ChatGPT and similar large language model (LLM) based conversational agents have brought shock waves to the research world. Although astonished by their human-like performance, we find they share a significant weakness with many other existing conversational agents in that they all take a passive approach in responding to user queries. This limits their capacity to understand the users and the task better and to offer recommendations based on a broader context than a given conversation. Proactiveness is still missing in these agents, including their ability to initiate a conversation, shift topics, or offer recommendations that take into account a more extensive context. To address this limitation, this tutorial reviews methods for equipping conversational agents with proactive interaction abilities. The full-day tutorial is divided into four parts, including multiple interactive exercises. We will begin the tutorial with an interactive exercise and cover the design of existing conversational systems architecture and challenges. The content includes coverage of LLM-based recent advancements such as ChatGPT and Bard, along with reinforcement learning with human feedback (RLHF) technique. Then we will introduce the concept of proactive conversation agents and preset recent advancements in proactiveness of conversational agents, including actively driving conversations by asking questions, topic shifting, and methods that support strategic planning of conversation. Next, we will discuss important issues in conversational responses' quality control, including safety, appropriateness, language detoxication, hallucination, and alignment. Lastly, we will launch another interactive exercise and discussion with the audience to arrive at concluding remarks, prospecting open challenges and new directions. By exploring new techniques for enhancing conversational agents' proactive behavior to improve user engagement, this tutorial aims to help researchers and practitioners develop more effective conversational agents that can better understand and respond to user needs proactively and safely.

Dialogue systems are designed to offer human users social support or functional services through natural language interactions. Traditional conversation research has put significant emphasis on a system’s response-ability, including its capacity to understand dialogue context and generate appropriate responses. However, the key element of proactive behavior—a crucial aspect of intelligent conversations—is often overlooked in these studies. Proactivity empowers conversational agents to lead conversations towards achieving pre-defined targets or fulfilling specific goals on the system side. Proactive dialogue systems are equipped with advanced techniques to handle complex tasks, requiring strategic and motivational interactions, thus representing a significant step towards artificial general intelligence. Motivated by the necessity and challenges of building proactive dialogue systems, we provide a comprehensive review of various prominent problems and advanced designs for implementing proactivity into different types of dialogue systems, including open-domain dialogues, task-oriented dialogues, and information-seeking dialogues. We also discuss real-world challenges that require further research attention to meet application needs in the future, such as proactivity in dialogue systems that are based on large language models, proactivity in hybrid dialogues, evaluation protocols and ethical considerations for proactive dialogue systems. By providing a quick access and overall picture of the proactive dialogue systems domain, we aim to inspire new research directions and stimulate further advancements towards achieving the next level of conversational AI capabilities, paving the way for more dynamic and intelligent interactions within various application domains.

Recent research on proactive conversational agents (PCAs) mainly focuses on improving the system's capabilities in anticipating and planning action sequences to accomplish tasks and achieve goals before users articulate their requests. This perspectives paper highlights the importance of moving towards building human-centered PCAs that emphasize human needs and expectations, and that considers ethical and social implications of these agents, rather than solely focusing on technological capabilities. The distinction between a proactive and a reactive system lies in the proactive system's initiative-taking nature. Without thoughtful design, proactive systems risk being perceived as intrusive by human users. We address the issue by establishing a new taxonomy concerning three key dimensions of human-centered PCAs, namely Intelligence, Adaptivity, and Civility. We discuss potential research opportunities and challenges based on this new taxonomy upon the five stages of PCA system construction. This perspectives paper lays a foundation for the emerging area of conversational information retrieval research and paves the way towards advancing human-centered proactive conversational systems.

This paper proposes a novel retrieval model for session search. Through gradient descent, the model finds optimal policies for the best search engine actions from what is observed in the user and search engine interactions. The proposed framework applies direct policy learning to session search such that it greatly reduce the model complexity than prior work. It is also a flexible design, which includes a wide range of features describing the rich interactions in session search. The framework is shown to be highly effective evaluated on the recent TREC Session Tracks. As part of the efforts to bring reinforcement learning to information retrieval, this paper makes a novel contribution in theoretical modeling for session search.

\AcIR is one of the most important fields to help users find relevant information. The interaction between IR systems and users can be naturally formulated as a decision-making problem. In the last decade, deep reinforcement learning (DRL) has become a promising direction to utilize the high model capacity of deep learning to improve long-term gains. On the one hand, there have been emerging research works focusing on leveraging DRL for IR tasks while the fundamental information theory under DRL settings, the principle of RL methods for IR tasks, or the experimental evaluation protocols of DRL-based IR systems, has not been deeply investigated. On the other hand, the emerging ChatGPT also provides new insights and challenges for DRL-based IR. Therefore, we propose the fourth DRL4IR workshop1 at CIKM 2023, which provides a venue for both academia researchers and industry practitioners to present the recent advances of DRL-based IR system, to foster novel research, interesting findings, and new applications of DRL for IR. We will pay more attention to fundamental research topics and recent application advances such as ChatGPT, with an expectation of over 300 workshop participants.

Information retrieval (IR) systems have become an essential component in modern society to help users find useful information, which consists of a series of processes including query expansion, item recall, item ranking and re-ranking, etc. Based on the ranked information list, users can provide their feedbacks. Such an interaction process between users and IR systems can be naturally formulated as a decision-making problem, which can be either one-step or sequential. In the last ten years, deep reinforcement learning (DRL) has become a promising direction for decision-making, since DRL utilizes the high model capacity of deep learning for complex decision-making tasks. On the one hand, there have been emerging research works focusing on leveraging DRL for IR tasks. However, the fundamental information theory under DRL settings, the challenge of RL methods for Industrial IR tasks, or the simulations of DRL-based IR systems, has not been deeply investigated. On the other hand, the emerging LLM provides new opportunities for optimizing and simulating IR systems. To this end, we propose the first Agent-based IR workshop at SIGIR 2024, as a continuation from one of the most successful IR workshops, DRL4IR. It provides a venue for both academia researchers and industry practitioners to present the recent advances of both DRL-based IR systems and LLM-based IR systems from the agent-based IR's perspective, to foster novel research, interesting findings, and new applications.