Semantic Abstraction for Natural Language Inference: a Methodological Framework for Discovering and Compensating Semantic Knowledge and Reasoning Gaps in Large Language Models

arXiv:2609.26610 · cs.CL · Submitted 2026-09-22 · Read on arXiv

cs.CL

Submitted: 2026-09-22

Updated: 2026-09-22

Comments: 59 pages, 13 figures. Preprint of the article published in Knowledge-Based Systems, https://doi.org/10.1016/j.knosys.2025.114825

Journal ref: Knowledge-Based Systems, 2026, 114825

DOI: 10.1016/j.knosys.2025.114825

Code: https://github.com/david-T-M/NLI_with_LLMs_ConcepNet

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

Terminology

• Abstraction — 49× in this paper · explained in 1 episode
• Semantic Compatibility — 22× in this paper
• Natural Language Inference — 21× in this paper
• Generalization — 12× in this paper · explained in 13 episodes
• Benchmark — 9× in this paper · explained in 2 episodes
• Hallucination — 9× in this paper · explained in 3 episodes
• Natural Language Processing — 9× in this paper
• Accuracy — 8× in this paper · explained in 1 episode
• Knowledge Graph — 7× in this paper · explained in 7 episodes
• Empirical Methods in Natural Language Processing — 6× in this paper
• Consistency — 5× in this paper · explained in 3 episodes
• Knowledge graphs — 4× in this paper · explained in 2 episodes
• Natural Language Inference (NLI) — 4× in this paper · explained in 2 episodes
• Self-consistency — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Scaffolding — 3× in this paper · explained in 3 episodes
• Weighted Majority Voting — 3× in this paper
• Ablation study — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Direct Relations — 2× in this paper
• Embeddings — 2× in this paper · explained in 2 episodes
• Hybrid Approach — 2× in this paper · explained in 4 episodes
• Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
• Natural Language Understanding — 2× in this paper
• Semantic Similarity — 2× in this paper · explained in 2 episodes
• Semantic Understanding — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Adaptability — 1× in this paper · explained in 1 episode
• Contextual Understanding — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Ensemble Approach — 1× in this paper · explained in 3 episodes
• Ensemble Learning — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Hallucination Detection — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Verifier — 1× in this paper · explained in 2 episodes

Sources

• A Survey of Large Language Models
• Lost in Inference: Rediscovering the Role of Natural Language Inference for Large Language Models
• A Comprehensive Evaluation of Semantic Relation Knowledge of Pretrained Language Models and Humans
• Self-Consistency Improves Chain of Thought Reasoning in Language Models

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