In this tutorial, we learn about an advanced strategy for information retrieval for question answering with Large Language Models (LLMs) in knowledge-intensive domains like sustainability reporting. We analyze the quality and quantity of sources before answering a question and quantify uncertainty around answering a question after LLM generation.
Author: Tobias Schimanski, University of Zurich, [email protected]
Originally presented at the CCAI Tackling Climate Change with Machine Learning Workshop at NeurIPS 2025.
We recommend executing this notebook in a Colab environment to gain access to GPUs and to manage all necessary dependencies.
Estimated time to execute end-to-end: 30 minutes
Please refer to these GitHub instructions to open a pull request via the "fork and pull request" workflow.
Pull requests will be reviewed by members of the Climate Change AI Tutorials team for relevance, accuracy, and conciseness.
Check out the tutorials page on our website for a full list of tutorials demonstrating how AI can be used to tackle problems related to climate change.
Usage of this tutorial is subject to the MIT License.
Schimanski, T. (2025). Question Answering over Sustainability Reports: Information Richness and Answer Quality [Tutorial]. In Conference on Neural Information Processing Systems. Climate Change AI.
@misc{tschimanski2025informationrichness,
title={Question Answering over Sustainability Reports: Information Richness and Answer Quality
},
author={Schimanski, Tobias},
year={2025},
organization={Climate Change AI},
type={Tutorial},
booktitle={Conference on Neural Information Processing Systems},
howpublished={\url{https://github.com/climatechange-ai-tutorials/sustainability-reports-richness}}
}