  Global sustainable fund universe encompasses open-end funds and
exchange-traded funds (ETF) that, by prospectus or other regulatory filings,
claim to focus on Environment, Social and Governance (ESG). Challengingly, the
claims can only be confirmed by examining the textual disclosures to check if
there is presence of intentionality and ESG focus on its investment strategy.
Currently, there is no regulation to enforce sustainability in ESG products
space. This paper proposes a unique method and system to classify and score the
fund prospectuses in the sustainable universe regarding specificity and
transparency of language. We aim to employ few-shot learners to identify
specific, ambiguous, and generic sustainable investment-related language.
Additionally, we construct a ratio metric to determine language score and
rating to rank products and quantify sustainability claims for US sustainable
universe. As a by-product, we publish manually annotated quality training
dataset on Hugging Face (ESG-Prospectus-Clarity-Category under cc-by-nc-sa-4.0)
of more than 1K ESG textual statements. The performance of the few-shot
finetuning approach is compared with zero-shot models e.g., Llama-13B, GPT 3.5
Turbo etc. We found that prompting large language models are not accurate for
domain specific tasks due to misalignment issues. The few-shot finetuning
techniques outperform zero-shot models by large margins of more than absolute
~30% in precision, recall and F1 metrics on completely unseen ESG languages
(test set). Overall, the paper attempts to establish a systematic and scalable
approach to measure and rate sustainability intention quantitatively for
sustainable funds using texts in prospectus. Regulatory bodies, investors, and
advisors may utilize the findings of this research to reduce cognitive load in
investigating or screening of ESG funds which accurately reflects the ESG
intention.
