arXiv Computation and Language
Sep 25

Scoring Both Directions: LLMs realize the MRS they cannot reliably parse

The paper evaluates two large language models, Claude Sonnet 4.5 and Claude Opus 5, on the bidirectional English Resource Grammar (ERG) tasks of generating English from Minimal Recursion Semantics (MRS) and parsing English into MRS. In generation, Opus achieves 76.3 BLEU—surpassing a 72k‑pair trained system and matching a million‑pair system—while Sonnet scores 65.7 BLEU, rising to 69.6 when selecting from ACE’s candidates. In parsing, both models lag behind ACE, attaining only 57.2 and 65.5 F₁ respectively, with exact‑match on about 1 % of sentences, highlighting that high generation scores do not guarantee accurate semantic parsing.

By Soham Dan
arXiv Machine Learning
Jul 31

Selecting Open-Weight Language Models for Zero-Shot Intent Classification: A Systematic Evaluation of 41 Models

arXiv:2607. 27421v1 Announce Type: cross Abstract: Intent classification is a core component of task-oriented dialogue systems, yet practitioners have limited systematic guidance for selecting deployable open-weight language models under compute, latency, and robustness constraints.

By Parishruthi Ganesh, Gerry Dozier, Cheryl Seals
arXiv AI
2d ago

Rethinking Retrieval in the Long-Context Era: Training-Free Semantic Trees for Adaptive Context Construction

The paper introduces TreeFuse, a training‑free framework that builds a semantic hierarchy from a full document and uses it to select and organize evidence for a query. By combining structural–semantic scoring with lexical attribution, TreeFuse retains supporting facts and relations while focusing the context on the question. Experiments on four large‑context models show a 1.72‑point improvement in QA‑F1 over full‑context prompting and a 58.55% reduction in answer‑context length.

By Zhe Xu, Tianhao Liao, Zhenqing Ling, Ying Shen, Yaliang Li, Daoyuan Chen
arXiv Computation and Language
Sep 4

FrameBench:A Language Understanding Benchmark Based on Frame Semantics

FrameBench is a new benchmark that evaluates language models on their ability to distinguish semantic frames evoked by the same verb in different contexts, using multiple-choice questions grounded in FrameNet-style resources for English and Japanese. The dataset is generated and verified through a pipeline that incorporates native-speaker judgments, and the authors provide both the data and the code for construction and evaluation. Experiments show that small models struggle with this task, while several large models outperform human reference scores.

By Chihiro Yano, Ryohei Sasano