Gillmore Centre Symposium - Revolutionising Finance: The Rise of Large Language Models and NLP
Join us online in examining the research that has underpinned Large Language Models (LLMs) and their likely use and impact on the world of Finance and investment management. Explore the advancements in natural language processing (NLP) that has led to the development of LLMs and gain valuable insights into the potential and limitations of these powerful language models. Engage with leading AI researchers and industry experts as they discuss the implications of LLMs on NLP, share real-world applications and use cases, and examine the challenges and opportunities in integrating LLMs into investment management processes. Don't miss this opportunity to stay ahead of the curve and understand how LLMs are reshaping the future of finance.
Agenda:
14:00 Dr Dan Philps and Dr Tim Law: Contextualizing the impact of LLMs on FinTech and investment management and the potential disruption to come.
14:05 Professor Yulan He: Have LLMs Solved NLP?
Abstract: Natural Language Processing (NLP) has witnessed significant advancements recently, with Large-scale Language Models (LLMs) such as ChatGPT, GPT-4 and LLaMA pushing the boundaries of machine reading comprehension and language generation. In this talk, I will delve into the question of whether these LLMs have successfully overcome the challenges of NLP by examining their capabilities in a range of NLP tasks. I will conclude my talk with the exciting future of AI-driven language understanding.
14:25 Dr Adriano Koshiyama: On Providing Risk Management Guardrails to LLMs
Abstract: the speaker will provide a quick summary of the tools and solutions that can be adopted by medium to large organizations to risk manage the use of LLMs by non-technical users. He will discuss the main safety concerns and how they can be translated into technological solutions for risk prevention, detection and correction.
15:50 Dr Dan Philps: LLMs in investment management: risk and opportunity
Abstract: For most investment managers, ChatGPT represents the starting whistle in a tech arms race many had hoped to avoid. How can quant and fundamental analysts apply LLMs like ChatGPT? How effective a "copilot" can these technologies be? Where is the technology headed? How will it transform investment management?
15:15 Dr Tillman Weyde: Knowledge integration and multimodality in LLMs
Abstract: Since their advent, LLMs have quickly received unprecedented levels of interest in many areas, including investment and finance. LLMs demonstrate impressive progress in language modelling and human-like fluency of the generated language. However, they are now used for their general intelligence, which is not what language models are designed or optimised for, and there are limitations that have an impact on application development. Factual correctness is one challenge that has been generally recognised. This can be addressed with knowledge integration, but it is still unclear how to reliably use knowledge bases and reasoning with language models. We will present ongoing research into these questions.
15:25 Xinyu Wang: A Scalable Framework for Table of Contents Extraction from Complex ESG Annual Reports
Abstract: Table of contents (ToC) extraction centres on structuring documents in a hierarchical manner. ESG reports pose significant challenges due to their diverse structures and extensive length. Existing large language models (LLMs), including ChatGPT and GPT-4, while capable of managing longer inputs, still fall short in their ability to process ESG reports due to their inherent length restrictions and the absence of hierarchical modelling capabilities. LLMs are also very expensive. To address these challenges, we propose a new framework for Toc extraction, consisting of three steps: (1) Constructing an initial tree of text blocks based on reading order and font sizes; (2) Modelling each tree node (or text block) independently by considering its contextual information captured in node-centric subtree; (3) Modifying the original tree by taking appropriate action on each tree node (Keep, Delete, or Move). This approach offers a practical solution for document segmentation, allowing section headings to exploit both local and long-distance contexts pertinent to themselves.
15:35 Conclusion and Closing Remarks
Note: The time allocations mentioned are approximate.