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20242026
most citedEnhancing Large Language Model Performance To Answer Questions and Extract Information More Accurately

11 citations · 23 across the 13 of their papers we have counts for

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2 papers · 1 filter

cs.SE2026

EnterpriseSynth: Zero-Execution SFT and Evaluation Data from OpenAPI Specs

Rashmi Thimmaraju, Natan Vidra, Spurthi Setty +1

Tool-using large language model agents require substantial training and evaluation data, yet collecting execution traces from enterprise APIs is often unsafe, expensive, or infeasi…

cs.SE2026

DevIntent: How Much Does LLM-Generated Code Violate Developer Intent?

Susana Haing, Natan Vidra, Spurthi Setty

Code generated by LLMs can violate a developer's implicit intentions when given an ambiguous prompt, yet standard benchmarks measure only whether code passes its stated test. We in…