| _version_ | 1866901118647271424 |
|---|---|
| author | Liu, Qizhuang Claude, Opus 4.5 |
| author_facet | Liu, Qizhuang Claude, Opus 4.5 |
| contents | <p>This study validates Large Language Models' capability to recognize implicit concepts—methodologies, principles, and patterns that texts embody but do not explicitly name. Through five cross-domain test cases (mathematical proof, algorithm description, economic phenomenon, game theory scenario, quantum physics experiment), we demonstrate that LLMs achieve 100% recall on core implicit concept recognition with 96% overall accuracy. However, LLMs cannot reliably output Wikidata Q-identifiers. We propose LICR (LLM-based Implicit Concept Recognition), a hybrid architecture combining LLM semantic understanding with Wikidata API precise mapping, providing a solution for implicit concept recognition that extends beyond traditional entity linking.</p> <p>Key contributions:<br>1. First formal definition of Implicit Concept Recognition Problem (ICRP)<br>2. Empirical validation of LLM implicit concept recognition capability<br>3. Identification of LLM limitations in arbitrary identifier memory<br>4. Proposal of LLM-API hybrid architecture (LICR)</p> <p>This work establishes prior art for the LICR methodology and architecture.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18211198 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Beyond Named Entities: LICR - An Empirical Study of LLM-based Implicit Concept Recognition Liu, Qizhuang Claude, Opus 4.5 Implicit Concept Recognition Entity Linking Large Language Models Wikidata Knowledge Graph LICR Natural Language Processing Prior Art <p>This study validates Large Language Models' capability to recognize implicit concepts—methodologies, principles, and patterns that texts embody but do not explicitly name. Through five cross-domain test cases (mathematical proof, algorithm description, economic phenomenon, game theory scenario, quantum physics experiment), we demonstrate that LLMs achieve 100% recall on core implicit concept recognition with 96% overall accuracy. However, LLMs cannot reliably output Wikidata Q-identifiers. We propose LICR (LLM-based Implicit Concept Recognition), a hybrid architecture combining LLM semantic understanding with Wikidata API precise mapping, providing a solution for implicit concept recognition that extends beyond traditional entity linking.</p> <p>Key contributions:<br>1. First formal definition of Implicit Concept Recognition Problem (ICRP)<br>2. Empirical validation of LLM implicit concept recognition capability<br>3. Identification of LLM limitations in arbitrary identifier memory<br>4. Proposal of LLM-API hybrid architecture (LICR)</p> <p>This work establishes prior art for the LICR methodology and architecture.</p> |
| title | Beyond Named Entities: LICR - An Empirical Study of LLM-based Implicit Concept Recognition |
| topic | Implicit Concept Recognition Entity Linking Large Language Models Wikidata Knowledge Graph LICR Natural Language Processing Prior Art |
| url | https://doi.org/10.5281/zenodo.18211198 |