arXiv AI
Sep 4

Representational alignment yields generalizable safety in language models

The paper argues that aligning large language models (LLMs) at the level of latent representations—specifically by matching their internal categorization of moral concepts to human prototype-based judgments—improves safety. Current alignment methods that focus on observable responses fail to preserve fine-grained moral categorization, leaving models vulnerable to adversarial rephrasings. By optimizing representational similarity, the authors demonstrate that LLMs can maintain more robust moral categorization and exhibit better adversarial robustness across multiple benchmarks and model sizes.

By Lingyu Li, Yan Teng, Yingchun Wang, Xia Hu
arXiv AI
Oct 5

Morality is Contextual: Learning Interpretable Moral Contexts from Human Data with Probabilistic Clustering and Large Language Models

The paper introduces COMETH, a framework that learns interpretable moral contexts from human judgments using probabilistic clustering and large language models. It builds a dataset of 300 scenarios involving six core actions and three moral rules, and employs an LLM-based preprocessing pipeline to cluster actions and scenarios. COMETH achieves roughly double the alignment with human judgments compared to end‑to‑end LLM prompting, while providing transparent explanations of the contextual features driving its predictions.

By Geoffroy Morlat, Marceau Nahon, Augustin Chartouny, Raja Chatila, Ismael T. Freire, Mehdi Khamassi
arXiv Computation and Language
Oct 15

Moral Combat: Exploiting Moral Foundations Theory to Jailbreak LLMs

The paper "Moral Combat: Exploiting Moral Foundations Theory to Jailbreak LLMs" presents a multi‑agent framework that uses Moral Foundations Theory to reframe harmful prompts in a way that aligns with different moral foundations, thereby enabling large language models to bypass safety guardrails. The authors demonstrate that this approach achieves high single‑trial jailbreak rates across several open‑source and proprietary models, with success increasing under repeated attempts. Their analysis also reveals that existing safety benchmarks are unevenly distributed across moral foundations, focusing on those foundations where models already show biases.

By Adel Khorramrouz, Yi-Chia Wang, Sharon Levy
arXiv Computation and Language
Sep 4

Less Is Moral: A CHARMing Framework for Moral Foundations Detection in Endorsement Behaviour

The paper introduces CHARM, a lightweight fine‑tuned language model framework for detecting moral foundations in text. CHARM combines MAC cross‑attention, rationale alignment, and hate‑speech modulation to operationalize distinct psychological constructs, achieving up to 15.3% higher AUC in‑domain and outperforming supervised baselines on all out‑of‑domain datasets. The authors demonstrate CHARM’s scalability by applying it to large‑scale COVID‑19 Twitter data, revealing a strong link between moral value alignment and online endorsement behavior.

By Huixiang Fu, Marian-Andrei Rizoiu
Hugging Face Trending Papers
Aug 27

How Language Models Organize and Structure Moral Knowledge

The paper investigates how large language models encode moral knowledge by training linear probes for each Moral Foundations Theory category and analyzing their geometric relationships. It finds that the model’s moral directions are largely independent yet share a common component, indicating integration rather than collapse into a single detector. This structure is consistent across architectures, emerges early in pre‑training, and reflects corpus statistics rather than the individualizing/binding distinction of Moral Foundations Theory.