12 accepted papers
Accurate traffic flow prediction is fundamental to Intelligent Transportation Systems (ITS). However, traffic dynamics exhibit inherent multi-scale heterogeneity, where stable global trends are often masked by stochastic local fluctuations. Existing methods struggle to reconcile these conflicting re
Retrieval-augmented language model (RALM) relies on retrieved external knowledge to generate responses, resulting in vulnerability in the face of retrieval results with noisy documents. Previous works integrate additional filters or finetune Large Language Models (LLMs) to learn adaptive retrieval t…
In recent years, knowledge graphs have been integrated into recommender systems as item-side auxiliary information, enhancing recommendation accuracy. However, constructing and integrating structural user-side knowledge remains a significant challenge due to the improper granularity and inherent sca…
Open Information Extraction (OpenIE) aims to extract structured information in the form of triples from unstructured text, serving as a foundation for various downstream NLP tasks. Despite the success of neural OpenIE models, their dependence on large-scale annotated datasets poses a challenge, part…
Self-reflection for Large LanguageModels (LLMs) has gained significant attention. Existing approaches involve models iterating and improving their previous responses based on LLMs’ internal reflection ability or external feedback. However, recent research has raised doubts about whether intrinsic se…
Traffic flow prediction remains a critical issue in intelligent transport systems. Despite significant efforts in traffic flow modeling, existing approaches exhibit several notable limitations: (i) Most models fail to capture traffic flow similarities over long distances and extended periods; (ii) T…
Decision-making tasks have highlighted fundamental challenges in grounding decisions within real-world contexts. Traditional decision knowledge utilization methods often struggle to effectively integrate structured decision constraints, limiting their ability to decompose high-level tasks, maintain
The rapid advancement of autonomous web navigation has significantly benefited from grounding pretrained Large Language Models (LLMs) as agents. However, current research has yet to fully leverage the redundancy of HTML elements for contrastive training. This paper introduces a novel approach to LLM…
Templates serve as a good starting point to implement a design (e.g. banner slide) but it takes great effort from designers to manually create. In this paper we present Desigen an automatic template creation pipeline which generates background images as well as harmonious layout elements over the ba…
Embodied agents navigating unknown environments face the challenge of optimizing exploration based on semantic information. Conventional methods, reliant on collected data or pre-defined rules, have limitations in scalability and applicability, while pretrained language models based methods focusing
Despite being widely deployed in safety-critical applications such as autonomous driving and health care, deep neural networks (DNNs) still suffer from non-negligible reliability issues. Numerous works had reported that DNNs were vulnerable to either natural environmental noises or man-made adversar…
Chinese word segmentation (CWS) is undoubtedly an important basic task in natural language processing. Previous works only focus on the textual modality, but there are often audio and video utterances (such as news broadcast and face-to-face dialogues), where textual, acoustic and visual modalities…