This workshop is dedicated to pioneering research at the intersection of information seeking (e.g., information retrieval, recommendation, and question-answering), with the transformative potential of big models (e.g., language, visual, audio, and multimodal models), specifically focusing on five key aspects. (1) It encourages exploring how big models can revolutionize information retrieval strategies, emphasizing advancements that enhance efficiency and effectiveness in accessing information. (2) We encourage active researchers to utilize big models to advance recommender algorithms, enhancing user modeling for improved personalized recommendations. (3) We encourage groundbreaking research in question-answering systems, leveraging the capabilities of big models to provide accurate and contextually relevant responses. (4) We emphasize the critical role of trustworthiness when employing big models for information seeking, to ensure the reliability of generated content with ethical and legal standards. (5) We encourage researchers to design robust evaluation methodologies, standards, and human evaluation paradigms to comprehensively assess the impact of big models in information seeking.

In summary, the workshop will focus on exploring the challenges and opportunities of integrating big models for information seeking, covering areas such as information retrieval, recommendation, and question-answering. Our aim is to offer a platform for participants to present their research, share experiences, and foster collaborative discussions.

In recent years, the field of data mining and machine learning has experienced a surge in the scale and complexity of models. These large-scale models, often referred to as "big models", hold immense potential to revolutionize the landscape of information seeking. Moreover, within the realm of language models, recent breakthroughs have given rise to ChatGPT-like powerful models that excel in both natural language understanding and generation. Trained on vast volumes of data, these models offer exciting opportunities for enhancing information seeking experiences. The workshop aims to foster collaboration by bringing together researchers, practitioners, and industry experts from the information seeking and big model communities. Participants will delve into novel techniques, methodologies, and applications related to leveraging big models for information retrieval, recommendation systems, and question-answering.

The workshop centers around pioneering research at the intersection of information seeking, big models, and novel technologies. It specifically focuses on five key aspects: (1) revolutionizing information retrieval, e.g., exploring how big models can enhance efficiency and effectiveness in accessing information; (2) advancing recommender algorithms, e.g., utilizing big models to improve personalized recommendations; (3) enhancing question-answering systems, e.g., leveraging big models for accurate and contextually relevant responses; (4) ensuring trustworthiness, e.g., emphasizing ethical and legal standards when using big models; (5) comprehensive evaluation, e.g., designing robust evaluation methodologies to assess big models' impact. We welcome original submissions across a wide range of topics, including but not limited to:

• Information retrieval with large generative models
  • examining how large generative models influence information retrieval and ranking, and vice versa.
• Query expansion and reformulation strategies
  • developing innovative approaches for query expansion and reformulation using contextual information from big models.
• User studies and experiences with big models
  • exploring the effectiveness of big models in addressing diverse information needs from a user-centric perspective.
• Ethical considerations and responsible AI
  • examining ethical implications and propose strategies for responsible AI in the development and deployment of large generative models.
• Architectural integration into search engines
  • discussing architectural considerations for integrating big models into existing search products.
• Scalability challenges in deploying big models
  • exploring solutions ensuring efficient and reliable performance at scale.
• Applications in specialized domains
  • investigating the role of big models in domains such as healthcare, education, finance, legal, and others.
• Robust evaluation metrics
  • exploring reliable metrics for assessing big models' performance in information seeking.
• Multimodal information retrieval
  • investigating theoretical, algorithmic or practical solutions addressing problems across the domain of multimedia and information retrieval with big models.
• Integration in recommendation systems
  • exploring how big models enhance recommendation systems for personalized content discovery and accurate recommendations.
• Advancements in question-answering
  • discussing advancements in using big models for both open-domain and domain-specific question-answering.

Prospective authors are invited to submit original research papers that address the topics of interest for the workshop. Paper submissions should be limited to a maximum of ten (10) pages, in the IEEE 2-column format, including the bibliography and any possible appendices. Submissions longer than 10 pages will be rejected without review. All submissions will be double-blind reviewed by the Program Committee on the basis of technical quality, relevance to scope of the workshop, originality, significance, and clarity. Please refer to the ICDM regular Submission Guidelines for more information.

Workshop papers submission: September 17, 2024
Notification of workshop papers acceptance to authors: October 7, 2024
Camera-ready deadline and copyright form: October 11, 2024
Workshops date: December 9, 2024

Paper submission link: Workshop on Information Seeking with Big Models
The submission deadline is at 11:59 p.m. of the stated deadline date Anywhere on Earth.

In case of questions, contact us via an email to Zheng Wang.