5 papers
From Causal Plausibility to Causal Reliability: Evaluating LLMs as Calibrated Direct Causal-Edge Classifiers
Amit Kumar, Elnur Adl Zarabi, Suranjana Trivedy +4
Large language models (LLMs) are increasingly used to provide prior causal knowledge for structural causal discovery, yet whether their direct-edge judgments and confidence can be…
MasterSet: A Large-Scale Benchmark for Must-Cite Citation Recommendation in the AI/ML Literature
Md Toyaha Rahman Ratul, Zhiqian Chen, Kaiqun Fu +2
The explosive growth of AI and machine learning literature -- with venues like NeurIPS and ICLR now accepting thousands of papers annually -- has made comprehensive citation covera…
StockTime: A Time Series Specialized Large Language Model Architecture for Stock Price Prediction
Shengkun Wang, Taoran Ji, Linhan Wang +4
The stock price prediction task holds a significant role in the financial domain and has been studied for a long time. Recently, large language models (LLMs) have brought new ways…
Don't Go To Extremes: Revealing the Excessive Sensitivity and Calibration Limitations of LLMs in Implicit Hate Speech Detection
Min Zhang, Jianfeng He, Taoran Ji +1
The fairness and trustworthiness of Large Language Models (LLMs) are receiving increasing attention. Implicit hate speech, which employs indirect language to convey hateful intenti…
AMA-LSTM: Pioneering Robust and Fair Financial Audio Analysis for Stock Volatility Prediction
Shengkun Wang, Taoran Ji, Jianfeng He +5
Stock volatility prediction is an important task in the financial industry. Recent advancements in multimodal methodologies, which integrate both textual and auditory data, have de…