Hongguang Li

Nuo Chen, Hongguang Li, Juhua Huang et al.

Existing retrieval-based methods have made significant strides in maintaining long-term conversations. However, these approaches face challenges in memory database management and accurate memory retrieval, hindering their efficacy in dynamic, real-world interactions. This study introduces a novel framework, COmpressive Memory-Enhanced Dialogue sYstems (COMEDY), which eschews traditional retrieval modules and memory databases. Instead, COMEDY adopts a "One-for-All" approach, utilizing a single language model to manage memory generation, compression, and response generation. Central to this framework is the concept of compressive memory, which intergrates session-specific summaries, user-bot dynamics, and past events into a concise memory format. To support COMEDY, we curated a large-scale Chinese instruction-tuning dataset, Dolphin, derived from real user-chatbot interactions. Comparative evaluations demonstrate COMEDY's superiority over traditional retrieval-based methods in producing more nuanced and human-like conversational experiences. Our codes are available at https://github.com/nuochenpku/COMEDY.

Li Shen, Yuning Wei, Yangzhu Wang et al.

With the development of Internet of Things (IoT) systems, precise long-term forecasting method is requisite for decision makers to evaluate current statuses and formulate future policies. Currently, Transformer and MLP are two paradigms for deep time-series forecasting and the former one is more prevailing in virtue of its exquisite attention mechanism and encoder-decoder architecture. However, data scientists seem to be more willing to dive into the research of encoder, leaving decoder unconcerned. Some researchers even adopt linear projections in lieu of the decoder to reduce the complexity. We argue that both extracting the features of input sequence and seeking the relations of input and prediction sequence, which are respective functions of encoder and decoder, are of paramount significance. Motivated from the success of FPN in CV field, we propose FPPformer to utilize bottom-up and top-down architectures respectively in encoder and decoder to build the full and rational hierarchy. The cutting-edge patch-wise attention is exploited and further developed with the combination, whose format is also different in encoder and decoder, of revamped element-wise attention in this work. Extensive experiments with six state-of-the-art baselines on twelve benchmarks verify the promising performances of FPPformer and the importance of elaborately devising decoder in time-series forecasting Transformer. The source code is released in https://github.com/OrigamiSL/FPPformer.