Back to the Future!
AI, LLMs and Newspapers

[Webinar] Tuesday, July 9, 2 pm New York | 7pm London

Gain insight from researchers

In the ever-evolving world of library science, staying updated with the latest advancements in data-driven research is not just beneficial — it’s essential. Join us for an informative session about how Large Language Models (LLMs) can dramatically transform the way historical data is accessed and analyzed, enabling your researchers to conduct more effective and efficient research.

Four talented researchers will showcase their projects which pair ProQuest TDM Studio and ProQuest Newspapers. Presentations will focus on three emerging and key research topics:
  • Enhancing Historical Understanding with Retrieval Augmented Generation”: Explore how large language models can understand and interpret historical events, offering new perspectives and insights.
  • Using Multi-Modal Models to Segment and Parse Historic Newspapers”: Learn how multi-modal language models can make historic newspaper data more accessible and valuable to researchers across disciplines.
  • Evolution of ESG Investing”: Investigate the shifts in our understanding of Environmental, Social, and Governance (ESG) investing over time through comprehensive data analysis.

Register here

HIDDEN FIELDS BELOW

Panelists

Join a fascinating online discussion live and learn from

Anusha Nandula

Data Science Student, University of California, San Diego

Boyuan Li

PhD Student (primary speaker), Warrington College of Business, University of Florida

Saachi Shenoy 

Data Science Student, University of California, San Diego

Searen Da

Computer Science Student (primary speaker) and University of Michigan Team, University of Michigan

Sehoon Kim

Assistant Professor of Finance, Warrington College of Business, University of Florida

John Dillon

Manager, Product Management, ProQuest, Part of Clarivate

Back to the Future! AI, LLMs and Newspapers

TDM Studio is our go-to resource for
researchers analyzing  newspaper data in bulk.
” 
Stephanie Labou, Data Science Librarian,
University of California, San Diego