SIGIR 2024 Large Language Model Day

July 16, 2024 - Washington DC, USA

Large Language Models (LLMs) have become increasingly central to search and recommendation scenarios due to their remarkable capabilities in natural language understanding and generation. Examples of LLMs include commercial products such as GPT-4 and Gemini, as well as open-source models such as LLaMA and Mistral. These models are demonstrating great potential to revolutionize various aspects of search and recommendation systems – including core algorithms, system architecture, content understanding, user understanding, interactive modalities, domain-specific applications, and how such systems impact society.

As the focus on LLMs have increased in all fields, the rate of innovation across the world has also accelerated. SIGIR 2024 will feature a Large Language Model Day (LLM Day) as a themedaddition to the SIGIR main conference We embrace LLMs into IR research and complement the offerings of the main conference schedule. The LLM Day will facilitate a timely discussion on recent research and applications of LLMs covering both the core SIGIR community as well as other communities. We provide a schedule highlighting relevant sessions on LLMs throughout the conference to help attendees of LLM Day navigate the full conference.

LLM Day focuses on the following types of work:

  • Recent progress of foundational research in LLMs in academia and industry;
  • Emerging applications of LLMs;
  • Innovating and building on open-source LLMs;
  • Intersection of core IR research and LLMs, including IR for LLMs and LLMs for IR;
  • The state-of-practice of LLMs for search and recommendation;
  • Evaluation of foundation models.

We expect the likely topics to include but not be limited to: RAG and Dense Retrieval, Inference Efficiency, Alignment and Fine-tuning from Open-Ended Feedback, Generating Synthetic Data, Generative Retrieval and Recommendations, Long-Context Understanding, Multimodal Interaction, and AI Ethics & Safety.

Registration for attendees is open for all interested in the topic.

Time & Location

Date: Tuesday, July 16, 2024
Room: Presidential Ballroom, Capital Hilton


Time Session Speaker
09:00 - 10:00 Plenary Keynote
Thorsten Joachims (Cornell University): Towards Steerable AI Systems
10:00 - 10:30 Coffee Break
10:30 - 11:00 SIGIR LLM Day Highlight Talk
Jason Weston (Meta): Self-Rewarding LLMs
11:00 - 11:45 State of LLMs in Search and Recommendations
Chenyan Xiong (Carnegie Mellon University): Elevating the Scaling Law of Large Language Model Pretraining through Data Influence Modeling

Xinyu Crystina Zhang (University of Waterloo): Multilinguality in the Era of Large Language Models
11:45 - 12:30 LLMs at Industry Scale
Dawei Yin (Baidu): When Search Engine Meets LLMs

Amanda Bertsch (Carnegie Mellon University): Unlimiformer and Long Context Models
12:30 - 13:30 Lunch Break
13:30 - 15:00 Health, Legal, Finance, and Ethics
Subhabrata Mukherjee (Hippocratic AI): Building the First Safety-focused Conversational AI for Healthcare

Michael Cole (LexisNexis): LLMs in the Legal Domain

Anju Kambadur (Bloomberg): Generative AI in Finance

Jordan Meyer (Spawning): AI Training Data and Consent
15:00 - 15:30 Coffee Break
15:30 - 16:30 Multi-Agents & Reasoning
Qingyun Wu (Pennsylvania State University) and Chi Wang (Microsoft Research)
AutoGen: A Multi-Agent Framework for Enabling Next-Gen AI Applications

Omar Khattab (Stanford University): DSPy: Self-Improving Language Programs

Mirac Suzgun (Stanford University): Meta-prompting
16:30 - 17:30 Anticipated Trends in Search
Benjamin Piwowarski (National Centre for Scientific Research): Index-based Retrievers in the Neural Era

Bryan McCann ( The Future of Trust in LLMs
17:30 - 17:35
Vijay Krishnan ( Providing Human Data to Power Foundational Models

Large Language Model Day Chairs

Yue Wang, University of North Carolina at Chapel Hill, USA
Paul Bennett, Spotify, USA
Julia Kiseleva, Microsoft Research, USA


Questions or suggestions related to LLM Day can be communicated to the SIGIR 2024 LLM Day Chairs at