English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The impact of non-deterministic outputs from Large Language Models (LLMs) isnot well examined for financial text understanding tasks. Through a compellingcase study on investing in the US equity market via news sentiment analysis, weuncover substantial variability in sentence-level sentiment classificationresults, underscoring the innate volatility of LLM outputs. These uncertaintiescascade downstream, leading to more significant variations in portfolioconstruction and return. While tweaking the temperature parameter in thelanguage model decoder presents a potential remedy, it comes at the expense ofstifled creativity. Similarly, while ensembling multiple outputs mitigates theeffect of volatile outputs, it demands a notable computational investment. Thiswork furnishes practitioners with invaluable insights for adeptly navigatinguncertainty in the integration of LLMs into financial decision-making,particularly in scenarios dictated by non-deterministic information.

Benchmarking Large Language Model Volatility