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by prolyxis
636 days ago
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The human brain, the authors argue, in fact uses multiple networks when interpreting and producing language. These include: - the language network, which delivers formal linguistic competence
- the multiple demand network, which provides reasoning ability
- the default network, which tracks narratives above the clause level
- the theory of mind network, which infers the mental state of another entity This leads to their argument that a modular structure would lead to enhanced ability for an LLM to be both formally and functionally competent. (While LLMs currently exhibit human-level formal linguistic competence, their functional competence--the ability to navigate the real world through language--has room for improvement.) Transformer models, they note, have degree of emergent modularity through "allowing different attention heads to attend to different input features." I was wondering, is it possible to characterize the degree of emergent modularity in current systems? |
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This is basically no different from a Turing machine going from one tape to multiple tapes. While in theory it doesn't make the Turing machine more powerful, it saves a whole lot of book keeping operations that are necessary to work around the limitations of a single tape.
Another limitation is the inability to seek to positions by moving the head back and forth to rewrite old data in the context.