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Frequency Matters: When Time Series Foundation Models Fail Under Spectral Shift
Tianze Wang, Sofiane Ennadir, John Pertoft +7
Time series foundation models (TSFMs) have shown strong results on public benchmarks, prompting comparisons to a "BERT moment" for time series. Their effectiveness in industrial se…
Secret-Protected Evolution for Differentially Private Synthetic Text Generation
Tianze Wang, Zhaoyu Chen, Jian Du +3
Text data has become extremely valuable on large language models (LLMs) and even lead to general artificial intelligence (AGI). A lot of high-quality text in the real world is priv…
Pool Me Wisely: On the Effect of Pooling in Transformer-Based Models
Sofiane Ennadir, Levente Zólyomi, Oleg Smirnov +4
Transformer models have become the dominant backbone for sequence modeling, leveraging self-attention to produce contextualized token representations. These are typically aggregate…
RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimization
Tianci Liu, Haoxiang Jiang, Tianze Wang +5
Large language models (LLMs) have achieved impressive performance but face high computational costs and latency, limiting their deployment in resource-constrained settings. In cont…
MPO: An Efficient Post-Processing Framework for Mixing Diverse Preference Alignment
Tianze Wang, Dongnan Gui, Yifan Hu +2
Reinforcement Learning from Human Feedback (RLHF) has shown promise in aligning large language models (LLMs). Yet its reliance on a singular reward model often overlooks the divers…
Towards Unified Approaches in Self-Supervised Event Stream Modeling: Progress and Prospects
Levente Zólyomi, Tianze Wang, Sofiane Ennadir +2
The proliferation of digital interactions across diverse domains, such as healthcare, e-commerce, gaming, and finance, has resulted in the generation of vast volumes of event strea…