Summary of Techgpt-2.0: a Large Language Model Project to Solve the Task Of Knowledge Graph Construction, by Jiaqi Wang et al.
TechGPT-2.0: A large language model project to solve the task of knowledge graph construction
by Jiaqi Wang, Yuying Chang, Zhong Li, Ning An, Qi Ma, Lei Hei, Haibo Luo, Yifei Lu, Feiliang Ren
First submitted to arxiv on: 9 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The TechGPT-2.0 project aims to enhance the capabilities of large language models in knowledge graph construction tasks, including named entity recognition (NER) and relationship triple extraction (RTE). Building upon the success of previous models, TechGPT-2.0 is a 7B-large language model that offers improved text processing capabilities, particularly in domains such as medicine, law, and natural sciences. The model’s enhancements include its ability to process texts from various domains, handle hallucinations, unanswerable queries, and lengthy texts. Additionally, the project provides a comprehensive introduction to the fine-tuning process on Huawei’s Ascend servers, covering debugging, data processing, and model training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TechGPT-2.0 is a new language model that can help computers understand natural language better. It’s good at finding important information in text, like names of people or places. The model was trained on a lot of text from different fields, like medicine, law, and science. This helps it understand more types of writing and answer questions about things like geography, transportation, and biology. The model can also handle tricky situations where computers might get confused, like when they’re given an impossible question or asked to process very long texts. |
Keywords
» Artificial intelligence » Fine tuning » Knowledge graph » Language model » Large language model » Named entity recognition » Ner
