# Connecting Large Language Models with Blockchain: Advancing the Evolution of Smart Contracts from Automation to Intelligence

arXiv: https://arxiv.org/abs/2412.02263 · HTML: https://mlsanity.com/paper/2412.02263/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 42 references that are in the map.
- First posted on arXiv 3 Dec 2024.

## Builds on (most cited first, 15 of 15)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models](https://mlsanity.com/paper/2305.08322/) (2023)
- [ToolQA: A Dataset for LLM Question Answering with External Tools](https://mlsanity.com/paper/2306.13304/) (2023)
- [Using an LLM to Help With Code Understanding](https://mlsanity.com/paper/2307.08177/) (2023)
- [Combining Fine-Tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justifications](https://mlsanity.com/paper/2403.16073/) (2024)
- [A Survey on Truth Discovery](https://mlsanity.com/paper/1505.02463/) (2015)
- [Sycophancy in Vision-Language Models: A Systematic Analysis and an Inference-Time Mitigation Framework](https://mlsanity.com/paper/2408.11261/) (2024)
- [A Survey on Blockchain Interoperability: Past, Present, and Future Trends](https://mlsanity.com/paper/2005.14282/) (2020)
- [DECO: Liberating Web Data Using Decentralized Oracles for TLS](https://mlsanity.com/paper/1909.00938/) (2019)
- [Astraea: A Decentralized Blockchain Oracle](https://mlsanity.com/paper/1808.00528/) (2018)
- [Extending On-chain Trust to Off-chain -- Trustworthy Blockchain Data Collection using Trusted Execution Environment (TEE)](https://mlsanity.com/paper/2106.15934/) (2021)
- [DeepThought: a Reputation and Voting-based Blockchain Oracle](https://mlsanity.com/paper/2209.11032/) (2022)
- [A Distributed Efficient Blockchain Oracle Scheme for Internet of Things](https://mlsanity.com/paper/2310.00254/) (2023)
- [Safeguarding the Truth of High-Value Price Oracle Task: A Dynamically Adjusted Truth Discovery Method](https://mlsanity.com/paper/2402.02543/) (2024)
- [Instant Resonance: Dual Strategy Enhances the Data Consensus Success Rate of Blockchain Threshold Signature Oracles](https://mlsanity.com/paper/2411.02945/) (2024)

## Built on by (most cited first, 1 of 1)

- [Can Artificial Intelligence solve the blockchain oracle problem? Unpacking the Challenges and Possibilities](https://mlsanity.com/paper/2507.02125/) (2025)

## Abstract (arXiv)

Blockchain smart contracts have catalyzed the development of decentralized applications across various domains, including decentralized finance. However, due to constraints in computational resources and the prevalence of data silos, current smart contracts face significant challenges in fully leveraging the powerful capabilities of Large Language Models (LLMs) for tasks such as intelligent analysis and reasoning. To address this gap, this paper proposes and implements a universal framework for integrating LLMs with blockchain data, sysname, effectively overcoming the interoperability barriers between blockchain and LLMs. By combining semantic relatedness with truth discovery methods, we introduce an innovative data aggregation approach, funcname, which significantly enhances the accuracy and trustworthiness of data generated by LLMs. To validate the framework's effectiveness, we construct a dataset consisting of three types of questions, capturing Q&A interactions between 10 oracle nodes and 5 LLM models. Experimental results demonstrate that, even with 40% malicious nodes, the proposed solution improves data accuracy by an average of 17.74% compared to the optimal baseline. This research not only provides an innovative solution for the intelligent enhancement of smart contracts but also highlights the potential for deep integration between LLMs and blockchain technology, paving the way for more intelligent and complex applications of smart contracts in the future.