7 citations · 10 across the 13 of their papers we have counts for
5 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…
Evaluating Financial Sentiment Analysis with Annotators Instruction Assisted Prompting: Enhancing Contextual Interpretation and Stock Prediction Accuracy
A M Muntasir Rahman, Ajim Uddin, Guiling "Grace" Wang
Financial sentiment analysis (FSA) presents unique challenges to LLMs that surpass those in typical sentiment analysis due to the nuanced language used in financial contexts. The p…
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…