Search Results for author:
Found 7 papers, 5 papers with code
ESG Classification by Implicit Rule Learning via GPT-4
Environmental, social, and governance (ESG) factors are widely adopted as higher investment return indicators.
Multi-Task Inference: Can Large Language Models Follow Multiple Instructions at Once?
Large language models (LLMs) are typically prompted to follow a single instruction per inference call.
KMMLU: Measuring Massive Multitask Language Understanding in Korean
We propose KMMLU, a new Korean benchmark with 35, 030 expert-level multiple-choice questions across 45 subjects ranging from humanities to STEM.
HAE-RAE Bench: Evaluation of Korean Knowledge in Language Models
Large language models (LLMs) trained on massive corpora demonstrate impressive capabilities in a wide range of tasks.
Beyond Classification: Financial Reasoning in State-of-the-Art Language Models
Overall, this research seeks to contribute to the understanding of the efficacy of language models in the field of finance, with a particular emphasis on their ability to engage in sophisticated reasoning and analysis within the context of investment decision-making.
Removing Non-Stationary Knowledge From Pre-Trained Language Models for Entity-Level Sentiment Classification in Finance
Extraction of sentiment signals from news text, stock message boards, and business reports, for stock movement prediction, has been a rising field of interest in finance.
Neural Networks for Delta Hedging
The Black-Scholes model, defined under the assumption of a perfect financial market, theoretically creates a flawless hedging strategy allowing the trader to evade risks in a portfolio of options.
