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Why are we using LLMs as calculators?

Dec 24, 2024 - vickiboykis.com
The article discusses the challenges and motivations behind using large language models (LLMs) for mathematical tasks, such as multiplication and algebraic reasoning, despite their primary design for natural language processing tasks like text completion and summarization. The author highlights two main reasons for this pursuit: the historical human tendency to use new technologies for calculations and the desire to explore if LLMs can achieve artificial general intelligence (AGI) by reasoning through logical problems. The article contrasts the deterministic nature of traditional calculators with the probabilistic and complex processes LLMs use to arrive at answers, which often results in inconsistent outputs for simple arithmetic tasks.

The author further elaborates on the training and operational processes of LLMs, emphasizing the involvement of human guidance and the non-deterministic nature of their outputs. Despite the current limitations in using LLMs for precise mathematical reasoning, the exploration is seen as a step towards understanding their potential for broader reasoning capabilities. The article concludes by questioning the balance between practical applications and research-driven goals, suggesting that while LLMs are not yet reliable for tasks requiring verification or reasoning, their development could eventually lead to more advanced capabilities beyond current programming languages.

Key takeaways:

  • LLMs are being tested for mathematical reasoning to explore their potential in achieving artificial general intelligence (AGI), not to replace calculators.
  • Historically, humans have developed machines primarily for mathematical calculations, and now LLMs are being explored for higher cognitive tasks.
  • LLMs process mathematical queries differently from calculators, relying on probabilistic language models rather than deterministic binary operations.
  • The inconsistency in LLMs' mathematical outputs highlights the challenges in using them for tasks requiring precise reasoning, despite their potential for broader applications.
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