Decoding SEC Actions: Enforcement Trends through Analyzing Blockchain litigation using LLM-based Thematic Factor Mapping
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Abstract (arXiv)
The proliferation of blockchain entities (persons or enterprises) exposes them to potential regulatory actions (e.g., being litigated) by regulatory authorities. Regulatory frameworks for crypto assets are actively being developed and refined, increasing the likelihood of such actions. The lack of systematic analysis of the factors driving litigation against blockchain entities leaves companies in need of clarity to navigate compliance risks. This absence of insight also deprives investors of the information for informed decision-making. This study focuses on U.S. litigation against blockchain entities, particularly by the U.S. Securities and Exchange Commission (SEC) given its influence on global crypto regulation. Utilizing frontier pretrained language models and large language models, we systematically map all SEC complaints against blockchain companies from 2012 to 2024 to thematic factors conceptualized by our study to delineate the factors driving SEC actions. We quantify the thematic factors and assess their influence on specific legal Acts cited within the complaints on an annual basis, allowing us to discern the regulatory emphasis, patterns and conduct trend analysis.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 60 references that are in the map. First posted on arXiv 21 Aug 2024.
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10 of 60 references.
References · 10
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2020MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformerscited by 1,463
- 2020MPNet: Masked and Permuted Pre-training for Language Understandingcited by 981
- 2021Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Modelscited by 463
- 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 463
- 2021Large Dual Encoders Are Generalizable Retrieverscited by 456
- 2020LEGAL-BERT: The Muppets straight out of Law Schoolcited by 111
- 2023GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformercited by 88
- 2019Blockchain in Global Supply Chains and Cross Border Trade: A Critical Synthesis of the State-of-the-Art, Challenges and Opportunitiescited by 1
- 2024Global Trends in Cryptocurrency Regulation: An Overviewcited by 1
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