Papers with Hybrid systems

2 papers
Inference-Time Feedback for Reasoning Controllability in Diffusion Language Models (2026.acl-srw)

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Challenge: In scientific NLP systems, model outputs often serve as interfaces to downstream systems that assume strict structural requirements.
Approach: They evaluate machine-checkable controllability along three axes: structural control, iterative correction, and decoding dynamics.
Outcome: The proposed model can be usefully decomposed into global structure versus local control . the proposed model improves global structure while improving iterative correction .
Neural Grammatical Error Correction with Finite State Transducers (N19-1)

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Challenge: Language model based GEC (LM-GEC) is a promising alternative to SMT and neural sequence-to-sequence models.
Approach: They propose to use finite state transducers to improve LM-GEC by rescoring with neural language models.
Outcome: The proposed model outperforms the best published results on the CoNLL-2014 test set and achieves far better relative improvements over the baselines.

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