mlsanityData to 7 Oct 2026

The Dark Side of ChatGPT: Legal and Ethical Challenges from Stochastic Parrots and Hallucination

arXiv 2304.14347 ↗First posted 21 Apr 2023cs.CY cs.CL cs.LGcited by 23

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Abstract (arXiv)

With the launch of ChatGPT, Large Language Models (LLMs) are shaking up our whole society, rapidly altering the way we think, create and live. For instance, the GPT integration in Bing has altered our approach to online searching. While nascent LLMs have many advantages, new legal and ethical risks are also emerging, stemming in particular from stochastic parrots and hallucination. The EU is the first and foremost jurisdiction that has focused on the regulation of AI models. However, the risks posed by the new LLMs are likely to be underestimated by the emerging EU regulatory paradigm. Therefore, this correspondence warns that the European AI regulatory paradigm must evolve further to mitigate such risks.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 5 of its 13 references that are in the map. First posted on arXiv 21 Apr 2023.

Builds on

5 of 13 references.

References · 5

  1. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  2. 2022Black-Box Tuning for Language-Model-as-a-Servicecited by 254
  3. 2023Regulating ChatGPT and other Large Generative AI Modelscited by 102
  4. 2022Using sensitive data to prevent discrimination by artificial intelligence: Does the GDPR need a new exception?cited by 7
  5. 2022Regulating Gatekeeper AI and Data: Transparency, Access, and Fairness under the DMA, the GDPR, and beyondcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 12; 2025: 6; 2026: 1.

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  1. 2024GPT-4o System Cardcited by 4,467
  2. 2024Confabulation: The Surprising Value of Large Language Model Hallucinationscited by 42
  3. 2024LexEval: A Comprehensive Chinese Legal Benchmark for Evaluating Large Language Modelscited by 36
  4. 2024AI Hallucinations: A Misnomer Worth Clarifyingcited by 34
  5. 2023Chameleon: a Heterogeneous and Disaggregated Accelerator System for Retrieval-Augmented Language Modelscited by 23
  6. 2023AI-assisted Code Authoring at Scale: Fine-tuning, deploying, and mixed methods evaluationcited by 20
  7. 2024Mapping the Ethics of Generative AI: A Comprehensive Scoping Reviewcited by 19
  8. 2025Do Language Models Mirror Human Confidence? Exploring Psychological Insights to Address Overconfidence in LLMscited by 19
  9. 2024The Social Impact of Generative AI: An Analysis on ChatGPTcited by 18
  10. 2025Towards Reliable Retrieval in RAG Systems for Large Legal Datasetscited by 12
  11. 2025The Stochastic Parrot on LLM's Shoulder: A Summative Assessment of Physical Concept Understandingcited by 11
  12. 2023International Governance of Civilian AI: A Jurisdictional Certification Approachcited by 8
11 more
  1. 2024LLM vs. Lawyers: Identifying a Subset of Summary Judgments in a Large UK Case Law Datasetcited by 8
  2. 2025The Accountability Paradox: How Platform API Restrictions Undermine AI Transparency Mandatescited by 7
  3. 2024Exploring the Nexus of Large Language Models and Legal Systems: A Short Surveycited by 6
  4. 2024TrustAI at SemEval-2024 Task 8: A Comprehensive Analysis of Multi-domain Machine Generated Text Detection Techniquescited by 3
  5. 2024Can large language models understand uncommon meanings of common words?cited by 3
  6. 2025How LLMs Comprehend Temporal Meaning in Narratives: A Case Study in Cognitive Evaluation of LLMscited by 3
  7. 2024De-amplifying Bias from Differential Privacy in Language Model Fine-tuningcited by 2
  8. 2024When Not to Answer: Evaluating Prompts on GPT Models for Effective Abstention in Unanswerable Math Word Problemscited by 2
  9. 2023The risks of risk-based AI regulation: taking liability seriouslycited by 1
  10. 2025Beyond Accuracy: Rethinking Hallucination and Regulatory Response in Generative AIcited by 1
  11. 2026Logic-Parametric Neuro-Symbolic NLI: Controlling Logical Formalisms for Verifiable LLM Reasoningcited by 0