We describe a method for proactive information retrieval targeted at retrieving relevant information during a writing task. In our method, the current task and the needs of the user are estimated, and the potential next steps are unobtrusively predicted based on the user's past actions. We focus on the task of writing, in which the user is coalescing previously collected information into a text. Our proactive system automatically recommends the user relevant background information. The proposed system incorporates text input prediction using a long short-term memory (LSTM) network. We present simulations, which show that the system is able to reach higher precision values in an exploratory search setting compared to both a baseline and a comparison system.
MoM: Mixtures of Scenario-Aware Document Memories for Retrieval-Augmented Generation Systems
School of Information, Renmin University of China, Beijing, China（中国人民大学信息学院）；MemTensor (Shanghai) Technology Co., Ltd.（MemTensor（上海）技术有限公司）；Institute for Advanced Algorithms Research, Shanghai（上海先进算法研究所）
Asking Clarifying Questions in Open-Domain Information-Seeking Conversations
Università della Svizzera italiana (USI)（瑞士意大利语区大学（USI））；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
用户往往无法在一个查询中表达其复杂的信息需求。因此，他们可能需要扫描多个结果页面或重新制定他们的查询，这可能是一个令人沮丧的经历。或者，系统可以通过主动询问用户的问题来澄清他们的信息需求，从而提高用户满意度。在会话系统中，提出澄清性问题尤其重要，因为它们只能返回有限数量的结果（通常只有一个）。在本文中，我们制定了开放域信息寻求会话系统中的问题澄清的任务。为此，我们提出了一种离线评估方法，并通过众包收集了一个名为Qulac的数据集。我们的数据集建立在TREC Web Track 2009-2012数据的基础上，由198个TREC主题的10 K多个问答对组成，涉及762个方面。我们在Oracle模型上的实验表明，只问一个好问题，就P@1而言，检索性能提高了170%以上，这清楚地表明了任务的潜在影响。我们进一步提出了一个检索框架，包括三个组成部分：问题检索，问题选择，和文档检索。特别是，我们的问题选择模型考虑到原始查询和以前的问答互动，同时选择下一个问题。我们的模型显著优于竞争基准。为了促进这一领域的研究，我们公开了Qulac。
Conversation AI Dialog for Medicare powered by Finetuning and Retrieval Augmented Generation
University of Florida（佛罗里达大学）；National Institute of Technology Calicut（卡利卡特国家理工学院）；SRM, Chennai（SRM大学（金奈））；University of Cincinnati（辛辛那提大学）；Dartmouth College（达特茅斯学院）；Arizona State University（亚利桑那州立大学）
Ngram-LSTM Open Rate Prediction Model (NLORP) and Error_accuracy@C metric: Simple effective, and easy to implement approach to predict open rates for marketing email
Satisfactory Medical Consultation based on Terminology-Enhanced Information Retrieval and Emotional In-Context Learning
School of Mechanical Science and Engineering, Huazhong University of Science and Technology（华中科技大学机械科学与工程学院）；School of Computer Science, University of Warwick（华威大学计算机学院）；School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；MRC Weatherall Institute of Molecular Medicine, University of Oxford（牛津大学MRC Weatherall分子医学研究所）
