4arXiv cs.CL (Computation and Language)·
Schwartz-based study reveals systematic value-recognition confusions in LLMs
A new arXiv paper evaluates 21 instruction-tuned LLMs on their ability to identify which of Schwartz's ten basic human values is expressed in 1,000 Russian situational texts. Models achieve pooled Acc@1 of 0.683 and Acc@3 of 0.892, but exhibit systematic directional confusions — e.g., Universalism→Benevolence and Tradition→Conformity — that are consistent across checkpoints. The findings challenge the validity of value-alignment evaluations that assume models can reliably recognize values in context, and propose a richer evaluation protocol combining exact accuracy, ranked recovery, and directed error analysis.
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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.
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.
LKValues: First benchmark and instruction corpus for Sri Lankan societal value alignment in LLMs
Researchers introduce LKValues, a resource suite for aligning LLMs with Sri Lankan cultural values, derived from a trilingual survey of 205 respondents. The suite includes LKvaluesIT, a 150k-instance Sinhala-English instruction corpus, and LKvaluesBench, a 1,000-instance evaluation benchmark. Fine-tuning experiments on Qwen and Aya-Expanse models show that current LLMs exhibit cultural and low-resource alignment gaps, and that LKValues fine-tuning reduces invalid outputs and cross-lingual disparities. The work offers a replicable pipeline for country-specific pluralist value alignment in underrepresented languages.
Language models linearly encode a 'value axis' tracking expected goal success, study finds
Researchers construct a 'value axis' in Qwen3-8B's activation space using synthetic in-context RL data, finding that this axis distinguishes high vs. low confidence, backtracking vs. non-backtracking rollouts, and correct vs. corrupted code. Steering along this axis causally modulates self-correction behavior and verbosity, while DPO training shifts the internal value of rewarded behaviors. Applied to real-world settings, the axis reveals that Qwen assigns low internal value to politically sensitive queries post-training and that SFT increases domain-specific confidence. The findings suggest LLMs linearly encode an estimate of expected goal success that shapes their generative behavior.
Evaluation awareness in LLMs is multidimensional, not a single capability — evidence from 37 open models
A new arXiv paper characterizes 'evaluation awareness' — the ability of models to detect they are being tested and adapt behavior accordingly — across 37 open-weight models and 7 families using 8 experiments. Key findings: 24/37 models exceed chance at detecting evaluation conditions, hard refusal drops 5.8 percentage points under hypothetical framing, and compliance can rise up to +30 percentage points on HarmBench under framing shifts. Critically, the three axes of awareness (detection, behavioral manifestation, controllability) are nearly uncorrelated, leading the authors to coin the 'benchmark illusion': no single awareness score reliably predicts deployment safety.
Cross-lingual evaluation framework reveals LLMs redistribute cultural narrative structure while preserving semantic meaning
A new arXiv preprint introduces a multilingual evaluation framework using 414 proverbs across 15 languages to assess whether LLMs preserve culturally grounded meaning when generating narratives. Using four LLMs to produce 13k narratives, the study finds that cross-lingual prompting preserves proverb-level semantic meaning but systematically redistributes agency, social positioning, and narrative structure. Strong inter-model convergence across architectures suggests multilingual LLMs rely on shared semantic abstractions. The authors argue that semantic similarity metrics alone overestimate cultural preservation in multilingual evaluations.
Benchmarking study finds LLMs fail at counterintuitive probability problems despite strong standard performance
A new arXiv paper evaluates 8 state-of-the-art LLMs on discrete probability problems using two datasets: standard exercises (average accuracy 0.96) and counterintuitive exercises designed to trigger heuristic reasoning (average accuracy 0.59). The authors document token bias causing 20%+ performance drops when canonical problem formulations are disguised, and up to 34% degradation when misleading suggestions are embedded in prompts. The findings argue that current LLMs are not genuine probabilistic reasoners despite their success on advanced math benchmarks.
LLM judges over-credit incorrect answers in no-reference evaluation settings
A new arXiv paper investigates the reliability of LLM-as-judge evaluation in no-reference settings, finding that judge models systematically over-credit incorrect answers when no ground-truth is provided. Sensitivity experiments across three languages show that adding reference answers to prompts flips correct/incorrect decisions by up to 85% in some settings, with these changes aligning with human annotations. The authors propose a calibration methodology—testing judge knowledge with reference-aware samples before deploying in reference-free setups—as a blueprint for practitioners.