The emergence of large language models (LLM’s) that offer significant capabilities in content comprehension, content generation, and flexible dialogues, has the potential to revolutionize the ways we seek and consume information. We can now freely converse with such systems to express our intent in a fine-grained and multimodal manner, and we expect the system to recommend existing items or generate new items as necessary, and present them in a concise summarized form. This has prompted the recent trend in both academia and industry to develop LLM-based systems for various applications with enhanced capabilities. However, before such systems can be widely used and accepted, we need to address several challenges. The first is the trust in generated content as we expect the LLM’s to make mistakes because of the quality of data being used for their training. They might also lack knowledge in certain vertical domains. We thus need to develop both external and self-evaluation techniques to assess the trustability of the generated content. The second is the integration of retrieved and generated content. This is because for many vertical domain applications, such as the Fintech, Healthcare, and Event Detection, there is a need to integrate the latest information and signals to supplement the existing and generated content. The third challenge is how to teach the system to be pro-active in anticipating the needs of the users and directing the conversation towards a fruitful direction. In this talk, I will present a generative information seeking paradigm, and discuss our research towards a trustable generative system for search and recommendation. In particular, I will discuss how we address the challenges of trust, integration of retrieved and generated content, and proactivity in two vertical domain LLM-based systems. Finally, I will present some promising research directions.

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