Quarterly Mark December 2023 - 89

So. Many. Acronyms. If you love reading ESG, NLP, BERT, MLM, and NSP,
this article is for you. In all seriousness ESG information likely ranks among
the most intriguing applications for certain technological advancements
observed in textual analysis, commonly carried out through natural
language processing.
The authors of this article test whether prompt-based learning will
outperform traditional machine learning models. Prompt-based learning is
a new type of natural language processing (NLP) that can be used across
various tasks but relies more on contextual analysis rather than training a
model to respond to inputs (one of the best examples of this is ChatGPT).
They also investigate if prompt-based learning can be applied to ESG
data and it's actual utility for analysis. The results of the experiment were
positive, with the conclusion that prompt-based learning can provide
further insight into certain ESG issues. To quote the authors:
" Given many applications require ESG information in a more
granular level, in future work, it will be worthwhile to apply
prompt-based learning approach to extract such granular
information. For example, the prompt-based learning approach
can be used to identify information of renewable energy use,
greenhouse gas emissions, water recycled, and so on. "
Why is this important? Despite ESG's politicization, many asset owners,
GPs, and service providers are still trying to find ways to measure these
non-financial but material risks. The problem is data is disorganized,
sparse in some areas, and hard to sift through to determine what's most
important. By creating new AI/ML models, perhaps we can automate
some of the analysis, pick what's most important, and end the analysis
paralysis that hinders adoption.
AARON FILBECK, CAIA, CFA, CFP®, CIPM, FDP
Managing Director and Head of UniFi by CAIA™, CAIA Association
Quarterly Mark | 89

Quarterly Mark December 2023

Table of Contents for the Digital Edition of Quarterly Mark December 2023

Quarterly Mark December 2023 - Cover
Quarterly Mark December 2023 - Inside Front Cover
Quarterly Mark December 2023 - 1
Quarterly Mark December 2023 - 2
Quarterly Mark December 2023 - 3
Quarterly Mark December 2023 - 4
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Quarterly Mark December 2023 - Back Cover
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