C-LLM is a framework for feeding LLM answers into blockchain smart contracts via oracles, and SenteTruth aggregates textual answers using SBERT semantic similarity plus truth discovery to resist up to 40% malicious nodes.
Safeguarding the Truth of High-Value Price Oracle Task: A Dynamically Adjusted Truth Discovery Method
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abstract
In recent years, the Decentralized Finance (DeFi) market has witnessed numerous attacks on the price oracle, leading to substantial economic losses. Despite the advent of truth discovery methods opening up new avenues for oracle development, it falls short in addressing high-value attacks on price oracle tasks. Consequently, this paper introduces a dynamically adjusted truth discovery method safeguarding the truth of high-value price oracle tasks. In the truth aggregation stage, we enhance future considerations to improve the precision of aggregated truth. During the credibility update phase, credibility is dynamically assessed based on the task's value and the Cumulative Potential Economic Contribution (CPEC) of information sources. Experimental results demonstrate a significant reduction in data deviation by 65.8\% and potential economic loss by 66.5\%, compared to the baseline scheme, in the presence of high-value attacks.
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Connecting Large Language Models with Blockchain: Advancing the Evolution of Smart Contracts from Automation to Intelligence
C-LLM is a framework for feeding LLM answers into blockchain smart contracts via oracles, and SenteTruth aggregates textual answers using SBERT semantic similarity plus truth discovery to resist up to 40% malicious nodes.