LEMoN: Label Error Detection using Multimodal Neighbors (2407.18941v2)

Abstract: Large repositories of image-caption pairs are essential for the development of vision-LLMs. However, these datasets are often extracted from noisy data scraped from the web, and contain many mislabeled instances. In order to improve the reliability of downstream models, it is important to identify and filter images with incorrect captions. However, beyond filtering based on image-caption embedding similarity, no prior works have proposed other methods to filter noisy multimodal data, or concretely assessed the impact of noisy captioning data on downstream training. In this work, we propose, theoretically justify, and empirically validate LEMoN, a method to identify label errors in image-caption datasets. Our method leverages the multimodal neighborhood of image-caption pairs in the latent space of contrastively pretrained multimodal models to automatically identify label errors. Through empirical evaluations across eight datasets and twelve baselines, we find that LEMoN outperforms the baselines by over 3% in label error detection, and that training on datasets filtered using our method improves downstream captioning performance by more than 2 BLEU points over noisy training.

• The paper introduces LEMoN, which uses multimodal neighbor distances to effectively detect label errors in image-caption pairs.
• It outperforms traditional unimodal methods, achieving over 4% F1-score improvement in classification and notable gains in captioning tasks.
• The methodology enhances downstream model robustness by serving as an unsupervised data-cleaning tool across diverse application domains.

Insights into LEMoN: Label Error Detection using Multimodal Neighbors

The paper "LEM O N: Label Error Detection using Multimodal Neighbors" introduces an approach to identify label errors in multimodal datasets, specifically image-caption pairs. Given the centrality of extensive image-caption pairs in developing vision-LLMs, ensuring the integrity of these datasets is paramount, especially when considering that web-scraped data can be inherently noisy. The novel method, LEM O N, stands out through its utilization of multimodal neighbors, leveraging image and text distances derived from contrastively pretrained models to detect label errors.

The paper notes a pertinent gap in existing literature where prior methods primarily focus on unimodal data—typically using image-based representations or shallow text-based heuristics for error detection. LEM O N distinguishes itself by leveraging the rich embedding spaces produced by models like CLIP, thus enabling a more nuanced exploration of neighborhood relationships both within and across modalities.

The conceptual underpinning of LEM O N involves calculating a composite score for potential mislabeling via a combination of multimodal distance and proximity to nearest neighbors in the embedding space. Central to this methodology is the assumption that greater divergence between an instance and its neighbors in embedding space—whether image or text—signals a higher likelihood of label error.

Numerical Results and Evaluations

Impressively, LEM O N provides robust numerical results, demonstrating superior performance over baseline models not requiring task-specific classifier training in both classification and captioning contexts. For instance, it surpassed task-unaware baselines by a margin greater than 4% in F1-score for classification and 3% in captioning tasks. This indicates its proficiency in accurately pinpointing label noise across diverse datasets, including challenging real-world image datasets like CIFAR10 and nuanced text-image datasets such as MSCOCO.

An evaluation of the implications of label error detection on downstream tasks highlights a key strength of LEM O N: its efficacy in enhancing the robustness of trained models. When deployed to filter training data, using LEM O N's mislabel score yields near-optimal classification accuracy and significantly improves captioning results. This finding underscores the practical value of LEM O N in real-world applications where training data cleanliness directly impacts model performance.

Theoretical and Practical Implications

The paper carries both theoretical and practical implications. Theoretically, it provides insights into the robustness of multimodal embeddings in noisy conditions and the benefits of integrating contrastive learning techniques in label error detection frameworks. The model’s theoretical basis is rooted in propositions demonstrating the robustness of the CLIP loss to noise and clarifying how multimodal embeddings inherently facilitate noise detection.

Practically, LEM O N serves as a non-proprietary, application-agnostic tool, applicable across varied domains. The paper's experiments indicate that, even in the absence of considerable validation data, hyperparameters derived from synthetic datasets transfer effectively, validating LEM O N’s potential utility as an unsupervised data-cleaning step in model development pipelines.

Conclusion and Future Developments

In conclusion, the contributions of LEM O N lie in its multimodal awareness and principled approach to label error detection, both theoretically informed and empirically validated. Future work could explore the integration of LEM O N within iterative training regimes or its adaptation to different domains beyond vision-LLMs. Moreover, extensions into areas like active learning, where the scores could be used to guide label acquisition processes, highlight exciting possibilities for further research in this direction. These pathways have the potential to significantly enhance the reliability and utility of large-scale datasets prevalent in the field of machine learning and artificial intelligence.