mlsanityData to 14 Oct 2026

Amplifying Limitations, Harms and Risks of Large Language Models

arXiv 2307.04821 ↗First posted 6 Jul 2023cs.CL cs.AI cs.CYcited by 4

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Abstract (arXiv)

We present this article as a small gesture in an attempt to counter what appears to be exponentially growing hype around Artificial Intelligence (AI) and its capabilities, and the distraction provided by the associated talk of science-fiction scenarios that might arise if AI should become sentient and super-intelligent. It may also help those outside of the field to become more informed about some of the limitations of AI technology. In the current context of popular discourse AI defaults to mean foundation and large language models (LLMs) such as those used to create ChatGPT. This in itself is a misrepresentation of the diversity, depth and volume of research, researchers, and technology that truly represents the field of AI. AI being a field of research that has existed in software artefacts since at least the 1950's. We set out to highlight a number of limitations of LLMs, and in so doing highlight that harms have already arisen and will continue to arise due to these limitations. Along the way we also highlight some of the associated risks for individuals and organisations in using this technology.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 18 of its 48 references that are in the map. First posted on arXiv 6 Jul 2023.

Builds on

18 of 48 references.

References · 18

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
  4. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  5. 2023GPT-4 Technical Reportcited by 18,396
  6. 2022Emergent Abilities of Large Language Modelscited by 2,512
  7. 2020Extracting Training Data from Large Language Modelscited by 2,189
  8. 2022LaMDA: Language Models for Dialog Applicationscited by 1,220
  9. 2021An Explanation of In-context Learning as Implicit Bayesian Inferencecited by 938
  10. 2022Quantifying Memorization Across Neural Language Modelscited by 763
  11. 2023Are Emergent Abilities of Large Language Models a Mirage?cited by 523
  12. 2022Are Large Pre-Trained Language Models Leaking Your Personal Information?cited by 200
6 more
  1. 2022The Debate Over Understanding in AI's Large Language Modelscited by 142
  2. 2021What's in the Box? A Preliminary Analysis of Undesirable Content in the Common Crawl Corpuscited by 80
  3. 2023A Latent Space Theory for Emergent Abilities in Large Language Modelscited by 43
  4. 2020Language (Re)modelling: Towards Embodied Language Understandingcited by 16
  5. 2022Automating Ambiguity: Challenges and Pitfalls of Artificial Intelligencecited by 7
  6. 2023AI Safety Subproblems for Software Engineering Researcherscited by 3

Built on by

Cited by year

Citing papers per year: 2024: 4.

4 papers.

  1. 2024ALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety through Red Teamingcited by 98
  2. 2024LlavaGuard: An Open VLM-based Framework for Safeguarding Vision Datasets and Modelscited by 52
  3. 2024LLMs Lost in Translation: M-ALERT uncovers Cross-Linguistic Safety Inconsistenciescited by 7
  4. 2024Large Language Models versus Classical Machine Learning: Performance in COVID-19 Mortality Prediction Using High-Dimensional Tabular Datacited by 3