Proposes a task taxonomy for functional diversity in LLM outputs, validates it via user study, introduces targeted sampling to boost diversity only where needed, and presents evidence that the diversity-quality tradeoff may be an artifact of task-agnostic measurement.
arXiv preprint arXiv:2507.09650 , year=
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A cross-evaluation setup with native-speaker SME rubrics benchmarks LLMs on underrepresented Arabic dialects and quantifies automated judge bias and cultural-reasoning gaps.
PEBS applies Morris-James-Stein empirical-Bayes shrinkage to per-rater affine calibrators in RLHF, cutting within-user held-out RMSE by 8.58% on PRISM and 9.66% on PluriHarms versus pooled baselines.
Preregistered behavioral study identifies a speedup illusion where users overestimate time savings from AI assistance on cognitive tasks despite no actual difference in completion times.
Align-Cultura introduces the CULTURAX dataset and shows that culturally fine-tuned LLMs improve joint HHH scores by 4-6%, cut cultural failures by 18%, and gain 10-12% efficiency with minimal leakage.
Positive Alignment is defined as AI systems that support human flourishing pluralistically while staying safe and cooperative, presented as a necessary complement to existing safety-focused alignment research.
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Task-Dependent Evaluation of LLM Output Homogenization: A Taxonomy-Guided Framework
Proposes a task taxonomy for functional diversity in LLM outputs, validates it via user study, introduces targeted sampling to boost diversity only where needed, and presents evidence that the diversity-quality tradeoff may be an artifact of task-agnostic measurement.
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Benchmarking Frontier LLMs on Arabic Cultural and Sociolinguistic Knowledge: A Cross-Evaluation Framework with Human SME Ground Truth
A cross-evaluation setup with native-speaker SME rubrics benchmarks LLMs on underrepresented Arabic dialects and quantifies automated judge bias and cultural-reasoning gaps.
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PEBS: Per-rater Empirical-Bayes Shrinkage for RLHF Reward-Model Calibration
PEBS applies Morris-James-Stein empirical-Bayes shrinkage to per-rater affine calibrators in RLHF, cutting within-user held-out RMSE by 8.58% on PRISM and 9.66% on PluriHarms versus pooled baselines.
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Cognitive offloading and the speedup illusion in human-AI interaction
Preregistered behavioral study identifies a speedup illusion where users overestimate time savings from AI assistance on cognitive tasks despite no actual difference in completion times.
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AlignCultura: Towards Culturally Aligned Large Language Models?
Align-Cultura introduces the CULTURAX dataset and shows that culturally fine-tuned LLMs improve joint HHH scores by 4-6%, cut cultural failures by 18%, and gain 10-12% efficiency with minimal leakage.
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Positive Alignment: Artificial Intelligence for Human Flourishing
Positive Alignment is defined as AI systems that support human flourishing pluralistically while staying safe and cooperative, presented as a necessary complement to existing safety-focused alignment research.