Summary of Large Language Models As Interpolated and Extrapolated Event Predictors, by Libo Zhang and Yue Ning
Large Language Models as Interpolated and Extrapolated Event Predictors
by Libo Zhang, Yue Ning
First submitted to arxiv on: 15 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the application of large language models (LLMs) in event prediction frameworks, leveraging quadruple-based or quintuple-based data while maintaining competitive accuracy. The authors propose LEAP, a unified framework that utilizes LLMs as event predictors, streamlining the design process. Specifically, they develop prompt templates for object prediction (OP) and multi-event forecasting (MEF), eliminating the need for graph neural networks (GNNs) and recurrent neural networks (RNNs). The approach uses an encoder-only LLM to generate fixed intermediate embeddings, processed by a customized downstream head with self-attention mechanism. Experimental results on multiple real-world datasets validate the effectiveness of LEAP using various evaluation metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how big language models can help predict what will happen next in events that we know about. The researchers created a way to use these models called LEAP, which makes it easier to design event prediction systems. They came up with special instructions for the model to follow when trying to guess what will happen next, and they tested it on real-life data. |
Keywords
» Artificial intelligence » Encoder » Prompt » Self attention
