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.
arXiv:2605. 22660v2 Announce Type: replace-cross Abstract: Moral language is subtle and culturally variable, making it difficult to translate faithfully across languages.
arXiv:2603. 23841v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) are increasingly used as primary sources of information, their potential for political bias may impact their objectivity.
PolERo presents a new dataset of 3,574 Romanian question‑answer pairs from presidential transcripts, annotated for political evasion using a two‑level taxonomy of response clarity and fine‑grained evasion strategies. The study evaluates various classification methods—including TF‑IDF baselines, fine‑tuned encoders, a sliding‑window encoder, and zero/few‑shot LLM prompting—under matched conditions. Cross‑lingual transfer experiments via joint bilingual training and machine‑translation augmentation reveal that fine‑tuned encoders perform competitively, transfer is asymmetric, and ambivalent evasion categories with pragmatic cues remain the most challenging across all models.
arXiv:2505. 24539v4 Announce Type: replace-cross Abstract: We present a study on how and where personas -- defined by distinct sets of human characteristics, values, and beliefs -- are encoded in the representation space of large language models (LLMs).
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.
arXiv:2609.27165v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to measure public value orientations from long social media posts, yet such posts often mix backgr...
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.
arXiv:2606. 12754v1 Announce Type: cross Abstract: Are large language models (LLMs) bad at capturing human judgment?
Polish ModernBERT is a new family of encoder‑only Transformers for Polish, offering Base and Large models with both 512‑token and 8K‑token context variants. The authors adapted the ModernBERT pretraining recipe through staged selection experiments and released a long‑context benchmark covering legal topic classification, ideological decision‑direction prediction, factual‑consistency assessment over literary plot summaries, and human‑rights violation assessment. Across 30 tasks, Polish ModernBERT outperforms existing Polish encoders, achieving 83.99 and 85.11 on the Base‑8K and Large‑8K models, respectively, and improving long‑context performance while using fewer parameters and lower memory and latency.
arXiv:2607. 27232v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly shaping how we consume information and form our worldview.
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.
Understanding moral values in social media text offers insight into moral judgement formation, and supervised NLP models trained on crowdsourced data have achieved strong classification performance. However, most approaches simplify the problem by aggregating multiple annotators' labels into a single "ground truth", overlooking the inherent subjectivity of the task.