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pro vyhledávání: '"Luo, Bingqiao"'
While many studies prove more advanced LLMs perform better on tasks such as math and coding, we notice that in cryptocurrency trading, stronger LLMs work worse than weaker LLMs often. To study how this counter-intuitive phenomenon occurs, we examine
Externí odkaz:
http://arxiv.org/abs/2410.12464
The utilization of Large Language Models (LLMs) in financial trading has primarily been concentrated within the stock market, aiding in economic and financial decisions. Yet, the unique opportunities presented by the cryptocurrency market, noted for
Externí odkaz:
http://arxiv.org/abs/2407.09546
Autor:
Luo, Bingqiao
Utilizing graph analytics and learning has proven to be an effective method for exploring aspects of crypto economics such as network effects, decentralization, tokenomics, and fraud detection. However, the majority of existing research predominantly
Externí odkaz:
http://arxiv.org/abs/2403.06454
Numerous studies have been conducted to investigate the properties of large-scale temporal graphs. Despite the ubiquity of these graphs in real-world scenarios, it's usually impractical for us to obtain the whole real-time graphs due to privacy conce
Externí odkaz:
http://arxiv.org/abs/2310.11709
While numerous public blockchain datasets are available, their utility is constrained by an exclusive focus on blockchain data. This constraint limits the incorporation of relevant social network data into blockchain analysis, thereby diminishing the
Externí odkaz:
http://arxiv.org/abs/2310.01015
In recent years, blockchain technology has introduced decentralized finance (DeFi) as an alternative to traditional financial systems. DeFi aims to create a transparent and efficient financial ecosystem using smart contracts and emerging decentralize
Externí odkaz:
http://arxiv.org/abs/2308.15992
As various forms of fraud proliferate on Ethereum, it is imperative to safeguard against these malicious activities to protect susceptible users from being victimized. While current studies solely rely on graph-based fraud detection approaches, it is
Externí odkaz:
http://arxiv.org/abs/2303.18138