3 papers
cs.IR2026
When Does Few-Shot Prompting Help? A Systematic Empirical Study of Shot-Count Effects Across Model Scale, Architecture, and Output Parsing Robustness
Ayush Dwivedi, Ashvi Soni
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…
cs.IR2026
Beyond Exact Match: How Evaluation Methodology Dominates Model Choice in LLM-Based Product Attribute Extraction
Ayush Dwivedi, Ashvi Soni
Large language models (LLMs) have become a default choice for structured product attribute extraction in e-commerce pipelines, with practitioners reporting widely varying performan…
cs.IR2026
SAFE-Cascade: Cost-Adaptive Vision-Language Routing for Chart Question Answering
Ayush Dwivedi, Qixin Wang, Ashvi Soni +5
Vision-language models (VLMs) are powerful for chart question answering, but invoking a VLM for every query can be unnecessarily expensive when many questions are answerable from O…