CRAFT is a Pareto-front prompt optimizer that allocates scarce LLM validation calls to candidates near the current front using accuracy- and cost-oriented generators plus NSGA-II retention.
Zero-Shot Cross-Lingual Transfer with Meta Learning
3 Pith papers cite this work, alongside 12 external citations. Polarity classification is still indexing.
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Across 20 open LLMs, shared multilingual neurons grow in number and per-neuron importance over release generations, which the authors interpret as evidence of language-agnostic abstract thought and use to guide neuron-targeted multilingual training.
The survey identifies a key tension in multilingual vision-language models between language neutrality via contrastive learning and cultural awareness via diverse data, with most benchmarks relying on translation-based evaluation.
citing papers explorer
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CRAFT: Cost-aware Refinement And Front-aware Tuning of Prompts
CRAFT is a Pareto-front prompt optimizer that allocates scarce LLM validation calls to candidates near the current front using accuracy- and cost-oriented generators plus NSGA-II retention.
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The Emergence of Abstract Thought in Large Language Models Beyond Any Language
Across 20 open LLMs, shared multilingual neurons grow in number and per-neuron importance over release generations, which the authors interpret as evidence of language-agnostic abstract thought and use to guide neuron-targeted multilingual training.
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Multilingual Vision-Language Models, A Survey
The survey identifies a key tension in multilingual vision-language models between language neutrality via contrastive learning and cultural awareness via diverse data, with most benchmarks relying on translation-based evaluation.