The paper investigates how different representations of Korean constituency structure affect parsing performance. It compares three formats—Morpheme+XPOS, Eojeol+XPOS, and Eojeol+UPOS—derived from the Penn Korean Treebank, using gold segmentation and labels to evaluate transition-based parsers. Results show that fine-grained morphological and XPOS information yields the best parsing accuracy, while eojeol-based representations offer shorter transition sequences but lower performance when only UPOS is used.
By Jungyeul Park, KyungTae Lim, Zihao Huang, Eunkyul Leah Jo, Yige Chen, Chulwoo Park
arXiv:2609.21509v1 Announce Type: new
Abstract: When language models reason in chain-of-thought or exchange free-text intermediates, they serialize structured information into natural language. How m...
By Xavier Suau, Alex Ferrando de las Morenas, Luca Zappella, Samy Bengio
arXiv:2608. 10137v1 Announce Type: cross Abstract: Grammar Constrained Decoding (GCD) forces Language Models (LMs) to produce syntactically valid outputs by masking out non-conforming tokens at each step.
By I\c{s}{\i}l \"Ozg\"u, Yaoxuan Wu, Guy Van den Broeck, Miryung Kim
arXiv:2607. 18357v1 Announce Type: cross Abstract: Large language models now write a growing share of the world's code, increasingly inside agents and serving systems that compile, execute, or dispatch generated code without line-by-line review.
By Shuoming Zhang, Ruiyuan Xu, Haofeng Li, Qiuchu Yu, Yangyu Zhang, Chunwei Xia, Xiaobing Feng, Chenxi Wang, Huimin Cui, Jiacheng Zhao
arXiv:2607. 11951v1 Announce Type: new Abstract: Large language models can write SQL, but enterprise deployment demands more than plausible text: outputs must be syntactically valid, must respect per-role and per-schema policy, must carry provable (not best-effort) guarantees, must not slow down as generations grow, and must leave a compliance-grade record of every decision.
By Mohsen Arjmandi
arXiv:2604. 26157v4 Announce Type: replace-cross Abstract: Structural generalization in semantic parsing requires systems to apply learned compositional rules to novel structural combinations.
By Zichao Wei
arXiv:2607. 18961v1 Announce Type: new Abstract: Large language models (LLMs) generate fluent text by incrementally predicting the next token from a prefix.
By Remo Pareschi
The paper introduces PASK (Parser-Aware Structural KV Persistence), a method that leverages parser transitions to inform key‑value (KV) persistence decisions in structured generation tasks. By aligning KV compression with task‑level structured risk, PASK sets protection floors based on error sensitivity and allocates remaining KV capacity using attention‑output distortion, producing a lightweight, structure‑conditioned lookup policy. In experiments on Qwen3‑4B, PASK achieves a 17.39‑point accuracy gain over the best compressed baseline, while delivering up to 2.2× higher throughput, 3.3× lower TPOT, and 0.53× the peak GPU memory of full KV.
By Linze Wu, Xinrui Chen
arXiv:2604. 22128v2 Announce Type: replace-cross Abstract: When trained on tasks requiring an understanding of hierarchical structure, transformers have been found to represent this hierarchy in distinct ways: in the geometry of the residual stream, and in stack-like attention patterns maintaining a last-in, first-out ordering.
By Aryan Sharma, Cutter Dawes, Shivam Raval
The paper presents a production streaming guardrail system that ensures exact release‑boundary equivalence for language‑model outputs. It compiles regular‑language predicates into persistent NFAs, distinguishes stable from provisional states, and applies document‑order priority to decide before each chunk release. Evaluations on over 200,000 partitioned cases show zero mismatches and demonstrate that incremental matching can outperform native regex at larger chunk sizes while remaining competitive at smaller ones.
By Christopher M. Frost
arXiv:2607. 08063v1 Announce Type: cross Abstract: Unsupervised constituency parsing aims to accurately induce latent tree structures from raw text alone.
By Ryosuke Yamaki, Daichi Mochihashi, Nobutaka Shimada, Tadahiro Taniguchi
arXiv:2604. 02047v2 Announce Type: replace-cross Abstract: Speculative decoding accelerates large language model inference by drafting multiple candidate tokens and verifying them in a single forward pass.
By Tao Jin, Phuong Minh Nguyen, Naoya Inoue