Author

Ashvi Soni

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Jul 2026

When Does Few-Shot Prompting Help? A Systematic Empirical Study of Shot-Count Effects Across Model Scale, Architecture, and Output Parsing Robustness

Few-shot prompting, the practice of prepending a small number of input-output demonstration pairs to a query before presenting it to a large language model (LLM), is among the most widely adopted inference-time techniques in NLP. Yet little systematic work investigates how shot count interacts with model scale, archite...

Ayush Dwivedi, Ashvi Soni · arXiv.org · 0 citations

Jul 2026

Beyond Exact Match: How Evaluation Methodology Dominates Model Choice in LLM-Based Product Attribute Extraction

Large language models (LLMs) have become a default choice for structured product attribute extraction in e-commerce pipelines, with practitioners reporting widely varying performance across models, datasets, and prompting strategies. This paper presents a controlled empirical study comparing four prompting strategies -...

Ayush Dwivedi, Ashvi Soni · arXiv.org · 0 citations