How can LLMs enable us to ingest massive streams of unstructured information, incorporate diverse perspectives and distill them into actionable insights that align with public opinion?
How effectively can language models structure and enable access to large amounts of opinion data?
This involves investigating how these technologies can be leveraged for topic modeling, measuring consensus or discord among opinions, and generating executive summaries and visualizations of the opinion landscape.
What metrics and insights can we generate from embeddings?
Text embeddings are numerical representations of text produced by transformer models that capture semantic meaning of textual data. This research leverages to cluster statements and opinions, identify outliers, and facilitate topic modeling.
What are the inherent risks associated with the deployment of language models?
This work aims to address concerns related to data bias, ethical implications, the potential for misinformation, and the overall integrity of AI-driven systems in shaping public policy. While language models offer transformative capabilities in analyzing vast, unstructured datasets, they also carry the potential to skew public discourse or influence policy decisions in unintended ways. Addressing these risks is crucial for the responsible and equitable use of AI in democratic deliberation.
