Thinking with Knowledge Graphs: Enhancing LLM Reasoning Through Structured Data
Gorde:
| Argitaratua izan da: | arXiv.org (Dec 14, 2024), p. n/a |
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| Egile nagusia: | |
| Beste egile batzuk: | |
| Argitaratua: |
Cornell University Library, arXiv.org
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| Gaiak: | |
| Sarrera elektronikoa: | Citation/Abstract Full text outside of ProQuest |
| Etiketak: |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3145903654 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3145903654 | ||
| 045 | 0 | |b d20241214 | |
| 100 | 1 | |a Wu, Xue | |
| 245 | 1 | |a Thinking with Knowledge Graphs: Enhancing LLM Reasoning Through Structured Data | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 14, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. However, they often struggle with complex reasoning tasks and are prone to hallucination. Recent research has shown promising results in leveraging knowledge graphs (KGs) to enhance LLM performance. KGs provide a structured representation of entities and their relationships, offering a rich source of information that can enhance the reasoning capabilities of LLMs. For this work, we have developed different techniques that tightly integrate KG structures and semantics into LLM representations. Our results show that we are able to significantly improve the performance of LLMs in complex reasoning scenarios, and ground the reasoning process with KGs. We are the first to represent KGs with programming language and fine-tune pretrained LLMs with KGs. This integration facilitates more accurate and interpretable reasoning processes, paving the way for more advanced reasoning capabilities of LLMs. | |
| 653 | |a Structured data | ||
| 653 | |a Performance enhancement | ||
| 653 | |a Semantics | ||
| 653 | |a Graphs | ||
| 653 | |a Large language models | ||
| 653 | |a Graphical representations | ||
| 653 | |a Natural language processing | ||
| 653 | |a Knowledge representation | ||
| 653 | |a Programming languages | ||
| 653 | |a Task complexity | ||
| 653 | |a Reasoning | ||
| 653 | |a Speech recognition | ||
| 700 | 1 | |a Tsioutsiouliklis, Kostas | |
| 773 | 0 | |t arXiv.org |g (Dec 14, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3145903654/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.10654 |