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This paper compares rule-based and example-based in-context learning in LLMs, finding that rules generally lead to more reliable learning across diverse tasks, with implications for model and task properties.
This paper introduces IDRAAK, an interpretable framework for detecting semantic drift in translated technical requirements using a Semantic Requirement Representation and multiple detection workflows, showing that simple few-shot prompting outperforms more complex multi-agent approaches.
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.