Summary of Convkgyarn: Spinning Configurable and Scalable Conversational Knowledge Graph Qa Datasets with Large Language Models, by Ronak Pradeep et al.
ConvKGYarn: Spinning Configurable and Scalable Conversational Knowledge Graph QA datasets with Large Language Models
by Ronak Pradeep, Daniel Lee, Ali Mousavi, Jeff Pound, Yisi Sang, Jimmy Lin, Ihab Ilyas, Saloni Potdar, Mostafa Arefiyan, Yunyao Li
First submitted to arxiv on: 12 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Information Retrieval (cs.IR); Machine Learning (cs.LG)
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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 proposed method, ConvKGYarn, aims to address the need for dynamic, scalable, and configurable conversational datasets by generating up-to-date and adjustable Knowledge Graph Question Answering (KGQA) datasets. This is crucial in today’s rapidly changing user information needs, where Large Language Models (LLMs) and conversational assistants are advancing at a rapid pace. To achieve this, the method leverages Knowledge Graphs’ structured and evolving nature to create high-quality datasets that can accommodate diverse user interaction modes, including text and voice inputs. By using ConvKGYarn, LLMs can be trained and evaluated on various conversations, exploring their behavior on different configurations grounded in the same KG fact set. The results highlight the potential of this method to improve KGQA foundations and evaluate the parametric knowledge of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ConvKGYarn is a new way to make conversation datasets that are up-to-date and easy to use. Right now, we have datasets made by humans, but they’re not keeping pace with how fast user information needs are changing. This method helps create datasets that can handle different ways people interact, like typing or talking. It uses special structures called Knowledge Graphs to make sure the data is accurate and up-to-date. By using this method, we can test Large Language Models (LLMs) on many different conversations and see how they behave. |
Keywords
» Artificial intelligence » Knowledge graph » Question answering
