# Can Information Behaviour Inform Machine Learning?

arXiv: https://arxiv.org/abs/2205.00538 · HTML: https://mlsanity.com/paper/2205.00538/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 84 references that are in the map.
- First posted on arXiv 1 May 2022.

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [Florence: A New Foundation Model for Computer Vision](https://mlsanity.com/paper/2111.11432/) (2021)
- [Data and its (dis)contents: A survey of dataset development and use in machine learning research](https://mlsanity.com/paper/2012.05345/) (2020)
- [Deep Learning: A Critical Appraisal](https://mlsanity.com/paper/1801.00631/) (2018)
- [The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence](https://mlsanity.com/paper/2002.06177/) (2020)
- [The Values Encoded in Machine Learning Research](https://mlsanity.com/paper/2106.15590/) (2021)
- [Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models](https://mlsanity.com/paper/2102.02503/) (2021)
- [Synthetic Data for Deep Learning](https://mlsanity.com/paper/1909.11512/) (2019)
- [Human-Centered Artificial Intelligence and Machine Learning](https://mlsanity.com/paper/1901.11184/) (2019)
- [Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark](https://mlsanity.com/paper/2112.00570/) (2021)
- [C5T5: Controllable Generation of Organic Molecules with Transformers](https://mlsanity.com/paper/2108.10307/) (2021)
- [Model-agnostic Fits for Understanding Information Seeking Patterns in Humans](https://mlsanity.com/paper/2012.04858/) (2020)

## Abstract (arXiv)

The objective of this paper is to explore the opportunities for human information behaviour research to inform and influence the field of machine learning and the resulting machine information behaviour. Using the development of foundation models in machine learning as an example, the paper illustrates how human information behaviour research can bring to machine learning a more nuanced view of information and informing, a better understanding of information need and how that affects the communication among people and systems, guidance on the nature of context and how to operationalize that in models and systems, and insights into bias, misinformation, and marginalization. Despite their clear differences, the fields of information behaviour and machine learning share many common objectives, paradigms, and key research questions. The example of foundation models illustrates that human information behaviour research has much to offer in addressing some of the challenges emerging in the nascent area of machine information behaviour.