7 papers
SPECTRA: Revealing the Full Spectrum of User Preferences via Distributional LLM Inference
Luyang Zhang, Jialu Wang, Shichao Zhu +4
Large Language Models (LLMs) are increasingly used to model user preferences, with the typical output as a directly-generated ranked item list per user. However, this generative pa…
Forecasting with Guidance: Representation-Level Supervision for Time Series Forecasting
Jiacheng Wang, Liang Fan, Baihua Li +1
Nowadays, time series forecasting is predominantly approached through the end-to-end training of deep learning architectures using error-based objectives. While this is effective a…
SG: Stock State Space Graph for Enhanced Stock Trend Prediction
Yao Lu, Kaiyi Hu, Luyan Zhang
Stock trend prediction has attracted considerable attention for its potential to generate tangible investment returns. With the advent of deep learning in quantitative finance, res…
Fairshare Data Pricing via Data Valuation for Large Language Models
Luyang Zhang, Cathy Jiao, Beibei Li +1
Training data is the backbone of large language models (LLMs), yet today's data markets often operate under exploitative pricing -- sourcing data from marginalized groups with litt…
IndexNet: Timestamp and Variable-Aware Modeling for Time Series Forecasting
Beiliang Wu, Peiyuan Liu, Yifan Hu +3
Multivariate time series forecasting (MTSF) plays a vital role in a wide range of real-world applications, such as weather prediction and traffic flow forecasting. Although recent…
Multi-Hierarchical Feature Detection for Large Language Model Generated Text
Luyan Zhang, Xinyu Xie
With the rapid advancement of large language model technology, there is growing interest in whether multi-feature approaches can significantly improve AI text detection beyond what…