Heterogeneous information networks (HINs) play an indispensable role in a wide range of domain-specific applications, from recommender systems to conversational platforms. Textual heterogeneous information networks (HINs) are graphs with abundant textual information. Currently, most advanced approaches to mine textual features from HINs follow a “pre-training and fine-tuning” schema which may cause a “negative transfer” problem since there is a gap between pre-training tasks and downstream tasks. We propose a prompt-learning framework P-HIN that provides a new angle to align textual information and graph information, while narrowing down the gap between the pre-trained models and various downstream tasks. To the best of our knowledge, we are among the first to introduce and exploit the idea of prompt learning to align HIN features and textual features. The proposed framework P-HIN is composed of a text encoder and a graph encoder, and uses contrastive learning to align and fuse the graph-text pair. This pre-training operation naturally fits the few-shot learning setting. For the graph encoder, we introduce two graph pre-training tasks, masked node modeling and edge reconstruction, to exploit self-supervised information. During optimization, instead of handcrafted prompts, we use a learnable continuous text that enables more efficient and task-relevant transfer to downstream datasets. We consider a residual connection to use the context from the graph to prompt the text encoder. In experiments, P-HIN consistently and significantly outperforms state-of-the-art alternatives on all real-life datasets.
Yang Fang, Xiang Zhao, Daojian Zeng et al.· ACM Transactions on Informat...· 0 citations
This tutorial first articulates a working definition of reasoning within the context of information retrieval and derives from it a unified analytical framework, which maps existing approaches along axes that reflect the core components of the definition.
Mohanna Hoveyda, Panagiotis Eustratiadis, Arjen P. de Vries et al.· Annual International ACM SIG...· 1 citation
It is shown that instruction-tuned models generate answers even when explicitly prompted to refuse when the answer is not supported by the documents, and Reward Shaping for Refusal and Reasoning (RSRR), a reinforcement learning framework that teaches LMs to reason step-by-step over multiple documents, is introduced.
Thilina C. Rajapakse, M. de Rijke· Annual International ACM SIG...· 0 citations
DeepRepro dynamically transforms evolving repository states and runtime feedback into fine-grained implementation subplans, keeping planning aligned with execution throughout repository construction, and consistently outperforms strong scientific and commercial code-agent baselines.
Hongru Song, Ruqing Zhang, Jiafeng Guo et al.· 0 citations
The need for task-specific designs that support cross-city preference transfer, semantic grounding, and scalable reasoning over unseen destination inventories is highlighted, with results highlighting the need for task-specific designs that support cross-city preference transfer, semantic grounding, and scalable reasoning over unseen destination inventories.
Pei-Bo Li, Yang Song, Hao Xue et al.· 0 citations
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