Moral Semantics Survive Machine Translation: Cross-Lingual Evidence from Moral Foundations Corpora
This paper investigates whether LLM-based machine translation can preserve moral semantic content well enough to enable cross-lingual moral values classification, using Polish as a test case with ~50k annotated social media posts. A four-method validation pipeline (LaBSE embedding similarity, CKA, LLM-as-judge, and classifier parity) shows mean cosine similarity of 0.86 and AUC gaps of only 0.01–0.02 across Moral Foundations categories. The results suggest machine translation is a practical path to extending moral values NLP research to under-resourced languages, with expected generalization to related Slavic languages.
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Calibrated LLM annotation and encoder transfer for measuring human values in social media text
A new arXiv preprint investigates how different LLMs, prompts, and instruction languages operationalize Schwartz's theory of basic human values when annotating non-English social media posts. The authors evaluate annotation quality beyond standard F1 metrics, examining structural alignment, error structure, and confidence-ambiguity relations, finding that iterative prompt calibration reduces misattributions. They also demonstrate that LLM annotations can be transferred to a smaller encoder model via soft-label training, preserving theory-grounded value interpretations and uncertainty information.
First Komi-Yazva–Russian parallel corpus and LLM translation evaluation protocol for endangered low-resource language
Researchers introduce the first Komi-Yazva–Russian parallel corpus of 457 aligned sentence pairs from 74 narrative texts, paired with a rigorous evaluation protocol for studying LLM translation under extreme data scarcity. The protocol includes story-level cross-validation, deterministic retrieval-based few-shot prompting, and both reference-based and judge-based metrics to ensure leakage-aware, reproducible evaluation. Results show LLMs produce non-trivial translations but performance varies strongly by model family; retrieval-based few-shot prompting consistently outperforms zero-shot, though gains plateau quickly. The work frames the corpus as both a dataset contribution and a reproducible testbed for endangered-language machine translation research.
Towards Reliable Multilingual LLMs-as-a-Judge: An Empirical Study
This paper systematically investigates strategies for extending LLM-based automatic evaluation (LLMs-as-a-Judge) to multilingual settings, covering high-, mid-, and low-resource languages (English, Spanish, Basque). The authors compare instruction translation, monolingual vs. multilingual supervision, and model size, finding that fine-tuned smaller models can match proprietary models when in-domain data is available, while zero-shot larger models are preferable out-of-domain. Two meta-evaluation datasets are extended to Spanish and Basque, and all data and code are publicly released.
Systematic Study of Schwartz Value Detection in Political Texts: Context, Scale, and Moral Knowledge
This paper investigates when additional context, larger models, or retrieved moral knowledge improve detection of Schwartz human values in political text using the ValueEval benchmark format. Key findings show that full-document context helps supervised DeBERTa encoders (+3.8–4.8 macro-F1) but not zero-shot LLMs, while RAG with a curated moral knowledge base consistently benefits all model families under early fusion. Scaling model size does not guarantee gains, and simple early fusion outperforms more complex RAG variants. The study recommends jointly evaluating context, knowledge, and model family rather than assuming larger inputs or models universally improve value-sensitive NLP.
Benchmarking Local LLMs for Confidential Translation Workflows
This paper evaluates locally runnable LLMs (via Ollama) for offline, privacy-constrained translation workflows targeting freelance translators and smaller language service providers. The authors expand their Reeve Foundation corpus to include German and Simplified Chinese, then benchmark local models across four language directions against commercial NMTs (DeepL, Baidu), a frontier LLM (GPT-5.2), and professional local NMT systems. Results show substantial performance variation by language direction and model size, with the best local LLMs matching or exceeding local NMT systems and the frontier LLM, though falling short of top commercial NMTs. The study supports the viability of local LLMs for confidentiality-sensitive translation use cases.
Large-scale social media analysis reveals stakeholder conflicts over machine translation priorities
Researchers analyze 79,286 social media posts from Reddit, Facebook, Bluesky, and Mastodon (2019–2025) to compare how four communities—AI developers, professional translators, language learners, and language service providers—discuss machine translation. The study finds significant disagreements and polarized sentiments across groups, with AI researchers framing MT as a technical benchmark problem while non-AI users prioritize quality nuances, trust, reliability, and social concerns. The work argues for redirecting MT research toward community-identified needs rather than benchmark performance alone.
Tracing the Emergence of Human-Like Pragmatic Reasoning in LLMs Across Languages
Researchers conducted a population-matching experiment evaluating 25 LLMs on conditional inference tasks across four languages, comparing model behavior to matched human populations. The study finds that LLMs function as accurate semantic operators but systematically fail to capture pragmatic enrichments—context-sensitive inferences beyond literal logical meaning—that humans apply effortlessly. Model performance on pragmatic reasoning is not predicted by open vs. closed weights, training orientation, or architecture type, suggesting pragmatic reasoning remains an emergent and unreliable capability. The findings contribute to ongoing debates about whether LLMs reason like humans or merely approximate surface-level linguistic patterns.
Adversarial robustness and safety alignment in multilingual multimodal LLMs: cross-lingual vulnerability and 'safety-by-failure'
A systematic study evaluates adversarial robustness and safety alignment of multimodal LLMs across 12 languages, finding that adversarial images optimized in one language transfer to others (cross-lingual transferability). The paper introduces the concept of 'safety-by-failure': low-resource languages appear safer not due to genuine alignment but because models fail to comprehend harmful instructions in those languages. Models like Qwen3-VL that integrate multilingual capability throughout training (rather than only at instruction tuning) show genuine cross-lingual safety with active refusal. The findings challenge the assumption that low-resource language safety metrics reflect real alignment.