LLM-based classification exposes keyword lexicon artifacts in computational social science stance measurement
A new arXiv preprint demonstrates that statistically significant findings in computational social science can be entirely measurement artifacts of keyword-based scoring instruments. Analyzing 85 interviews across four public intellectuals, the authors show that keyword-based certainty scores produce strong correlations (r=0.72–0.93) that collapse or invert when replaced with LLM zero-shot semantic classification on 32,625 sentences. The paper identifies three structural failure modes in keyword lexicons—syntactic blindness, polysemy blindness, and categorical absence—and argues that keyword counts measure lexical co-occurrence tendencies rather than rhetorical stance. The work has implications for the validity of prior NLP-based social science research and for the comparative utility of LLMs as measurement instruments.
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LLMs automate reproducibility assessments in social and behavioral sciences, outperforming human reanalysts
A preprint from arXiv demonstrates that an LLM pipeline can automate reproducibility assessments of published social and behavioral science studies, recovering original effect sizes in 41% of cases (vs. 34% for human reanalysts) and reaching the same qualitative conclusion in 96% of cases (vs. 74% for humans). The study evaluated 76 published studies with predefined claims. The results suggest LLMs could serve as a scalable tool for systematic auditing of empirical research, addressing the resource-intensive nature of traditional reproducibility efforts.
Text Analytics Evaluation Framework: Benchmarking LLMs on Social Media NLP Tasks
Researchers introduce a 470-question evaluation framework to assess LLM performance on aggregated social media text, applied to Twitter datasets across sentiment analysis, hate speech detection, and emotion recognition. Results show performance degrades substantially as input scale exceeds 500 instances, particularly for open-weights models on numerical tasks. Multi-label and target-dependent scenarios also show notable performance drops, and task complexity progressively erodes accuracy from basic semantic identification to comparison and counting operations. The findings point to architectural bottlenecks in current LLMs for rigorous quantitative analysis over large text collections.
LLM psychological profiles are largely measurement artifacts, not model properties
A new arXiv preprint administers a battery of personality and risk-preference instruments to 56 instruction-tuned LLMs alongside large human reference samples, finding that 81-90% of between-model variation is explained by directional response bias rather than the traits the instruments target. The authors introduce the concept of 'response orthogonality' to explain why some instruments appear more reliable than others, and show that apparent psychological profiles can be manufactured through item selection. The findings challenge the validity of using human-designed psychometric tools to characterize LLMs, with direct implications for safety assessment and the use of LLMs as proxies for human participants in research.
LLMs fail to consistently simulate demographic perspective-taking in hate speech annotation
A new arXiv paper evaluates whether persona-conditioned LLMs can replicate how different demographic groups perceive hate speech, testing three dimensions: inter-group disagreement, in-group sensitivity, and vicarious prediction. No model consistently captures all three dimensions, and performance is highly model-dependent rather than emerging reliably from identity prompts alone. Vicarious prompting with Llama 3.1 provides the closest approximation to human disagreement patterns across demographic axes. The findings have implications for using LLMs as proxies for diverse human annotators in content moderation tasks.
Paper challenges LLM expert-level claims by measuring variance and error magnitude in code-based data analysis tasks
A new arXiv paper argues that standard LLM benchmarks overstate model capabilities by focusing on average performance on training-data-adjacent tasks while ignoring response variance and error magnitude. The authors introduce a novel benchmark requiring frontier LLMs to write code for data analysis tasks, comparing results against human expert submissions. Human experts outperformed the frontier LLM on average across multiple metrics and showed lower performance variability. The findings challenge the prevailing narrative that LLMs perform at human-expert level on knowledge economy tasks.
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.
LLMs fail to reliably self-report adversarial prefill attacks, study finds
A new arXiv paper evaluates whether LLMs can recognize that their own prior responses were elicited by adversarial prefill attacks, testing ten open-weight models (3B–70B) across four safety benchmarks. Models claim intent on prefilled responses only 27.3% of the time on average, and introspective signal is largely mediated by refusal-related reasoning. Three LoRA fine-tuning methods (SFT, GRPO, DPO) improve the intention-probe gap but counterintuitively raise attack success rates on most models, suggesting partial and fragile mitigation. The findings raise concerns about the reliability of LLM self-reports in safety-critical contexts.
Diagnostic framework decomposes LLM difficulty on historical Italian and Russian texts
A new arXiv preprint proposes a four-dimensional framework for measuring LLM difficulty on historical language: tokenization cost, surprisal, semantic robustness, and context sensitivity. Evaluated on 17th-century Italian, 19th-century Italian, and 18th-century Russian texts, the study finds that tokenization penalties (25-30% inflation) are similar across languages but predictive difficulty diverges sharply—early modern Italian is 2.4x more surprising than modern Italian while Russian shows only modest increase. Crucially, embedding similarity remains high (>0.85) even when generation is unstable, and a simple temporal context prompt reduces historical surprisal by ~60%. The findings have practical implications for deploying LLMs in digital library and historical document workflows.