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Learning Soft Linear Constraints with Application to Citation Field Extraction
16 Pith papers cite this work. Polarity classification is still indexing.
16
Pith papers citing it
abstract
Accurately segmenting a citation string into fields for authors, titles, etc. is a challenging task because the output typically obeys various global constraints. Previous work has shown that modeling soft constraints, where the model is encouraged, but not require to obey the constraints, can substantially improve segmentation performance. On the other hand, for imposing hard constraints, dual decomposition is a popular technique for efficient prediction given existing algorithms for unconstrained inference. We extend the technique to perform prediction subject to soft constraints. Moreover, with a technique for performing inference given soft constraints, it is easy to automatically generate large families of constraints and learn their costs with a simple convex optimization problem during training. This allows us to obtain substantial gains in accuracy on a new, challenging citation extraction dataset.
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2026 16roles
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CiteTracer detects citation hallucinations at 97.1% accuracy on synthetic and real-world benchmarks by combining structured extraction, multi-source retrieval, deterministic matching, and class-specialist agents.
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LUCID detects hallucinations in LLM-KG reasoning by extracting node/edge features from attention and semantics then integrating them with KG structure in a GNN, achieving SOTA on nine new benchmark datasets versus 15 baselines.
A single-image head reconstruction method uses coarse-to-fine optimization with normal consistency, landmarks, and geometry-aware constraints on curvature and conformality to produce meshes with industry-grade topology and preserved facial identity.
UniVidX unifies diverse video generation tasks into one conditional diffusion model using stochastic condition masking, decoupled gated LoRAs, and cross-modal self-attention.
Introduces BetXplain, an explanation-annotated dataset of social media betting ads collected from Instagram and Reddit for detecting manipulative and deceptive advertising.
DARS replaces single-shot response labels with distribution-aware supervision derived from input and output uncertainty to produce more reliable LLM routing policies.
A new keyframe selection framework combines structural, tracking, and semantic criteria to select reliable anchor frames for diffusion-based video editing under occlusion.
QREAM rewrites documents to question-focused style using iterative ICL and distilled FT models, boosting RAG performance by up to 8% relative improvement.
DeepTrans Studio is a demo system that intercepts agentic translation workflows to let experts review, revise, and store decisions in shared team memory for propagation across segments and members.
TRUST searches for minimal input changes that achieve a user-defined confidence target in PTM models, claiming perfect robustness and low cost on benchmarks versus standard boundary-crossing methods.
The paper analyzes dependencies in genome alignment pipelines and implements synthesized optimizations from four prior tools into LASTZ to reduce serial bottlenecks.
QuadLink generates anisotropic quad-dominant meshes from point clouds via autoregressive anchor prediction and centroid-conditioned linking, with a Tri-to-Quad data converter and quad-first assembly.
AI is shifting researchers from creators to curators of generated content, risking loss of intellectual ownership and genuine understanding of science.
Round-table discussions with researchers and practitioners indicate verification and validation skills will become central for software engineers in the agentic AI era.
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Source or It Didn't Happen: A Multi-Agent Framework for Citation Hallucination Detection
CiteTracer detects citation hallucinations at 97.1% accuracy on synthetic and real-world benchmarks by combining structured extraction, multi-source retrieval, deterministic matching, and class-specialist agents.