The proposed retrieval and summarization techniques in the AutoCast++ framework can be extended to other domains beyond event forecasting by adapting the model architecture and training data to suit the specific requirements of the new domain. Here are some ways in which these techniques can be applied to financial or political predictions:
Domain-specific Training Data: To adapt the model for financial predictions, the training data can be sourced from financial news articles, reports, and market data. Similarly, for political predictions, data from political news sources, speeches, and policy documents can be used.
Customized Relevance Scoring: The relevance scoring mechanism can be tailored to prioritize information relevant to financial indicators or political events. This can involve adjusting the scoring criteria to reflect the specific nuances of the new domain.
Temporal Dynamics: Just as in event forecasting, the model can be trained to consider the temporal dynamics of financial markets or political landscapes. Recent news articles and updates can be given more weight in the retrieval process.
Human-Aligned Loss Function: The human-aligned loss function can be fine-tuned to capture the unique forecasting behaviors in the financial or political domains. This can involve incorporating expert opinions or historical forecasting data to guide the model training.
Adaptive Summarization: The text summarization component can be adapted to extract key insights from financial reports, market analyses, or political speeches. The model can be trained to generate concise summaries that capture the essential information for prediction tasks.
By customizing the retrieval and summarization techniques to suit the specific characteristics of financial or political data, the AutoCast++ framework can be effectively extended to these domains for accurate predictions.