LLM-based Atomic Propositions help weak extractors: Evaluation of a Propositioner for triplet extraction

arXiv:2604.02866 · cs.CL · Submitted 2026-04-03 · Read on arXiv

Luc Pommeret, Thomas Gerald, Patrick Paroubek, Sahar Ghannay, Christophe Servan, Sophie Rosset

cs.CL

Submitted: 2026-04-03

Updated: 2026-08-20

Journal ref: Proceedings of the Knowledge Graphs and Large Language Models Workshop (KG-LLM) @ LREC 2026

DOI: 10.63317/3kna3utavhgb

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

Benchmark — 16× in this paper · explained in 2 episodes
Knowledge Graph — 12× in this paper · explained in 7 episodes
AUC — 7× in this paper · explained in 2 episodes
Accuracy — 6× in this paper · explained in 1 episode
Distillation — 6× in this paper · explained in 2 episodes
Interpretability — 5× in this paper · explained in 15 episodes
Natural Language Processing — 5× in this paper
Knowledge graphs — 3× in this paper · explained in 2 episodes
Graph Construction — 2× in this paper · explained in 3 episodes
Knowledge Distillation — 2× in this paper · explained in 11 episodes
RAG — 2× in this paper · explained in 3 episodes
Abstraction — 1× in this paper · explained in 1 episode
Conjunctive Normal Form — 1× in this paper
Cosine Similarity — 1× in this paper · explained in 3 episodes
Explainability — 1× in this paper · explained in 3 episodes
F1 Score — 1× in this paper · explained in 3 episodes
Generative Model — 1× in this paper · explained in 2 episodes
Generative Models — 1× in this paper · explained in 2 episodes
Grounding — 1× in this paper · explained in 3 episodes
Hallucination — 1× in this paper · explained in 3 episodes
Transformer — 1× in this paper · explained in 2 episodes

Sources

Distilling the Knowledge in a Neural Network

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