Large Language Models as Event Forecasters

• URL: http://arxiv.org/abs/2406.10492v1
• Date: Sat, 15 Jun 2024 04:09:31 GMT
• Title: Large Language Models as Event Forecasters
• Authors: Libo Zhang, Yue Ning,
• Abstract summary: Key elements of human events are extracted as quadruples that consist of subject, relation, object, and timestamp. These quadruples or quintuples, when organized within a specific domain, form a temporal knowledge graph (TKG)
• Score: 10.32127659470566
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Key elements of human events are extracted as quadruples that consist of subject, relation, object, and timestamp. This representation can be extended to a quintuple by adding a fifth element: a textual summary that briefly describes the event. These quadruples or quintuples, when organized within a specific domain, form a temporal knowledge graph (TKG). Current learning frameworks focus on a few TKG-related tasks, such as predicting an object given a subject and a relation or forecasting the occurrences of multiple types of events (i.e., relation) in the next time window. They typically rely on complex structural and sequential models like graph neural networks (GNNs) and recurrent neural networks (RNNs) to update intermediate embeddings. However, these methods often neglect the contextual information inherent in each quintuple, which can be effectively captured through concise textual descriptions. In this paper, we investigate how large language models (LLMs) can streamline the design of TKG learning frameworks while maintaining competitive accuracy in prediction and forecasting tasks. We develop multiple prompt templates to frame the object prediction (OP) task as a standard question-answering (QA) task, suitable for instruction fine-tuning with an encoder-decoder generative LLM. For multi-event forecasting (MEF), we design simple yet effective prompt templates for each TKG quintuple. This novel approach removes the need for GNNs and RNNs, instead utilizing an encoder-only LLM to generate fixed intermediate embeddings, which are subsequently processed by a prediction head with a self-attention mechanism to forecast potential future relations. Extensive experiments on multiple real-world datasets using various evaluation metrics validate the effectiveness and robustness of our approach.

Related papers

• Context is Key: A Benchmark for Forecasting with Essential Textual Information [87.3175915185287]
  "Context is Key" (CiK) is a time series forecasting benchmark that pairs numerical data with diverse types of carefully crafted textual context. We evaluate a range of approaches, including statistical models, time series foundation models, and LLM-based forecasters. Our experiments highlight the importance of incorporating contextual information, demonstrate surprising performance when using LLM-based forecasting models, and also reveal some of their critical shortcomings.
  arXiv  Detail & Related papers  (2024-10-24T17:56:08Z)
• MM-Forecast: A Multimodal Approach to Temporal Event Forecasting with Large Language Models [55.5765505287505]
  We study an emerging and intriguing problem of multimodal temporal event forecasting with large language models. We propose to identify two essential functions that images play in the scenario of temporal event forecasting, i.e., highlighting and complementary. We develop a novel framework, named MM-Forecast, which incorporates these function descriptions into large language models.
  arXiv  Detail & Related papers  (2024-08-08T11:44:57Z)
• Analyzing Temporal Complex Events with Large Language Models? A Benchmark towards Temporal, Long Context Understanding [57.62275091656578]
  We refer to the complex events composed of many news articles over an extended period as Temporal Complex Event (TCE) This paper proposes a novel approach using Large Language Models (LLMs) to systematically extract and analyze the event chain within TCE.
  arXiv  Detail & Related papers  (2024-06-04T16:42:17Z)
• Interaction Event Forecasting in Multi-Relational Recursive HyperGraphs: A Temporal Point Process Approach [12.142292322071299]
  This work addresses the problem of forecasting higher-order interaction events in multi-relational recursive hypergraphs. The proposed model, textitRelational Recursive Hyperedge Temporal Point Process (RRHyperTPP), uses an encoder that learns a dynamic node representation based on the historical interaction patterns. We have experimentally shown that our models perform better than previous state-of-the-art methods for interaction forecasting.
  arXiv  Detail & Related papers  (2024-04-27T15:46:54Z)
• Time-LLM: Time Series Forecasting by Reprogramming Large Language Models [110.20279343734548]
  Time series forecasting holds significant importance in many real-world dynamic systems. We present Time-LLM, a reprogramming framework to repurpose large language models for time series forecasting. Time-LLM is a powerful time series learner that outperforms state-of-the-art, specialized forecasting models.
  arXiv  Detail & Related papers  (2023-10-03T01:31:25Z)
• MTS2Graph: Interpretable Multivariate Time Series Classification with Temporal Evolving Graphs [1.1756822700775666]
  We introduce a new framework for interpreting time series data by extracting and clustering the input representative patterns. We run experiments on eight datasets of the UCR/UEA archive, along with HAR and PAM datasets.
  arXiv  Detail & Related papers  (2023-06-06T16:24:27Z)
• Pre-trained Language Model with Prompts for Temporal Knowledge Graph Completion [30.50032335014021]
  We propose a novel TKGC model, namely Pre-trained Language Model with Prompts for TKGC (PPT) We convert a series of sampled quadruples into pre-trained language model inputs and convert intervals between timestamps into different prompts to make coherent sentences with implicit semantic information. Our model can effectively incorporate information from temporal knowledge graphs into the language models.
  arXiv  Detail & Related papers  (2023-05-13T12:53:11Z)
• Complex Event Forecasting with Prediction Suffix Trees: Extended Technical Report [70.7321040534471]
  Complex Event Recognition (CER) systems have become popular in the past two decades due to their ability to "instantly" detect patterns on real-time streams of events. There is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detected by a CER engine. We present a formal framework that attempts to address the issue of Complex Event Forecasting.
  arXiv  Detail & Related papers  (2021-09-01T09:52:31Z)
• TrackMPNN: A Message Passing Graph Neural Architecture for Multi-Object Tracking [8.791710193028903]
  This study follows many previous approaches to multi-object tracking (MOT) that model the problem using graph-based data structures. We create a framework based on dynamic undirected graphs that represent the data association problem over multiple timesteps. We also provide solutions and propositions for the computational problems that need to be addressed to create a memory-efficient, real-time, online algorithm.
  arXiv  Detail & Related papers  (2021-01-11T21:52:25Z)
• Modeling Preconditions in Text with a Crowd-sourced Dataset [17.828175478279654]
  This paper introduces PeKo, a crowd-sourced annotation of preconditions between event pairs in newswire. We also introduce two challenge tasks aimed at modeling preconditions. Evaluation on both tasks shows that modeling preconditions is challenging even for today's large language models.
  arXiv  Detail & Related papers  (2020-10-06T01:52:34Z)
• Predicting Temporal Sets with Deep Neural Networks [50.53727580527024]
  We propose an integrated solution based on the deep neural networks for temporal sets prediction. A unique perspective is to learn element relationship by constructing set-level co-occurrence graph. We design an attention-based module to adaptively learn the temporal dependency of elements and sets.
  arXiv  Detail & Related papers  (2020-06-20T03:29:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.