LatticeBridge: Rare-Event Sequential Inference for Faithful Structured Sequence Synthesis
Quick Answer
LatticeBridge introduces a novel approach to structured sequence generation, leveraging a compact prefix language model and a twisted sequential Monte Carlo decoder.
Quick Take
It significantly outperforms traditional methods on 2,610 tasks from CommonGen, E2E NLG, and WikiBio, improving exact anchor satisfaction and mean anchor coverage.
Key Points
- LatticeBridge combines prefix language models and Monte Carlo decoding for better sequence generation.
- Improves exact anchor satisfaction and mean anchor coverage over traditional decoding methods.
- Evaluated on 2,610 tasks across CommonGen, E2E NLG, and WikiBio benchmarks.
- Reports metrics including required-anchor coverage and source-intrusion diagnostics.
- Focuses on rare-event sequential inference to meet multiple constraints in outputs.
Paper Resources
Source Excerpt
arXiv:2606. 11203v1 Announce Type: new Abstract: Structured sequence generation often requires a model to satisfy several input-derived constraints in a single output. Standard decoding methods may assign high probability to fluent continuations while placing low mass on continuations that realize all required anchors jointly. We study this regime as a rare-event sequential inference problem.
LatticeBridge combines a compact prefix language model, instance-compiled surface automata, and a twisted sequential Monte Carlo (SMC) decoder with resampling, multilevel splitting, and a source-support proposal term derived from instance-provided phrases. …
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