Beyond Scaling: Predicting Patent Approval with Domain-specific Fine-grained Claim Dependency Graph (2024.acl-long)
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| Challenge: | Scaling up language models has demonstrated predictable improvement and unprecedented abilities in many language tasks. |
| Approach: | They propose a fine-grained cLAim depeNdency graph that captures the dependencies within the patent data and extends the embedding-based state-of-the-art (SOTA) they then explore prompt-based methods to harness proprietary LLMs' potential, but find the best results close to random guessing, underlining the ineffectiveness of model scaling-up. |
| Outcome: | The proposed graph methods outperform the standard model scaling methods in the patent approval prediction task and show that they are cost-effective. |
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| Challenge: | Large language models (LLMs) have shown exceptional performance across various text generation tasks, but remain under-explored in the patent domain, which offers highly structured and precise language. |
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| Challenge: | Existing approaches to augment LLMs with Knowledge Graphs (KGs) Knowledge-intensive tasks are prone to errors and require a large amount of knowledge to be understood. |
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| Challenge: | specialized LLMs are often limited in domain-specific applications that require specialized knowledge. |
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Fact Finder - Enhancing Domain Expertise of Large Language Models by Incorporating Knowledge Graphs (2026.eacl-demo)
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Daniel Steinigen, Roman Teucher, Timm Heine Ruland, Max Rudat, Nicolas Flores-Herr, Peter Fischer, Nikola Milosevic, Christopher Schymura, Angelo Ziletti
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