Empirical Computation: Prompting versus Programming
Learn how Large Language Models can solve computational problems without algorithms, using prompting instead of programming, and why this matters for the future of computation
- Explore the concept of empirical computation using LLMs
- Compare the differences between prompting and programming approaches
- Evaluate the potential benefits of using LLMs for solving computational problems without algorithms
- Investigate the challenges and limitations of empirical computation
- Apply prompting techniques to a specific computational problem to test its effectiveness
Researchers and developers working with LLMs and interested in exploring alternative approaches to problem-solving can benefit from this concept, as it challenges traditional programming paradigms and offers new possibilities for efficient computation
💡 Large Language Models can solve computational problems without algorithms, using prompting instead of programming, which can lead to more efficient and flexible computation
🤖 LLMs can solve computational problems without algorithms! 🚀 Prompting vs programming: a new vision for computation #LLMs #EmpiricalComputation
Key Takeaways
Learn how Large Language Models can solve computational problems without algorithms, using prompting instead of programming, and why this matters for the future of computation
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Abstract:
arXiv:2503.10954v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) agents can solve *any* computational problem *without* an algorithm in a runtime *independent* of the computational complexity of that problem. Instead of specifying precisely how to solve problem instance using *programming*, we ask an LLM to solve the problem instance using *prompting*. Outputs are sampled from a distribution rather than generated procedurally. In this vision paper, we explore the challenges a
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