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    Tom McCoy on Compositional Filler-Role Representations

    Yale professor outlines hypothesis on compositional filler-role representations allowing novel combinations.

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    6 Sources, 25d ago, first seen 25d ago

    TLDR

    Tom McCoy, assistant professor of linguistics at Yale, posted thread replies addressing how gradient and statistical information is encoded and used by LLMs. He notes it is almost certainly encoded in many domains but not in the fully systematic tasks studied. One reply states hypothesis 5 that the filler-role representations are compositional, enabling generalization to novel filler-role combinations. McCoy adds that this could have been false and references appendix M, which contrasts two networks that both perform nearly perfectly on their task yet differ in showing systematic filler-role composition.

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    6 Sources, first seen 25d ago

    Combined views

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    6 Sources, first seen 25d ago

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    7 comments
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    6 Sources

    @tallinzenMy take (Tom's might differ): the hypothesis that symbolic structure needs to be represented by the network in one way or another for the network to generalize correctly on symbolic tasks is indeed almost guaranteed to be true. But the cool part is that we are able to reverse engineer the format of this symbolic representation - sometimes almost perfectly - as a sum of vectors that represent filler/role bindings. And these filler/role representations themselves are compositional such that we can predict how the network will represent a new filler/role binding!
    @RTomMcCoy@tallinzen @DamienTeney @paul_smolensky Hypothesis 2: The symbolic structure uses roles of a specific type X (e.g., left-to-right positions) For any given X, this could easily be false. Each of our experiments has an X that succeeds, but also many X's that fail - eg, left-to-right in the reversing GRU (see image) 4/n

    6 Sources

    @tallinzenMy take (Tom's might differ): the hypothesis that symbolic structure needs to be represented by the network in one way or another for the network to generalize correctly on symbolic tasks is indeed almost guaranteed to be true. But the cool part is that we are able to reverse engineer the format of this symbolic representation - sometimes almost perfectly - as a sum of vectors that represent filler/role bindings. And these filler/role representations themselves are compositional such that we can predict how the network will represent a new filler/role binding!
    @RTomMcCoy@tallinzen @DamienTeney @paul_smolensky Hypothesis 2: The symbolic structure uses roles of a specific type X (e.g., left-to-right positions) For any given X, this could easily be false. Each of our experiments has an X that succeeds, but also many X's that fail - eg, left-to-right in the reversing GRU (see image) 4/n