# Reliable and Responsible Foundation Models

## Overview

In the era of AI-driven transformations, foundation models (FMs), like large-scale language and vision models, have become pivotal in various applications, from natural language processing to computer vision. These models, with their immense capabilities, offer a plethora of benefits but also introduce challenges related to reliability, transparency, and ethics. The workshop on reliable and responsible FMs (R2-FM) delves into the urgent need to ensure that such models are trustworthy and aligned with human values. The significance of this topic cannot be overstated, as the real-world implications of these models impact everything from daily information access to critical decision-making in fields like medicine and finance. Stakeholders, from developers to end-users, care deeply about this because the responsible design, deployment, and oversight of these models dictate not only the success of AI solutions but also the preservation of societal norms, equity, and fairness. Some of the fundamental questions that this workshop aims to address are:


- How can we identify and characterize unreliable and irresponsible behaviors in FMs? Topics include susceptibility to spurious features, prompt sensitivity, lack of self-consistency, and issues of nonfactuality or “hallucinations”
- How should we assess the potentially harmful capabilities of FMs and quantify their societal impact? For example, how can we predict the consequences of misuse of highly capable large language models?
- How can we pinpoint and understand the causes behind known or emerging sources of FM unreliability? This may involve examining training data, objectives, architectural design, learned weights, or other facets.
- What principles or guidelines should inform the design of the next generation of FMs to ensure they are both reliable and responsible?
- Can we establish theoretical frameworks that guarantee the reliability and responsibility of FMs?
- In practical applications, how might we leverage domain-specific knowledge to guide FMs towards improved reliability and responsibility across diverse areas, such as drug discovery, education, or clinical health?

## Topics

We invite submissions from researchers in the fields of reliability and responsibility pertaining to foundation models. Additionally, we welcome contributions from scholars in the natural sciences (such as physics, chemistry, and biology) and social sciences (including pedagogy and sociology) that necessitate the use of reliable and responsible foundation models In summary, our topics of interest include, but are not limited to:

- Theoretical foundations of FMs and related domains
- Empirical investigations into the reliability and responsibility of various FMs
- In-depth discussions exploring new dimensions of foundation model reliability and responsibility
- Interventions during pre-training to enhance the reliability and responsibility of FMs
- Innovations in fine-tuning processes to bolster the reliability and responsibility of FMs
- Discussions on aligning models with potentially superhuman capabilities to human values
- Benchmark methodologies for assessing the reliability and responsibility of FMs
- Issues of reliability and responsibility of FMs in broad applications