Surprisingly Fragile: Assessing and Addressing Prompt Instability in Multimodal Foundation Models

• URL: http://arxiv.org/abs/2408.14595v1
• Date: Mon, 26 Aug 2024 19:26:55 GMT
• Title: Surprisingly Fragile: Assessing and Addressing Prompt Instability in Multimodal Foundation Models
• Authors: Ian Stewart, Sameera Horawalavithana, Brendan Kennedy, Sai Munikoti, Karl Pazdernik,
• Abstract summary: Multimodal foundation models (MFMs) such as OFASys show the potential to unlock analysis of complex data via text prompts alone. Their performance may suffer in the face of text input that differs even slightly from their training distribution. This study demonstrates that prompt instability is a major concern for MFMs, leading to a consistent drop in performance across all modalities.
• Score: 1.9001431325800364
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal foundation models (MFMs) such as OFASys show the potential to unlock analysis of complex data such as images, videos, and audio data via text prompts alone. However, their performance may suffer in the face of text input that differs even slightly from their training distribution, which is surprising considering the use of modality-specific data to "ground" the text input. This study demonstrates that prompt instability is a major concern for MFMs, leading to a consistent drop in performance across all modalities, but that instability can be mitigated with additional training with augmented data. We evaluate several methods for grounded prompt perturbation, where we generate perturbations and filter based on similarity to text and/or modality data. After re-training the models on the augmented data, we find improved accuracy and more stable performance on the perturbed test data regardless of perturbation condition, suggesting that the data augmentation strategy helps the models handle domain shifts more effectively. In error analysis, we find consistent patterns of performance improvement across domains, suggesting that retraining on prompt perturbations tends to help general reasoning capabilities in MFMs.

Related papers

• FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization [11.954904313477176]
  Federated Learning (FL) is a method for training machine learning models using distributed data sources. This study proposes a novel framework named FedMAC, designed to address multi-modality missing under conditions of partial-modality missing in FL.
  arXiv  Detail & Related papers  (2024-10-04T01:24:02Z)
• Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models [89.88010750772413]
  Synthetic data has been proposed as a solution to address the issue of high-quality data scarcity in the training of large language models (LLMs) Our work delves into these specific flaws associated with question-answer (Q-A) pairs, a prevalent type of synthetic data, and presents a method based on unlearning techniques to mitigate these flaws. Our work has yielded key insights into the effective use of synthetic data, aiming to promote more robust and efficient LLM training.
  arXiv  Detail & Related papers  (2024-06-18T08:38:59Z)
• Sexism Detection on a Data Diet [14.899608305188002]
  We show how we can leverage influence scores to estimate the importance of a data point while training a model. We evaluate the model performance trained on data pruned with different pruning strategies on three out-of-domain datasets.
  arXiv  Detail & Related papers  (2024-06-07T12:39:54Z)
• Combating Missing Modalities in Egocentric Videos at Test Time [92.38662956154256]
  Real-world applications often face challenges with incomplete modalities due to privacy concerns, efficiency needs, or hardware issues. We propose a novel approach to address this issue at test time without requiring retraining. MiDl represents the first self-supervised, online solution for handling missing modalities exclusively at test time.
  arXiv  Detail & Related papers  (2024-04-23T16:01:33Z)
• Robustness Analysis on Foundational Segmentation Models [28.01242494123917]
  In this work, we perform a robustness analysis of Visual Foundation Models (VFMs) for segmentation tasks. We benchmark seven state-of-the-art segmentation architectures using 2 different datasets. Our findings reveal several key insights: VFMs exhibit vulnerabilities to compression-induced corruptions, despite not outpacing all of unimodal models in robustness, multimodal models show competitive resilience in zero-shot scenarios, and VFMs demonstrate enhanced robustness for certain object categories.
  arXiv  Detail & Related papers  (2023-06-15T16:59:42Z)
• Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition [94.56304526014875]
  We propose the first Source-Free Unsupervised Domain Adaptation (SFUDA) method for Facial Expression Recognition (FER) Our method exploits self-supervised pretraining to learn good feature representations from the target data. We validate the effectiveness of our method in four adaptation setups, proving that it consistently outperforms existing SFUDA methods when applied to FER.
  arXiv  Detail & Related papers  (2022-10-11T08:24:50Z)
• Automatic Data Augmentation via Invariance-Constrained Learning [94.27081585149836]
  Underlying data structures are often exploited to improve the solution of learning tasks. Data augmentation induces these symmetries during training by applying multiple transformations to the input data. This work tackles these issues by automatically adapting the data augmentation while solving the learning task.
  arXiv  Detail & Related papers  (2022-09-29T18:11:01Z)
• DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup [58.894901088797376]
  federated learning has emerged as a promising approach for training a global model using data from multiple organizations without leaking their raw data. We propose a general framework to solve the above two challenges simultaneously. We provide comprehensive theoretical analysis including robustness analysis, convergence analysis, and generalization ability.
  arXiv  Detail & Related papers  (2022-04-16T08:08:29Z)
• Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models [51.46732511844122]
  Powerful pre-trained language models (PLM) can be fooled by small perturbations or intentional attacks. We present Virtual Data Augmentation (VDA), a general framework for robustly fine-tuning PLMs. Our approach is able to improve the robustness of PLMs and alleviate the performance degradation under adversarial attacks.
  arXiv  Detail & Related papers  (2021-09-13T09:15:28Z)
• Improving Commonsense Causal Reasoning by Adversarial Training and Data Augmentation [14.92157586545743]
  This paper presents a number of techniques for making models more robust in the domain of causal reasoning. We show a statistically significant improvement on performance and on both datasets, even with only a small number of additionally generated data points.
  arXiv  Detail & Related papers  (2021-01-13T09:55:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.