The article argues that current evaluations of large language models’ moral competence focus mainly on whether outputs align with human moral values—the so‑called moral value problem—while neglecting the moral norm problem, which concerns the models’ ability to identify and apply context‑sensitive moral norms. It attributes this imbalance to the field’s reliance on descriptive ethics frameworks that emphasize value representation over normative application. The authors review existing benchmarks, highlight three gaps—lack of ground‑truth norm data, insufficient evaluation of intermediate reasoning, and limited focus on context‑relevant features—and propose a research agenda to develop formal normative representations, expert‑annotated datasets, and evaluation protocols that distinguish between value‑level and norm‑level competence.
By Aidan Kierans, Ritam Dutt, Kaley Rittichier, Shiri Dori-Hacohen, Avijit Ghosh
arXiv:2608. 12368v1 Announce Type: new Abstract: Agreement with human judgments is a common proxy for evaluating the alignment of large language models (LLMs).
By Octavian M. Machidon, Alina L. Machidon, Vojko Strahovnik, Mateja Centa Strahovnik, Jonas Miklav\v{c}i\v{c}, Marko Robnik \v{S}ikonja
arXiv:2505.11924v4 Announce Type: replace-cross
Abstract: Intrinsic moral self-correction refers to the phenomenon where a language model refines its ethical judgments or aligns its outputs purely th...
By Yu-Ting Lee, Fu-Chieh Chang, Yu-En Shu, Hui-Ying Shih, Pei-Yuan Wu
arXiv:2605. 22641v3 Announce Type: replace-cross Abstract: Detecting Schwartz values in political text is difficult because implicit cues often depend on surrounding arguments and fine-grained distinctions between neighboring values.
By V\'ictor Yeste, Paolo Rosso
The study investigates how large language models encode moral knowledge by training linear probes for each category of Moral Foundations Theory. It finds that the model’s representations for different moral foundations occupy distinct, largely independent dimensions yet share a common positive component, indicating an integrated but nuanced moral structure. This geometry is consistent across model architectures and scales, emerges early in pre‑training, and reflects corpus statistics rather than the individualizing/binding distinction of the theory.
By Orion Reblitz-Richardson
How do large language models (LLMs) organize moral knowledge? Models detect moral content broadly, but detection is a low bar. We ask whether they go further, distinguishing moral foundations from one...
arXiv:2603. 00048v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly deployed in sensitive applications including psychological support, healthcare, and high-stakes decision-making.
By Erica Coppolillo, Emilio Ferrara
arXiv:2510. 12229v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have been shown to internalize human-like biases during finetuning, yet the mechanisms by which these biases manifest remain unclear.
By Bianca Raimondi, Daniela Dalbagno, Maurizio Gabbrielli
arXiv:2608. 10327v1 Announce Type: new Abstract: Can AI systems be aligned to human values?
By Andrew Smart, Shazeda Ahmed, Jackie Kay, Jimmy Tobin, Kris Shrishak, Abeba Birhane
Large Language Models (LLMs) often struggle to navigate value conflicts when trained with the compressed scalar rewards of Reinforcement Learning from Human Feedback (RLHF). To address this challenge, we investigate how chain-of-thought (CoT) reasoning can help improve performance in this domain.
arXiv:2605. 30036v2 Announce Type: replace Abstract: Large Language Models (LLMs) demonstrate a remarkable capacity to adopt different personas and roles; however, it remains unclear whether they can manifest behavior that adheres to a coherent, human-like value structure.
By Asaf Yehudai, Naama Rozen, Ariel Gera
arXiv:2606. 31213v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly deployed as moral advisors and agents, they need to address dilemmas between two competing values.
By Jongchan Choi, Nari Yang, Sung Soo Park, Jaemin Cho, Han Seoyoung, Haerin Shin, Jun-Hyung Park