Dissecting Misalignment of Multimodal Large Language Models via Influence Function

Authors: Lijie Hu, Chenyang Ren, Huanyi Xie, Khouloud Saadi, Shu Yang, Jingfeng Zhang, Di Wang
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Multimodal Large Language Model, Interpretability

Abstract:
Multi-modal Large Language models (MLLMs) are always trained on data from diverse and unreliable sources,  which may contain misaligned or mislabeled text-image pairs. This frequently causes robustness issues and hallucinations, leading to performance degradation. Data valuation is an efficient way to detect and trace these misalignments. Nevertheless, existing methods are computationally expensive for MLLMs. 
While computationally efficient, the classical influence functions are inadequate for contrastive learning models because they were originally designed for pointwise loss. Additionally, contrastive learning involves minimizing the distance between the modalities of positive samples and maximizing the distance between the modalities of negative samples. This requires us to evaluate the influence of samples from both perspectives. To tackle these challenges, we introduce the Extended Influence Function for Contrastive Loss (ECIF), an influence function crafted for contrastive loss. ECIF considers both positive and negative samples and provides a closed-form approximation of contrastive learning models, eliminating the need for retraining. Building upon ECIF, we develop a series of algorithms for data evaluation in MLLM, misalignment detection, and misprediction trace-back tasks. Experimental results demonstrate our ECIF advances the transparency and interpretability of MLLMs by offering a more accurate assessment of data impact and model alignment compared to traditional baseline methods.

Decision: reject
Reviewer scores: 5, 6, 5

Reviews and discussion:
<thread>
creation date:
1729987245768

thread_type:
official_review

<thread_object>
creator: reviewer_prsw

<content>
summary:
This paper proposed extended influence function for contrastive loss. It evaluates the training data contribution in MLLMs. The proposed method tackles the challenges of applying influence functions to contrastive learning models, and propose algorithms for data evaluation, misalignment detection, and misprediction trace-back tasks. The experiments show that ECIF improves the transparency and interpretability of MLLMs.

strengths:
(1) The paper addresses the misalignment of text-image pairs in training data and its impact on model performance.

(2) The proposed ECIF method provides an approach to evaluate the contribution of training data in contrastive learning models.

weaknesses:
(1) The dataset used in the experiment is too simple; All of them are old and toy datasets. More complex datasets should be used, for example Imagenet instead of Imagenette.

(2) The experiments were not thorough enough, leading to the conclusions being insufficiently validated.

questions:
N/A
</content>
</thread_object>

</thread>
<thread>
creation date:
1730555970287

thread_type:
official_review

<thread_object>
creator: reviewer_dhqx

<content>
summary:
This paper proposes the Extended Influence Function for Contrastive Loss (ECIF), a method designed to improve data valuation in multimodal large language models (MLLMs). ECIF addresses the limitations of traditional influence functions by accounting for the roles of both positive and negative samples in contrastive learning, enabling a more accurate assessment of data impact and model alignment.

strengths:
1. ECIF is a novel extension of influence functions tailored specifically for contrastive loss, allowing it to consider both positive and negative samples' impacts, which is essential for contrastive learning in MLLMs.
2. By providing a closed-form approximation, ECIF avoids the need for retraining, making it practical and computationally efficient for large models.
3. The paper demonstrates ECIF's effectiveness across multiple tasks, including data valuation, misalignment detection, and misprediction trace-back, showcasing its versatility.

weaknesses:
1. ECIF assumes a linear combination of influences from positive and negative samples, which may not accurately reflect real-world complexities in multimodal contrastive tasks.
2. The experiments focus on relatively clean and well-defined datasets, limiting the assessment of ECIF's robustness in more challenging, noisy, or open-domain multimodal datasets.

questions:
Please see the weakness part.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730569460706

thread_type:
official_review

<thread_object>
creator: reviewer_9qpr

<content>
summary:
The paper introduces the Extended Influence Function for Contrastive Loss (ECIF) to address data misalignment in multimodal large language models (MLLMs). ECIF is designed to efficiently evaluate data influence for contrastive learning, considering both positive and negative samples. It provides a closed-form approximation, avoiding retraining, and improves the transparency and interpretability of MLLMs by accurately assessing data impact and alignment.

strengths:
- **Dual-Perspective Data Valuation**: The proposed ECIF is the first dual-perspective data valuation method for multimodal large language models.

- **Applicable to Various Tasks**: The authors developed algorithms for different tasks, including identifying the most valuable data for specific tasks, detecting data misalignment, and tracing mispredictions.

- **Efficient Sample Influence Removal**: Experimental results show that ECIF is more efficient than retraining at removing the influence of samples and identifying influential data in the training set.

weaknesses:
- **Questionable Design Rationale**: The authors applied the Influence Function to contrastive learning, but the overall design's rationale is debatable.

- **Insufficient Experiments**: The experiments are limited, with too few baselines and datasets, leading to insufficient empirical validation.

- **Lack of Clear Explanation**: Despite the use of many formulas, the paper lacks a clear explanation of why the Influence Function was used.

questions:
- The number of datasets and baselines used in the experimental section is limited, which fails to sufficiently demonstrate the generalizability and effectiveness of the proposed method.

- What is the necessity of using the Influence Function?
</content>
</thread_object>

</thread>
<thread>
creation date:
1732455617133

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
General Response (Revision Updates)

comment:
Dear Reviewers and ACs,

Thank you for providing your detailed, insightful, and helpful review comments. We addressed your concerns in our responses to each reviewer and revised the paper based on your suggestions. We greatly appreciate how the reviewers' comments have strengthened our paper. Below, we summarize the key revisions made.

1. We add experiments on Cifar-100 dataset. The results are listed in Appendix F.2 in the revision.
2. We add experiments on ANIMAL-10 dataset, a **real-world noisy dataset**, to further demonstrate the robustness of our ECIF. The results are listed in Appendix F.3 in the revision.
3. We use ClipScore as a **baseline** method and conduct harmful data removal, while ClipScore cannot attribute data but only evaluate data quality. This is due to the lack of alternative baseline methods available for this task. The results are also listed in Appendix F.3 in the revision.
4. We update the description of retraining from 'baseline' to 'ground truth' in the Baselines and Evaluation Metric part.

We noticed that reviewers may have some misunderstandings when comparing retrain and ECIF. For our data attribution task, retrain serves as a ground truth rather than a baseline. By retraining the model after removing the data to be evaluated, we quantitatively provide the influence score of the data. However, retraining is impossible for large models and thus it is the ideal cas rather than an efficient method. 

Our proposed ECIF method is an efficient and accurate approximation of this ground truth. As shown in Table 1, the estimation results of ECIF are very similar to those of the retrain method, while the runtime is significantly reduced. These results show the efficiency of our method. Therefore, to avoid misinterpretation, we update the description of retraining from 'baseline' to 'ground truth' in the Baselines and Evaluation Metric part.


We sincerely hope that the responses and the revised paper assist in your re-evaluation of our paper.

Sincerely,\
Authors
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</thread>


Metareview:
metareview:
This paper proposes the Extended Influence Function for Contrastive Loss (ECIF), designed to evaluate data influence in contrastive learning models for multimodal large language models (MLLMs). While the idea of using influence functions for contrastive loss is novel, the paper suffers from significant shortcomings. The experiments are limited in scope, relying on small and simplistic datasets that do not adequately demonstrate the robustness or scalability of the proposed method. Additionally, key assumptions, such as the linearity of influence from positive and negative samples, lack sufficient justification. Despite attempts to address these issues during the rebuttal, the revisions were not comprehensive enough to resolve major concerns. Therefore, this paper is not ready for acceptance in its current form.