Papers by Dana Angluin

3 papers
Simulating Hard Attention Using Soft Attention (2026.tacl-1)

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Challenge: a central element of hard attention is attention, which computes a weighted average of values from all unmasked positions.
Approach: They propose transformers that can simulate hard attention by using temperature scaling and positional embeddings.
Outcome: The proposed transformers can effectively focus all attention on a subset of positions.
Formal Language Recognition by Hard Attention Transformers: Perspectives from Circuit Complexity (2022.tacl-1)

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Challenge: averaging hard attention is used to recognize formal languages that UHAT and GUHAT cannot recognize.
Approach: They analyze three formal Transformer encoders that differ in the form of their self-attention mechanism . they find that UHAT and GUHAT Transformers can only recognize formal languages in AC0 .
Outcome: The proposed models can recognize languages that UHAT and GUHAT cannot . the proposed models are based on the DYCK and PARITY languages .
Transformers as Transducers (2025.tacl-1)

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Challenge: Using finite transducers, we find that transformers can express large classes of (total functional) transductions.
Approach: They extend existing RASP programming language to sequence-to-sequence transductions and introduce two new extensions.
Outcome: The proposed model can express surprisingly large classes of (total functional) transductions.

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