Summary of Large Language Models and the Rationalist Empiricist Debate, by David King
Large Language Models and the Rationalist Empiricist Debate
by David King
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent debate between rationalists and empiricists has resurfaced with the advent of Large Language Models (LLMs), which some argue vindicate rationalism by requiring innate biases for functionality. The necessity of these biases is seen as evidence that empiricism lacks conceptual resources to explain linguistic competence. However, externalized empiricism, which determines innate apparatus empirically, is not refuted by LLMs’ need for innate biases. Moreover, the relevance of LLMs to the rationalist-empiricist debate in humans is questionable, as any claims about LLM learning methods being empiricist require showing that LLMs and humans learn similarly. Key differences between humans and LLMs include poverty of stimulus versus rich stimulus and grounding of linguistic outputs in sensory experience versus absence thereof. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) have sparked a debate between rationalists and empiricists, similar to one that existed centuries ago. Some people think LLMs prove that rationalism is right because they need special built-in biases to work. This idea says that empiricism can’t explain how we understand language because it doesn’t account for these biases. However, there’s another type of empiricism that tries to figure out the rules behind these biases, so this argument doesn’t necessarily mean empiricism is wrong. Additionally, LLMs are very different from humans, as they have access to a vast amount of information and their language outputs aren’t based on what we see or experience. |
Keywords
» Artificial intelligence » Grounding
