Relevance modeling is a critical component for enhancing user experience in search engines, with the primary objective of identifying items that align with users' queries. Traditional models only rely on the semantic congruence between queries and items to ascertain relevance. However, this approach represents merely one aspect of the relevance judgement, and is insufficient in isolation. Even powerful Large Language Models (LLMs) still cannot accurately judge the relevance of a query and an item from a semantic perspective. To augment LLMs-driven relevance modeling, this study proposes leveraging user interactions recorded in search logs to yield insights into users' implicit search intentions. The challenge lies in the effective prompting of LLMs to capture dynamic search intentions, which poses several obstacles in real-world relevance scenarios, i.e., the absence of domain-specific knowledge, the inadequacy of an isolated prompt, and the prohibitive costs associated with deploying LLMs. In response, we propose ProRBP, a novel Progressive Retrieved Behavior-augmented Prompting framework for integrating search scenario-oriented knowledge with LLMs effectively. Specifically, we perform the user-driven behavior neighbors retrieval from the daily search logs to obtain domain-specific knowledge in time, retrieving candidates that users consider to meet their expectations. Then, we guide LLMs for relevance modeling by employing advanced prompting techniques that progressively improve the outputs of the LLMs, followed by a progressive aggregation with comprehensive consideration of diverse aspects. For online serving, we have developed an industrial application framework tailored for the deployment of LLMs in relevance modeling. Experiments on real-world industry data and online A/B testing demonstrate our proposal achieves promising performance.
Progressive Multimodal Search and Reasoning for Knowledge-Intensive Visual Question Answering
Interdisciplinary Program in Artificial Intelligence（人工智能交叉学科项目）；Department of Intelligence and Information（智能与信息系）；Samsung Advanced Institute of Technology（三星先进技术研究院）
Reflect to Inform: Boosting Multimodal Reasoning via Information-Gain-Driven Verification
University of Science and Technology of China（中国科学技术大学）；Huawei Foundation Model Department（华为基础模型部）；The Chinese University of Hong Kong（香港中文大学）；Hong Kong University of Science and Technology（香港科技大学）
Chain of Evidence: Pixel-Level Visual Attribution for Iterative Retrieval-Augmented Generation
National Engineering Research Center for Software Engineering, Peking University（软件工程国家级工程研究中心，北京大学）；City University of Hong Kong（香港城市大学）；Peking University（北京大学）；Tencent Technology（腾讯科技）
提出Chain of Evidence (CoE)框架，利用视觉语言模型直接对检索到的文档截图进行推理，输出精确边界框以可视化完整推理链，解决迭代检索增强生成中的粗粒度归因和视觉语义丢失问题。
The Impact of Quantization on Retrieval-Augmented Generation: An Analysis of Small LLMs
Amsterdam University of Applied Sciences（阿姆斯特丹应用科学大学）；Leiden University（莱顿大学）；Peoples' Friendship University of Russia (RUDN University)（俄罗斯人民友谊大学（鲁登大学））；Joint Institute for Nuclear Research（联合核子研究所）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；University of Skövde（舍夫德大学）
On most sponsored search platforms, advertisers bid on some keywords for their advertisements (ads). Given a search request, ad retrieval module rewrites the query into bidding keywords, and uses these keywords as keys to select Top N ads through inverted indexes. In this way, an ad will not be retrieved even if queries are related when the advertiser does not bid on corresponding keywords. Moreover, most ad retrieval approaches regard rewriting and ad-selecting as two separated tasks, and focus on boosting relevance between search queries and ads. Recently, in e-commerce sponsored search more…
Alibaba Group（阿里巴巴集团）；The Chinese University of Hong Kong（香港中文大学）
Collaborative filtering is an important technique for recommendation. Whereas it has been repeatedly shown to be effective in previous work, its performance remains unsatisfactory in many real-world applications, especially those where the items or users are highly diverse. In this paper, we explore an ensemble-based framework to enhance the capability of a recommender in handling diverse data. Specifically, we formulate a probabilistic model which integrates the items, the users, as well as the associations between them into a generative process. On top of this formulation, we further derive …
Dual-Hypergraph Indexing: Bridging Knowledge Islands for Multi-Hop Reasoning in Retrieval-Augmented Generation
School of Software Engineering, Xi'an Jiaotong University（西安交通大学软件学院）；EHV Power Transmission Company of China Southern Power Grid Co., Ltd.（中国南方电网有限责任公司超高压输电公司）
