Stochastic Parrots Argument

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A Stochastic Parrots Argument is a criticism argument that LLMs generate text by mimicking patterns from their training data without genuine understanding of the underlying content.

References

2024

  1. ↑ Cite error: Invalid <ref> tag; no text was provided for refs named Uddin
  2. ↑ Cite error: Invalid <ref> tag; no text was provided for refs named parrot-paper
    • NOTE: A Stochastic Parrot Argument highlights the theory that large language models (LLMs) generate text by mimicking patterns from their training data without genuine understanding.
    • NOTE: A Stochastic Parrot Argument points out that LLMs, though capable of producing fluent language, may create nonsensical or contradictory outputs, indicating a lack of true comprehension.
    • NOTE: A Stochastic Parrot Argument emphasizes the ethical and philosophical concerns regarding the use of LLMs in contexts requiring human-like understanding, raising questions about their cognitive capabilities.
    • NOTE: A Stochastic Parrot Argument references the idea that LLMs are trained solely on text and lack sensory grounding or interaction with the real world, leading to potential biases and environmental costs.
    • NOTE: A Stochastic Parrot Argument serves as a critique of LLMs by comparing them to "stochastic parrots," highlighting the limitations of these models in tasks that require deep reasoning or genuine understanding.

2024

  • (Goldstein & Levinstein, 2024) ⇒ Simon Goldstein, and Benjamin A. Levinstein. (2024). “Does ChatGPT Have a Mind?.” doi:10.48550/arXiv.2407.11015
    • NOTE: LLM Stochastic Parrots Argument: The core of the stochastic parrots argument is that Large Language Models (LLMs) like ChatGPT are trained solely to predict the next word in a sequence without genuine comprehension of the content they generate. This leads to outputs that are fluent but can be nonsensical or contradictory, revealing a lack of true understanding and indicating that LLMs merely "parrot" patterns from their training data in a statistically sophisticated but fundamentally meaningless way .
    • NOTE: Challenges to the Stochastic Parrots View: The paper discusses several challenges to the stochastic parrots argument. One challenge is that this view overgeneralizes, potentially applying to human cognition as well, which involves significant pattern recognition and statistical learning. Another challenge is that representing the world could be an efficient means for LLMs to predict the next word, suggesting that LLMs might develop internal understanding of concepts to improve their predictions .
    • NOTE: LLM Emergent Capabilities: The paper presents evidence that LLMs exhibit emergent capabilities not explicitly part of their training objectives, such as few-shot learning, in-context learning, chain-of-thought reasoning, and unexpected coding abilities. These capabilities challenge the notion that LLMs are mere pattern matchers and suggest that LLMs can develop structured internal representations that go beyond simple pattern matching, countering the stochastic parrots argument .

2023

2021

  • (Bender et al., 2021) ⇒ Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. (2021). “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜.” In: Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pp. 610-623.
    • ABSTRACT: The past 3 years of work in NLP have been characterized by the development and deployment of ever larger language models, especially for English. BERT, its variants, GPT-2/3, and others, most recently Switch-C, have pushed the boundaries of the possible both through architectural innovations and through sheer size. Using these pretrained models and the methodology of fine-tuning them for specific tasks, researchers have extended the state of the art on a wide array of tasks as measured by leaderboards on specific benchmarks for English. In this paper, we take a step back and ask: How big is too big? What are the possible risks associated with this technology and what paths are available for mitigating those risks? We provide recommendations including weighing the environmental and financial costs first, investing resources into curating and carefully documenting datasets rather than ingesting everything on the web, carrying out pre-development exercises evaluating how the planned approach fits into research and development goals and supports stakeholder values, and encouraging research directions beyond ever larger language models.
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