Signals 4 · free daily AI digest

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

Paper recorded by Signals 4 on 2026-09-24 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-24 on arXiv · recorded by Signals 4 on 2026-09-25

Category: cs.CL · 自然语言处理 · first seen 2026-09-25

Abstract

The English Resource Grammar (ERG) is a hand-written computational grammar of English. Given a sentence, its processor, ACE, produces a formal meaning representation called Minimal Recursion Semantics (MRS): a graph of the sentence's predicates and their arguments. The grammar is bidirectional and can also turn an MRS back into an English sentence. \citet{hajdik2019} used the ERG's treebank to bui

Read on arXiv →

#13 most recent of 252 cs.CL papers we have recorded · ↑ newer: How Reproducible Are Evaluation Conclusions? A Self-Audit of LLM-Infer · ↓ older: Cross-Scale Transfer Learning for Depression Severity Prediction: From
Cite this page: Scoring Both Directions: LLMs realize the MRS they cannot reliably parse: the #13 most recent of 252 cs.CL papers we have recorded (as of 2026-09-24). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/scoring-both-directions-llms-realize-the-mrs-they-cannot-reliably-parse.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
Related: More cs.CL papers · arXiv signals · All papers · Today in AI
Get 4 AI signals a day by email — free.
Subscribe free → See all plans →
Get 4 AI signals a day by email — free
All models · All repos · By company · Daily editions