1 citations · 1 across the 4 of their papers we have counts for
5 papers
Draft-KV: Learning Useful Latent Communication Between Language Models
Linquan Wu, Shichang Meng, Tianxiang Jiang +7
Latent communication passes internal states between language models instead of decoded text, but higher receiver accuracy does not show that the receiver used the message content.…
Temporal-Spectral Alignment with Frequency Adaptation for Source-Free Time-Series Adaptation
Shichang Meng, Linquan Wu, Xuan Ai +1
The goal of source-free domain adaptation (SFDA) for time-series data is to transfer knowledge from a pre-trained source model to an unlabeled target domain without requiring acces…
BizCompass: Benchmarking the Reasoning Capabilities of LLMs in Business Knowledge and Applications
Jianing Hao, Yuhe Wu, Yuanjian Xu +5
Large language models (LLMs) hold great promise for business applications, yet business analysis remains inherently complex, demanding rigorous reasoning and the integration of div…
ClarifyMT-Bench: Benchmarking and Improving Multi-Turn Clarification for Conversational Large Language Models
Sichun Luo, Yi Huang, Mukai Li +5
Large language models (LLMs) are increasingly deployed as conversational assistants in open-domain, multi-turn settings, where users often provide incomplete or ambiguous informati…
LENS: Large Pre-trained Transformer for Exploring Financial Time Series Regularities
Yuanjian Xu, Anxian Liu, Jianing Hao +3
Modeling large-scale time series has gained significant attention in recent years. However, its direct application in finance remains challenging due to substantial differences in…