mlsanityData to 7 Oct 2026

A Moral Imperative: The Need for Continual Superalignment of Large Language Models

arXiv 2403.14683 ↗First posted 13 Mar 2024cs.CY cs.AI cs.CLcited by 5

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

Abstract (arXiv)

This paper examines the challenges associated with achieving life-long superalignment in AI systems, particularly large language models (LLMs). Superalignment is a theoretical framework that aspires to ensure that superintelligent AI systems act in accordance with human values and goals. Despite its promising vision, we argue that achieving superalignment requires substantial changes in the current LLM architectures due to their inherent limitations in comprehending and adapting to the dynamic nature of these human ethics and evolving global scenarios. We dissect the challenges of encoding an ever-changing spectrum of human values into LLMs, highlighting the discrepancies between static AI models and the dynamic nature of human societies. To illustrate these challenges, we analyze two distinct examples: one demonstrates a qualitative shift in human values, while the other presents a quantifiable change. Through these examples, we illustrate how LLMs, constrained by their training data, fail to align with contemporary human values and scenarios. The paper concludes by exploring potential strategies to address and possibly mitigate these alignment discrepancies, suggesting a path forward in the pursuit of more adaptable and responsive AI systems.

Key facts. Cited by 5 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 23 of its 39 references that are in the map. First posted on arXiv 13 Mar 2024.

Builds on

23 of 39 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision14 Dec

Other references · 22

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 2,914
  4. 2022Large Language Models Encode Clinical Knowledgecited by 1,807
  5. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  6. 2016Concrete Problems in AI Safetycited by 1,582
  7. 2023Aligning Large Multimodal Models with Factually Augmented RLHFcited by 703
  8. 2023Bias and Fairness in Large Language Models: A Surveycited by 626
  9. 2023Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignmentcited by 394
  10. 2023Large Language Model Alignment: A Surveycited by 241
  11. 2023Secrets of RLHF in Large Language Models Part I: PPOcited by 226
  12. 2021Towards Continual Knowledge Learning of Language Modelscited by 172
10 more
  1. 2024Continual Learning for Large Language Models: A Surveycited by 144
  2. 2023Full Parameter Fine-tuning for Large Language Models with Limited Resourcescited by 134
  3. 2023Large Language Models for Robotics: A Surveycited by 132
  4. 2024How to Train Data-Efficient LLMscited by 91
  5. 2024Datasets for Large Language Models: A Comprehensive Surveycited by 69
  6. 2024Understanding LLMs: A Comprehensive Overview from Training to Inferencecited by 58
  7. 2022Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generationcited by 17
  8. 2023Designing LLM Chains by Adapting Techniques from Crowdsourcing Workflowscited by 17
  9. 2019Learning and Planning for Time-Varying MDPs Using Maximum Likelihood Estimationcited by 10
  10. 2023Weathering Ongoing Uncertainty: Learning and Planning in a Time-Varying Partially Observable Environmentcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 0; 2026: 1.

5 papers.

  1. 2024Weak-to-Strong Reasoningcited by 32
  2. 2024Your Weak LLM is Secretly a Strong Teacher for Alignmentcited by 20
  3. 2024Hallucination Detection in Foundation Models for Decision-Making: A Flexible Definition and Review of the State of the Artcited by 17
  4. 2026Beyond Static Models: An Evolving Framework for Continual Learning in Large Language Models across Training Stagescited by 4
  5. 2024Supertrust foundational alignment: mutual trust must replace permanent control for safe superintelligencecited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2026Toward a Theory of Value in AI Alignment
  2. 2025Diverse Human Value Alignment for Large Language Models via Ethical Reasoning
  3. 2023From Instructions to Intrinsic Human Values -- A Survey of Alignment Goals for Big Models
  4. 2024ValueCompass: A Framework for Measuring Contextual Value Alignment Between Human and LLMs
  5. 2025Dynamic Alignment for Collective Agency: Toward a Scalable Self-Improving Framework for Open-Ended LLM Alignment
  6. 2024Position: Towards Bidirectional Human-AI Alignment
  7. 2025Decoding Alignment: A Critical Survey of LLM Development Initiatives through Value-setting and Data-centric Lens
  8. 2024Strong and weak alignment of large language models with human values