6 research outputs found
Alpha-GPT 2.0: Human-in-the-Loop AI for Quantitative Investment
Recently, we introduced a new paradigm for alpha mining in the realm of
quantitative investment, developing a new interactive alpha mining system
framework, Alpha-GPT. This system is centered on iterative Human-AI interaction
based on large language models, introducing a Human-in-the-Loop approach to
alpha discovery. In this paper, we present the next-generation Alpha-GPT 2.0
\footnote{Draft. Work in progress}, a quantitative investment framework that
further encompasses crucial modeling and analysis phases in quantitative
investment. This framework emphasizes the iterative, interactive research
between humans and AI, embodying a Human-in-the-Loop strategy throughout the
entire quantitative investment pipeline. By assimilating the insights of human
researchers into the systematic alpha research process, we effectively leverage
the Human-in-the-Loop approach, enhancing the efficiency and precision of
quantitative investment research
A Principled Framework for Knowledge-enhanced Large Language Model
Large Language Models (LLMs) are versatile, yet they often falter in tasks
requiring deep and reliable reasoning due to issues like hallucinations,
limiting their applicability in critical scenarios. This paper introduces a
rigorously designed framework for creating LLMs that effectively anchor
knowledge and employ a closed-loop reasoning process, enhancing their
capability for in-depth analysis. We dissect the framework to illustrate the
contribution of each component to the LLMs' performance, offering a theoretical
assurance of improved reasoning under well-defined assumptions.Comment: 10 page
On the Evolution of Knowledge Graphs: A Survey and Perspective
Knowledge graphs (KGs) are structured representations of diversified
knowledge. They are widely used in various intelligent applications. In this
article, we provide a comprehensive survey on the evolution of various types of
knowledge graphs (i.e., static KGs, dynamic KGs, temporal KGs, and event KGs)
and techniques for knowledge extraction and reasoning. Furthermore, we
introduce the practical applications of different types of KGs, including a
case study in financial analysis. Finally, we propose our perspective on the
future directions of knowledge engineering, including the potential of
combining the power of knowledge graphs and large language models (LLMs), and
the evolution of knowledge extraction, reasoning, and representation