Evaluating Moral and Ethical Alignment in Large Language Models: A Dungeons & Dragons Benchmark

(1) Background: Large language models (LLMs) are increasingly used to generate morally distinct non-player characters in interactive fiction and tabletop role-playing games. However, prior work shows that reinforcement learning from human feedback (RLHF) safety training suppresses morally extreme expression. Hence, whether models can reliably generate and evaluate speech with a fixed moral–ethical alignment, within an assigned fictional persona, remains an open question. (2) Methods: To explore this issue, eight generator LLMs each produced three-turn role-play conversations for 37 Dungeons & Dragons 5th Edition deities spanning all nine cells of the 3 × 3 moral–ethical alignment grid (888 conversations by design, 885 obtained after one generator’s midexperiment deprecation). Next, eight evaluator LLMs classified each conversation on both alignment axes using structured output evaluation (6951 evaluation records), assessed across 17 analysis metrics. (3) Results: The obtained results can be summarised as follows. Moral-axis accuracy (Good/Neutral/Evil) exceeded ethical-axis accuracy (Lawful/Neutral/Chaotic) by approximately 10 percentage points on average across all 64 generator–evaluator pairs, with the pair-level gap ranging up to 32 points and reversing sign for one generator (nemotron-120b). Moreover, the alignment-cell hardness ranged 27.6-fold, from Lawful Good (0.910) to Neutral Neutral (0.033). Furthermore, a strong good-ward moral drift, consistent with RLHF suppression effects reported in prior work, was observed for nemotron-120b (0.931 of Evil-target outputs classified as Good) and was near-absent for grok-4.1-fast (0.024). Finally, deity wiki prominence did not predict per-deity accuracy after controlling for alignment cell (partial ρ < 0.20, p > 0.20). (4) Conclusions: In summary, good-ward drift, consistent with RLHF suppression of morally extreme expression, turned out to be graded and model-specific. Moreover, iconic characters override it (Cyric, Chaotic Evil: 0.947, nearly matching the top Lawful Good score). Finally, alignment-cell identity is the dominant accuracy predictor, not the deity familiarity, within the role-played persona setting studied here.