Author

Ayush Dwivedi

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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

A controlled study of five LLMs across six shot-count configurations on the AG News four-class benchmark demonstrates that the relationship between shot count and classification performance is not monotonic, not universal, and not predictable from model scale alone.

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