Summary of Distilling Event Sequence Knowledge From Large Language Models, by Somin Wadhwa et al.
Distilling Event Sequence Knowledge From Large Language Models
by Somin Wadhwa, Oktie Hassanzadeh, Debarun Bhattacharjya, Ken Barker, Jian Ni
First submitted to arxiv on: 14 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 paper explores using Large Language Models (LLMs) to generate high-quality event sequences for probabilistic event model construction. This is particularly useful when clean structured event sequences are unavailable or noisy/incomplete automated extraction results. The approach relies on a Knowledge Graph (KG) of event concepts with partial causal relations guiding the generative language model for causal event sequence generation. The generated sequences can be leveraged to discover complex structured knowledge from pattern mining and probabilistic event models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses Large Language Models to create useful event sequences that can help build better event models. This is important because sometimes we don’t have good data, or the data we do have is messy. By using a special kind of graph called a Knowledge Graph, the model creates new event sequences that are helpful for making predictions about what might happen next. |
Keywords
» Artificial intelligence » Knowledge graph » Language model
