Yue Chen

Yue Chen, Chen Huang, Yang Deng et al.

Equipping a conversational search engine with strategies regarding when to ask clarification questions is becoming increasingly important across various domains. Attributing to the context understanding capability of LLMs and their access to domain-specific sources of knowledge, LLM-based clarification strategies feature rapid transfer to various domains in a post-hoc manner. However, they still struggle to deliver promising performance on unseen domains, struggling to achieve effective domain transferability. We take the first step to investigate this issue and existing methods tend to produce one-size-fits-all strategies across diverse domains, limiting their search effectiveness. In response, we introduce a novel method, called Style, to achieve effective domain transferability. Our experimental results indicate that Style bears strong domain transferability, resulting in an average search performance improvement of ~10% on four unseen domains.

Ao Lv, Guige Ouyang, Yongzhong Huang et al.

Temporal Knowledge Graph (TKG) reasoning involves predicting future events based on historical information. However, due to the unpredictability of future events, this task is highly challenging. To address this issue, we propose a multi-scale hybrid architecture model based on ensemble learning, called RLGNet (Repeating-Local-Global History Network). Inspired by the application of multi-scale information in other fields, we introduce the concept of multi-scale information into TKG reasoning. Specifically, RLGNet captures and integrates different levels of historical information by combining modules that process information at various scales. The model comprises three modules: the Repeating History Module focuses on identifying repetitive patterns and trends in historical data, the Local History Module captures short-term changes and details, and the Global History Module provides a macro perspective on long-term changes. Additionally, to address the limitations of previous single-architecture models in generalizing across single-step and multi-step reasoning tasks, we adopted architectures based on Recurrent Neural Networks (RNN) and Multi-Layer Perceptrons (MLP) for the Local and Global History Modules, respectively. This hybrid architecture design enables the model to complement both multi-step and single-step reasoning capabilities. Finally, to address the issue of noise in TKGs, we adopt an ensemble learning strategy, combining the predictions of the three modules to reduce the impact of noise on the final prediction results. In the evaluation on six benchmark datasets, our approach generally outperforms existing TKG reasoning models in multi-step and single-step reasoning tasks.