Online dispute resolution: can we leave the initial decision to Large Language Models (LLM)?

Authors

DOI:

https://doi.org/10.56294/mr202223

Keywords:

Large Language Models, Pre-Trained Model, Online Dispute Resolution, Dispute Resolution, Law

Abstract

In the era of digitization and artificial intelligence, online dispute resolution has become a topic of growing interest. In this article, we will explore the potential of Large Language Models (LLM) in online dispute resolution, how they can be implemented, the necessary technological resources, as well as their limitations and challenges. LLMs have the ability to process and analyze large volumes of data in a short period of time. This allows them to evaluate many indicators, criteria, and parameters, something that could take a long time for human judges or experts. This speed and efficiency can be particularly useful in cases involving a large number of documents, such as contracts, expert reports, and others. To implement LLMs in online dispute resolution, adequate technological resources are needed. One of the main challenges is ensuring the security and privacy of the data processed by these models. To do this, the use of technologies such as blockchain can be of great help, as it allows for the creation of secure, decentralized, and unalterable records of transactions and decisions made during the dispute resolution process. LLMs are promising tools for online dispute resolution, but it is important to recognize their limitations and challenges. Although they can offer greater efficiency and agility in the analysis of legal cases, they should not be used as substitutes for human legal professionals. Instead, LLMs should be considered as complementary tools, which can enhance and enrich the decision-making process in legal cases. By responsibly and ethically implementing LLMs in online dispute resolution, and proactively addressing the risks of bias and partiality, these tools can provide great value in the legal field and improve accessibility to justice for all.

References

1. OpenAI. ChatGPT FAQ. OpenAI 2022. https://help.openai.com/en/articles/6783457-chatgpt-faq.

2. Hoffmann J, Borgeaud S, Mensch A, Buchatskaya E, Cai T, Rutherford E, et al. Training Compute-Optimal Large Language Models 2022. https://doi.org/10.48550/arXiv.2203.15556.

3. Austin J, Odena A, Nye M, Bosma M, Michalewski H, Dohan D, et al. Program Synthesis with Large Language Models 2021. https://doi.org/10.48550/arXiv.2108.07732.

4. Carlini N, Tramer F, Wallace E, Jagielski M, Herbert-Voss A, Lee K, et al. Extracting Training Data from Large Language Models 2021. https://doi.org/10.48550/arXiv.2012.07805.

5. Wei J, Tay Y, Bommasani R, Raffel C, Zoph B, Borgeaud S, et al. Emergent Abilities of Large Language Models 2022. https://doi.org/10.48550/arXiv.2206.07682.

6. Xu FF, Alon U, Neubig G, Hellendoorn VJ. A systematic evaluation of large language models of code. Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming, New York, NY, USA: Association for Computing Machinery; 2022, p. 1–10. https://doi.org/10.1145/3520312.3534862.

7. Tirumala K, Markosyan A, Zettlemoyer L, Aghajanyan A. Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models. Advances in Neural Information Processing Systems 2022;35:38274–90.

8. Yang K-C, Menczer F. Large language models can rate news outlet credibility 2022. https://doi.org/10.48550/arXiv.2304.00228.

9. Majmudar J, Dupuy C, Peris C, Smaili S, Gupta R, Zemel R. Differentially Private Decoding in Large Language Models 2022. https://doi.org/10.48550/arXiv.2205.13621.

10. Abid A, Farooqi M, Zou J. Persistent Anti-Muslim Bias in Large Language Models. Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, New York, NY, USA: Association for Computing Machinery; 2021, p. 298–306. https://doi.org/10.1145/3461702.3462624.

11. Nozza D, Bianchi F, Hovy D. Pipelines for social bias testing of large language models. Proceedings of BigScience Episode# 5–Workshop on Challenges & Perspectives in Creating Large Language Models, Association for Computational Linguistics; 2022.

12. Rytting C, Wingate D. Leveraging the Inductive Bias of Large Language Models for Abstract Textual Reasoning. Advances in Neural Information Processing Systems, vol. 34, Curran Associates, Inc.; 2021, p. 17111–22.

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Published

2022-12-27

How to Cite

1.
Ferrer-Benítez M. Online dispute resolution: can we leave the initial decision to Large Language Models (LLM)?. Metaverse Basic and Applied Research [Internet]. 2022 Dec. 27 [cited 2024 Dec. 22];1:23. Available from: https://mr.ageditor.ar/index.php/mr/article/view/14