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When Does Bigger Help? A Controlled Study of LLM Scale for Ontology Learning

Paper recorded by Signals 4 on 2026-08-31 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-31 on arXiv · recorded by Signals 4 on 2026-09-01

Category: cs.AI · 人工智能 · first seen 2026-09-01

Abstract

The effect of Large Language Model (LLM) scale on ontology learning (OL) performance remains insufficiently characterized. We present a controlled evaluation of 13 models spanning dense and Mixture-of-Experts variants from the Qwen3.5 and Qwen3.6 lineages, together with proprietary GPT release variants, using the OntoLearner retrieval-augmented generation pipeline. All models are evaluated with th

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#244 most recent of 300 cs.AI papers we have recorded · ↑ newer: OntoAligner-Ensemble: Voting-Based Fusion across Heterogeneous Ontolog · ↓ older: BLOOM-WILT: Logit Tilting for Behaviour Elicitation in Automated LLM A
Cite this page: When Does Bigger Help? A Controlled Study of LLM Scale for Ontology Learning: the #244 most recent of 300 cs.AI papers we have recorded (as of 2026-08-31). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/when-does-bigger-help-a-controlled-study-of-llm-scale-for-ontology-learning.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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