On the Role of Directionality in Structural Generalization
Several SLOG test categories explicitly involve directional distinctions (modifier position shifts, argument extraction positions), yet AM-Parser, the previous SOTA, uses an AM algebra whose operations do not encode direction. We redesign the symbolic backend around CCG directed types (deterministic CKY + single linear decoder, 30K learnable parameters). Under the same BERT-base encoder, the system achieves 75.9$\pm$6.4% LF exact match, surpassing AM-Parser (70.8$\pm$4.3%). Per SLOG's own category groupings, gains are highly directional: the CCG system outperforms AM-Parser on all 5 position-s
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- PossiblePossibly related (embedding) · 53%Looking for feedback on a small test SLM I built completely from scratch [P] →
- PossiblePossibly related (embedding) · 50%MathFormer: Testing whether symbolic math is pattern matching or reasoning [D] →
- PossiblePossibly related (embedding) · 49%Tagashy/mtg-compiler →
- PossiblePossibly related (embedding) · 46%thu-pacman/chitu →
- PossiblePossibly related (embedding) · 46%Transformer →
- LinkedLinked via arxiv author · 85%Zichao Wei →
“On the Role of Directionality in Structural Generalization”
- PossiblePossibly related (embedding) · 46%Contrastive Decoding Diffing (CDD): recovering verbatim finetuning data from logits alone, no weight access needed[R] →
