Principles
  7. 2026Auditing Proprietary Alignment in Large Language Models: A Comparative Framework Without a Ground-Truth Standard
  8. 2026Are Aligned Large Language Models Still Misaligned?