Evaluating AI News Commentator Performance
The article discusses experiments conducted by Gunosy's ML team to improve the quality of AI-generated news commentary using prompt engineering and external data. It presents findings from evaluations in economic and sports domains.
Why it matters
The findings provide insights into the current capabilities and limitations of using large language models for generating high-quality news commentary, informing future improvements.
Key Points
- 1Providing 'oracle' external data can improve the informativeness of AI-generated comments, but does not enable advanced reasoning
- 2In the economic domain, AI-generated comments were on par with or better than human expert comments, while in sports the AI struggled to match human-level insights
- 3Consistency, informativeness, and novelty are key factors in generating valuable commentary, requiring careful prompt design and domain-specific data utilization
Details
The article describes experiments by Gunosy's ML team to evaluate and improve their AI news commentator system. They first tested providing the AI model with 'oracle' external data related to the news articles, to see if this could boost the quality and informativeness of the generated comments beyond simple article summarization. While this did increase the perceived usefulness of the comments in around half the cases, the AI still struggled to demonstrate the deep reasoning and broad background knowledge that human experts can provide. The team then conducted human evaluations of the AI-generated comments in the economic and sports domains, finding that in economics the AI could match or exceed human expert-level commentary, while in sports it tended to provide less insightful and sometimes off-base perspectives compared to humans. The analysis suggests the AI excels at generating consistent, middle-of-the-road commentary, but struggles in domains where it lacks comprehensive training data and background knowledge.
No comments yet
Be the first to comment