mlsanityData to 14 Oct 2026

Integrating Emotional and Linguistic Models for Ethical Compliance in Large Language Models

arXiv 2405.07076 ↗First posted 11 May 2024cs.CL cs.AIcited by 1

On the map: LLM safety and security → Safety alignment, refusal and guardrails

Abstract (arXiv)

This research develops advanced methodologies for Large Language Models (LLMs) to better manage linguistic behaviors related to emotions and ethics. We introduce DIKE, an adversarial framework that enhances the LLMs' ability to internalize and reflect global human values, adapting to varied cultural contexts to promote transparency and trust among users. The methodology involves detailed modeling of emotions, classification of linguistic behaviors, and implementation of ethical guardrails. Our innovative approaches include mapping emotions and behaviors using self-supervised learning techniques, refining these guardrails through adversarial reviews, and systematically adjusting outputs to ensure ethical alignment. This framework establishes a robust foundation for AI systems to operate with ethical integrity and cultural sensitivity, paving the way for more responsible and context-aware AI interactions.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Safety alignment, refusal and guardrails, one of 12 directions in LLM safety and security. Builds on 15 of its 61 references that are in the map. First posted on arXiv 11 May 2024.

Builds on

15 of 61 references.

References · 15

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  3. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  4. 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
  5. 2017Deep reinforcement learning from human preferencescited by 3,912
  6. 2020Learning to summarize from human feedbackcited by 2,588
  7. 2023A General Theoretical Paradigm to Understand Learning from Human Preferencescited by 912
  8. 2021GLaM: Efficient Scaling of Language Models with Mixture-of-Expertscited by 897
  9. 2023RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedbackcited by 625
  10. 2015Policy Distillationcited by 440
  11. 2023SLiC-HF: Sequence Likelihood Calibration with Human Feedbackcited by 401
  12. 2023Role-Play with Large Language Modelscited by 294
3 more
  1. 2024Generalized Preference Optimization: A Unified Approach to Offline Alignmentcited by 157
  2. 2023Prompting Large Language Models With the Socratic Methodcited by 43
  3. 2024Relative Preference Optimization: Enhancing LLM Alignment through Contrasting Responses across Identical and Diverse Promptscited by 36

Built on by

Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024Ensuring Ground Truth Accuracy in Healthcare with the EVINCE frameworkcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment
  2. 2025Diverse Human Value Alignment for Large Language Models via Ethical Reasoning
  3. 2025Cultural Bias in Large Language Models: Evaluating AI Agents through Moral Questionnaires
  4. 2026Unsupervised Elicitation of Moral Values from Language Models
  5. 2024ValueCompass: A Framework for Measuring Contextual Value Alignment Between Human and LLMs
  6. 2025AI shares emotion with humans across languages and cultures
  7. 2026Jailbreaking Large Language Models with Morality Attacks
  8. 2020Aligning AI With Shared Human Values