Decoder-only transformers prefer left-branching order on artificial PCFG languages but increasingly favor SVO right-branching order on larger natural language corpora, indicating the preference is data-driven.
m GPT : Few-Shot Learners Go Multilingual
9 Pith papers cite this work, alongside 34 external citations. Polarity classification is still indexing.
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Dedicated monolingual models and tokenizers for Tamil, Telugu, Kannada, and Malayalam outperform a shared multilingual model and mGPT on tokenizer efficiency and most fine-tuned tasks, but the evaluation is single-run and partly unequal-budget.
A systematic benchmark of multilingual authorship attribution shows fine-tuned LLM detectors exceed 0.9 macro F1 in-language but transfer poorly across languages, with Russian training generalizing better than English.
Across 35 models and two languages, language models perform at or below chance at telling possible-but-unlikely events from impossible ones when semantic relatedness conflicts with possibility.
A new 11-task Maltese benchmark shows that 55 large language models lag behind small fine-tuned models, with prior Maltese exposure the strongest predictor.
Across ten languages, mutual information between orthographic words and pitch curves is higher in tonal than in pitch-accent and stress-accent languages, consistent with a gradient view of prosodic typology.
Multi-agent debate with tit-for-tat arguments and a judge LLM improves reasoning by preventing LLMs from locking into incorrect initial solutions.
A survey of NLU diagnostics benchmarks finds no shared naming convention or standard set of linguistic phenomena, and asks whether the field should build an ISO-like evaluation standard.
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