Multivariate time series classification (MTSC) plays a critical role in a wide range of real-world applications, such as healthcare, finance, and industrial monitoring. This paper proposes a triple-fusion network (TriFusNet), a novel convolutional network designed to address the challenges of MTSC. TriFusNet employs a specialized architecture that captures both variable-specific features and features shared across variables through the parallel use of standard, depth-wise, and shared-kernel convolutions. A hierarchical triple-fusion strategy is introduced to enhance representation learning across three stages: input-level fusion transforms raw variables, intermediate-level fusion integrates heterogeneous features, and output-level fusion improves decision robustness. Extensive experiments on 26 benchmark datasets show that TriFusNet outperforms 15 competitive baselines, achieving the best average rank (4.3077) with a Win/Draw/Loss of 5/4/17. The effectiveness of its architectural design and parameter settings is empirically validated, and a qualitative theoretical discussion is conducted to support the proposed fusion strategy. These results highlight TriFusNet's strong potential for real-world applications involving complex and high-dimensional time series data.

Large Language Models (LLMs) have been proven to have strong zero-shot passage ranking capabilities. In-context learning effectively enhances LLM performance by providing few-shot demonstrations, opening avenues for further improving LLM’s ranking ability. However, existing studies usually retrieve the most similar demonstrations to the input, ignoring the demonstration dependencies and diversity, which is insufficient to inspire the LLM for assessing the current query-passage relevance. In this article, we propose a framework named DemoRank, which selects few-shot demonstrations by performing a novel dependency-aware reranking of the retrieved demonstrations. Considering the dependency, combining top-ranked demonstrations yields better results. Nevertheless, generating the training samples for such a dependency-aware demonstration reranker faces two challenges: (1) the traditional demonstration ranked list assumes demonstration independence, which cannot be used to train our reranker, and (2) obtaining the optimal demonstration ranked list from the retrieved set is NP-hard and inefficient. To overcome these challenges, we propose an approach to construct a kind of dependency-aware training samples efficiently and design a list-pairwise training approach for the optimization of the demonstration reranker. We conduct extensive experiments on a series of passage ranking datasets, and the results demonstrate the superior performance of our proposed DemoRank framework under various scenarios. Our code is publicly available at https://github.com/8421bcd/demorank .

As a primary means of information acquisition, information retrieval (IR) systems, such as search engines, have integrated themselves into our daily lives. These systems also serve as components of dialogue, question-answering, and recommender systems. The trajectory of IR has evolved dynamically from its origins in term-based methods to its integration with advanced neural models. While the neural models excel at capturing complex contextual signals and semantic nuances, they still face challenges such as data scarcity, interpretability, and the generation of contextually plausible yet potentially inaccurate responses. This evolution requires a combination of traditional methods (such as term-based sparse retrieval methods with rapid response) and modern neural architectures (such as language models with powerful language understanding capacity). Meanwhile, the emergence of large language models (LLMs) has revolutionized natural language processing due to their remarkable language understanding, generation, and reasoning abilities. Consequently, recent research has sought to leverage LLMs to improve IR systems. Given the rapid evolution of this research trajectory, it is necessary to consolidate existing methodologies and provide nuanced insights through a comprehensive overview. In this survey, we delve into the confluence of LLMs and IR systems, including crucial aspects such as query rewriters, retrievers, rerankers, readers, and search agents.

Users' queries are usually vague, and their search intents tend to be ambiguous, thereby needing search clarification to clarify users' current intent by asking a clarifying question and providing several clickable sub-intent items as clarification options. However, in addition to drilling down the current query, users may also have exploratory needs that diverge from their current intent. For example, a user searching for the query "Cartier women watches'' may also potentially want to explore some parallel information by issuing queries such as "Rolex women watches'' or "Cartier women bracelets'', named exploratory queries in this paper. These exploratory needs are common during the search process yet cannot be satisfied by current search clarification approaches which typically stick to the sub-intents of the query. This paper focuses on mining exploratory queries as additional options to meet users' exploratory needs in conversational search systems. Specifically, we first design a rule-based model that generates exploratory queries based on the current query's top retrieved documents. Then, we propose using the data generated by the rule-based model to train a neural generation model through multi-task learning for further generalization. Finally, we borrow the in-context learning ability of the large language model to generate exploratory queries based on prompt engineering. We constructed an evaluation dataset based on human annotations and conduct an extensive set of experiments. The results show that our proposed methods generate higher-quality exploratory queries compared with several baselines.