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This paper measures tokenizer fertility across 25 European languages on parallel text, revealing a 2.5x spread from English to Greek/Maltese, with Ukrainian paying a 15-18% penalty. It demonstrates domain invariance of fertility rankings, analyzes subword fragmentation, and evaluates cross-lingual few-shot effects.
PromptAudit is a controlled evaluation framework that isolates the effects of prompt formulations on LLM-based vulnerability detection, finding that chain-of-thought prompting achieves the best overall performance while prompt sensitivity must be treated as a first-class system property.
SwanNLP presents an LLM-based framework for plausibility scoring in narrative word sense disambiguation at SemEval-2026 Task 5, using structured reasoning and dynamic few-shot prompting to predict human-perceived plausibility of word senses in short stories. The work demonstrates that commercial large-parameter LLMs with few-shot prompting and model ensembling effectively replicate human judgment patterns in realistic narrative contexts.