44 citations · 63 across the 14 of their papers we have counts for
4 papers · 1 filter
Damage Predicts Recovery: When Calibration Data Matters in Compressing Financial LLMs
Junyi Ye, Mengjia Yu, Debapriya Hazra +1
Post-training quantization and pruning rely on a small calibration corpus. Whether specialized domains such as finance require domain-matched calibration data remains unsettled. We…
Assessing the Creativity of LLMs in Proposing Novel Solutions to Mathematical Problems
Junyi Ye, Jingyi Gu, Xinyun Zhao +2
The mathematical capabilities of AI systems are complex and multifaceted. Most existing research has predominantly focused on the correctness of AI-generated solutions to mathemati…
From Blind Solvers to Logical Thinkers: Benchmarking LLMs' Logical Integrity on Faulty Mathematical Problems
A M Muntasir Rahman, Junyi Ye, Wei Yao +5
Consider the math problem: "Lily received 3 cookies from her best friend yesterday and ate 5 for breakfast. Today, her friend gave her 3 more cookies. How many cookies does Lily ha…
DataFrame QA: A Universal LLM Framework on DataFrame Question Answering Without Data Exposure
Junyi Ye, Mengnan Du, Guiling Wang
This paper introduces DataFrame question answering (QA), a novel task that utilizes large language models (LLMs) to generate Pandas queries for information retrieval and data analy…