ICBINB@NeurIPS 2023 : ICBINB@NeurIPS2023 - Failure Modes in the Age of Foundation Models

Subject: Call For Papers ICBINB@NeurIPS2023 - Failure Modes in the Age of Foundation Models

We are happy to announce the I Can’t Believe It’s Not Better workshop at NeurIPS 2023. This year the workshop is titled Failure Modes in the Age of Foundation Models. We invite submissions that focus on surprising or negative results when using foundation models as well as submissions with more general negative results from machine learning. The full call for papers is below.

Key Information

Paper Submission Deadline - October 1, 2023 (Anywhere on Earth)

Workshop Website: https://sites.google.com/view/icbinb-2023/home

Call For Papers

The goal of the I Can’t Believe It’s Not Better workshop series is to promote “slow science” that pushes back against “leaderboard-ism”, and provides a forum to share surprising or negative results. In 2023 we propose to apply this same approach to the timely topic of foundation models.

The hype around ChatGPT, Stable Diffusion and SegmentAnything might suggest that all the interesting problems have been solved and artificial general intelligence is just around the corner. In this workshop we cooly reflect on this optimism, inviting submissions on failure modes of foundation models, i.e. unexpected negative results. In addition we invite contributions that will help us understand when we should expect foundation models to disrupt existing sub-fields of ML and when these powerful methods will remain complementary to another sub-field of machine learning.

We invite submissions on the following topics:
Failure modes of current foundation models (safety, explainability, methodological limitations, etc.)
Failure modes of applying foundation models, embeddings or other massive scale deep learning models.
Development of machine learning methodologies that benefit from foundation models, but necessitate other techniques.
Meta machine learning research and reflections on the impact of foundation models on the broader field of machine learning.
Negative scientific findings in a more general sense. In keeping with previous workshops we will accept findings on methodologies or tools that gave surprising negative results without foundation models. Such submissions are encouraged especially with discussion on the relevance of findings in the present climate where foundation models are changing the field.

Technical submissions may center on machine learning, deep learning or deep learning adjacent fields (causal DL, meta-learning, generative modelling, adversarial examples, probabilistic reasoning, etc) as well as domain specific applications.

Papers will be assessed on:
Clarity of writing
Rigor and transparency in the scientific methodologies employed
Novelty and significance of insights
Quality of discussion of limitations
Reproducibility of results

Selected papers will be optionally included in a special issue of PMLR. Alternatively, some authors may prefer their paper to be in the non-archival track which is to share preliminary findings that will later go to full review at another venue.