New Interaction Paradigms for Information Retrieval in the Era of Generative AI

Redefining Interaction Paradigms in Information Retrieval: Generative AI enables a significant shift from the traditional query- response model to dynamic, user-centric interaction paradigms. Rather than providing static ranked lists, IR systems powered by generative AI can facilitate multi-turn conversations and adapt to evolving user needs. This segment of the workshop will focus on:
• Designing adaptive IR systems capable of responding to user contexts and intents in real time.
• Integrating multi-modal capabilities to combine text, visuals, and audio in retrieval processes.
• Promoting user-centered designs that enhance accessibility, in- clusivity, and engagement.
The discussions aim to establish innovative design principles for the next generation of IR systems.

Addressing Challenges in Trust, Fairness, and Transparency. The integration of generative AI introduces new challenges related to ethical and responsible use. To ensure the effectiveness and ac- ceptability of these systems, this section will address key concerns, including:
• Mitigating biases to ensure fairness and inclusivity in system outputs.
• Enhancing transparency by explaining how generative models produce their results.
• Developing accountability frameworks to address errors, inaccu- racies, and unintended consequences.
This part of the workshop will guide researchers in building systems that are not only innovative but also ethical and trustworthy.

Advancing Metrics and Evaluation Frameworks. Traditional IR evaluation metrics, such as precision and recall, are insufficient to capture the nuanced and conversational capabilities of generative AI systems. This section will focus on the development of new evaluation methods tailored to:
• Assessing conversational quality, coherence, and adaptability in multi-turn interactions.
• Measuring user satisfaction and long-term engagement with interactive IR systems.
• Evaluating task success in complex, goal-oriented information- seeking scenarios.
By refining evaluation frameworks, this segment aims to ensure that the effectiveness of generative AI-enhanced IR systems can be rigorously assessed and continuously improved.