Narrowing Information Bottleneck Theory for Multimodal Image-Text Representations Interpretability

Authors: Zhiyu Zhu, Zhibo Jin, Jiayu Zhang, NAN YANG, Jiahao Huang, Jianlong Zhou, Fang Chen
Primary area: interpretability and explainable AI
Keywords: Interpretability, CLIP

Abstract:
The task of identifying multimodal image-text representations has garnered increasing attention, particularly with models such as CLIP (Contrastive Language-Image Pretraining), which demonstrate exceptional performance in learning complex associations between images and text. Despite these advancements, ensuring the interpretability of such models is paramount for their safe deployment in real-world applications, such as healthcare. While numerous interpretability methods have been developed for unimodal tasks, these approaches often fail to transfer effectively to multimodal contexts due to inherent differences in the representation structures. Bottleneck methods, well-established in information theory, have been applied to enhance CLIP's interpretability. However, they are often hindered by strong assumptions or intrinsic randomness. To overcome these challenges, we propose the Narrowing Information Bottleneck Theory, a novel framework that fundamentally redefines the traditional bottleneck approach. This theory is specifically designed to satisfy contemporary attribution axioms, providing a more robust and reliable solution for improving the interpretability of multimodal models. In our experiments, compared to state-of-the-art methods, our approach enhances image interpretability by an average of 9\%, text interpretability by an average of 58.83\%, and accelerates processing speed by 63.95\%. Our code is publicly accessible at https://github.com/LMBTough/NIB.

Decision: accept (poster)
Reviewer scores: 8, 3, 5, 5

Reviews and discussion:
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creation date:
1730110365631

thread_type:
official_review

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creator: reviewer_nfc1

<content>
summary:
This paper tackles the challenge of enhancing interpretability in multimodal image-text models like CLIP, which are widely used in applications demanding high reliability, such as healthcare. The authors introduce the Narrowing Information Bottleneck Theory, a re-engineered bottleneck approach that aligns with modern attribution standards to improve interpretability. Experimental results demonstrate significant gains: a 9% improvement in image interpretability, a 58.83% boost in text interpretability, and a 63.95% increase in processing speed compared to state-of-the-art methods.

strengths:
- Clarity and Structure: The paper is well-organized and written in a clear, accessible manner, making it easy to follow even for readers who may be less familiar with the technical aspects of interpretability in multimodal models.
- Code Accessibility: The authors have made their code publicly available, which greatly enhances the reproducibility of the research and provides valuable resources for the community to build on this work.
- Mathematical Rigor: The theoretical formulae are meticulously derived and clearly presented, enhancing the paper’s rigor and allowing readers to grasp the technical foundation of the Narrowing Information Bottleneck Theory.
- Comprehensive Literature Review: The paper thoroughly addresses related work, including important methods like M2IB, COCOA, and FALCON. This contextualization not only strengthens the theoretical foundation but also allows readers to understand how the proposed method improves upon existing approaches.

weaknesses:
- Paper Length: The paper exceeds the ICLR25 page limit, taking more than 10 pages for the main text. According to the ICLR25 “Call for Papers,” submissions with main text on the 11th page are subject to desk rejection. The authors may need to condense certain sections to adhere to submission guidelines.
- Application-Specific Relevance: Although the paper emphasizes the importance of interpretability for high-risk applications such as medical diagnosis and content moderation, it lacks application-specific experiments. No quantitative or qualitative evaluations on healthcare or content moderation datasets are included, which would provide critical insight into the real-world applicability of the proposed method.
- Experiment Scope: The experimental evaluation appears limited. In contrast to the M2IB paper, which includes evaluations on diverse datasets (image captioning, radiology, and remote sensing), this paper only tests on standard image datasets. A broader range of datasets would strengthen the evidence for the proposed method’s versatility and robustness in high-stakes domains.
- Table Formatting: The significant figures in Tables 2 and 3 lack consistency, which affects readability and may lead to confusion when comparing results.
- Minor Typos:
  - Line 44-45: “blackbox” should be formatted as ``blackbox’’.
  - Line 322: “Appendix” is referenced without any supporting link, in contrast to Line 296, which does provide a reference.

questions:
- Can the authors clarify whether the current content will be revised to meet ICLR’s page limitations for the main text?
- Have the authors considered adding experiments on healthcare or content moderation datasets to validate the method’s relevance for high-risk applications?
- Given the limited dataset variety in the current experiments, could the authors discuss how the proposed method might generalize to other domains, even by qualitative analysis?
- Would it be possible to revise Tables 2 and 3 to ensure consistent significant figures for improved readability?
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<thread>
creation date:
1730624878277

thread_type:
official_review

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creator: reviewer_ycuc

<content>
summary:
This paper presents the Narrowing Information Bottleneck Theory (NIBT) as a solution to the challenges of randomness and hyperparameter sensitivity in the interpretation of multimodal models like CLIP. The authors systematically summarize existing methods of Multimodal Image-Text Representation (MITR) interpretability and modify the traditional Bottleneck approach with NIBT, which enhances the interpretability of both image and text representations. The proposed method shows improved performance in attribution accuracy and computational efficiency across various datasets.

strengths:
1.	The paper systematically and theoretically analyzes the current MITR interpretability methods. The theory is based on information theory and seems reasonable. 
2.	The proposed NIBT is simple by replacing the univariate Gaussian noise in the bottleneck layer with the multivariate gaussian distribution to control all dimensions of the model hidden states.

weaknesses:
1.	The proposed method is simple by changing univariate distribution to multivariate distribution, but it's unclear whether it will correlate with the limitations of IBP. The theoretical analysis and the proposed algorithm seem loosely connected to me. I don't see the proposed method as a direct result of the theoretical analysis.
2.	The story is a little bit hard to follow. It would be better for authors to make the story clearer.

questions:
1.	The proposed method NIBT claims no randomness. But the computation of z_{ic} still contains the sampling noise.
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<thread>
creation date:
1730660217849

thread_type:
official_review

<thread_object>
creator: reviewer_bwwd

<content>
summary:
The paper addresses the challenge of improving interpretability in Vision-Language Pre-trained Models, such as the multi-modal model CLIP (Contrastive Language-Image Pretraining) which maps images and texts into a shared embedding space. Existing interpretability methods, designed for unimodal tasks, are generally insufficient for explaining the complex relationships between visual and textual modalities. To assess the interpretability in the model CLIP, Wang et al (2023) recently introduced the Multi-modal Information Bottleneck (M2IB) approach, aiming to find attribution parameters that maximize the likelihood of observing features of one modality given features associated with the respective other modality.

Following from Wang et al (2023), here, the authors propose the Narrowing Information Bottleneck Theory (NIBT), a new interpretability approach that redefines bottleneck methods in information theory. NIBT targets the randomness and hyperparameter dependency associated with previous approaches, such as M2IB, to deliver more deterministic interpretability outcomes. The method includes a mechanism to pinpoint negative features that adversely impact model predictions, aiming to further enhance the interpretability.

Through simulations on the datasets Conceptual Captions, ImageNet, and Flickr8k, the authors state that NIBT significantly improves interpretability and processing speed compared to state-of-the-art methods.

strengths:
The paper introduces the Narrowing Information Bottleneck Theory (NIBT), a new interpretability method that redefines traditional bottleneck concepts from information theory. This approach departs from the randomness and hyperparameter sensitivity inherent in previous methods, enabling a deterministic alternative for multimodal representation interpretability.

The concept of attributing negative features—dimensions that negatively impact model performance—is original. This feature provides an innovative perspective on model understanding, distinguishing NIBT from other methods focused solely on positive attributions.

The paper provides theoretical derivations and mathematical proofs for its main claims.

The method is assessed across three datasets, including Conceptual Captions, ImageNet, and Flickr8k.

weaknesses:
1. The Narrowing Information Bottleneck Theory (NIBT) is introduced to address limitations in previous bottleneck methods by providing a deterministic approach, aiming to reduce randomness and dependency on hyperparameters. The method states to use a single scalar λ to control information flow across all feature dimensions. This simplification is significant:

1.1. The scalar λ as a universal control across all dimensions suggests a uniform bottleneck effect, which can be problematic if different features vary significantly in their contribution or sensitivity. In practice, applying a single λ across all dimensions might oversimplify the dynamics of feature importance, potentially reducing interpretability in models where feature contributions are heterogeneous.

1.2. The noise distribution, ϵ∼N(0,σ^2), plays a role in regularizing the information flow by adding Gaussian noise to the controlled dimensions. This assumption is justified under the simplification of feature distributions. However, the authors should justify why the Gaussian assumption holds across different multimodal applications, as non-Gaussian feature distributions might invalidate this approach or reduce its robustness.


2. Equations

2.1. Equation 1 is well-grounded in information theory, with a clear goal of maximizing relevant information while minimizing redundancy with respect to the input. The critical factor here is the interaction between λ and β. While NIBT eliminates β, this raises questions about managing trade-offs between task-relevant and redundant information. The authors should address potential limitations of a fixed λ and how it impacts feature discrimination.

2.2. Equation 2: The decomposition of features into spatial (i) and channel (c) dimensions adds specificity but introduces complexity. By normalizing the interaction with Gaussian noise across spatial dimensions, the method assumes consistency in spatial features' importance, which may not hold in multimodal contexts where image regions and text embeddings exhibit varied significance.


3. Theorems 

3.1. Theorem 1: it claims that mutual information, I(z~(λ),x), is a monotonically increasing function of λ, with I(z~(0),x)=0. The proof relies on KL divergence and a Gaussian assumption. While monotonicity is proven under Gaussian noise, real-world feature distributions could violate this condition. The impact of deviations from Gaussianity on this monotonic relationship needs to be tested. Also, the assumption of independence between noise and features for bottleneck properties needs further validation, as dependency might arise naturally in multimodal models where spatial and channel-wise correlations exist.

3.2. Theorem 2: the reduction of σ toward zero should be more justified. Practically, in cases of low signal-to-noise ratios, completely removing σ could reduce robustness. A discussion of trade-offs in interpretability and stability as σ approaches zero would help add clarity. Also, the claim that I(\bar{z}(λ), x) becomes deterministic as σ decreases suggests a very controlled bottleneck. It is theoretically sound, but validating this deterministic behavior across different datasets (especially with diverse noise levels) is needed to confirm this claim.

3.3. Theorem 3: equation is both novel and complex, stating that negative values represent features that reduce information relevance. The introduction of negative features could indeed improve interpretability by explicitly identifying detrimental features. The authors should show that these attributions correspond to irrelevant or misleading features across model types. Another remark is that the integral's dependency on λ suggests that the influence of each feature diminishes as λ decreases. Yet, this gradual reduction might fail for complex, highly nonlinear multimodal interactions. A closer look at cases where feature relevance changes nonlinearly with λ could show edge cases where the method’s assumptions do not hold.


4. Qualitative Analysis: The paper includes one visual comparison with M2IB only, but a more detailed qualitative assessment would be beneficial. For example, showing more examples where NIBT succeeds and where it fails compared to other methods would provide a better understanding of the strengths and limitations.

For the interpretation of Figure 1, the authors write: "As shown in Figure 1, our proposed method successfully distinguishes and excludes negative properties from the explanation. In Figure 1d, the M2IB method continues to highlight irrelevant negative features, such as the cat’s face, even when the subject is a dog. However, in Figure 1b, our method correctly ignores these negative properties, focusing on more relevant, positive features, showcasing its improved attribution performance." Looking at this unique visualisation, the method proposed seems to lack specificity and highlight areas of the image that are not relevant, compared to M2IB.


5. Computational Efficiency: The authors emphasize the efficiency of NIBT, Yet the experiments lack a detailed breakdown of computational costs relative to model complexity and dataset size. Including this analysis would better illustrate the trade-offs between interpretability and computation.


6. The paper does not address whether the differences in the results between the different methods are statistically significant. Adding statistical tests (e.g., t-tests or bootstrap analysis) would add rigor to the claims of superiority over baseline methods.


7. Regarding the impact of negative feature attribution: The paper does not at the moment empirically validate whether the identified negative features are truly detrimental.


8. The ablation studies on num steps and target layer provide useful insights, but they feel somewhat limited in scope. It would be useful to look at the interplay between num steps, target layer, and other model hyperparameter would give a more holistic view of how these factors influence performance.

questions:
Q1. You write that "the interpretability methods proposed in this paper aim to improve model transparency and are intended for enhancing the trustworthiness of AI systems, especially in sensitive domains such as healthcare" (lines 540-542). You chose to present results on three datasets. Are these datasets sufficiently diverse to reflect real-world multimodal scenarios, especially in domains where interpretability is crucial, like medical imaging? Why did you not choose at least one medical imaging dataset like the M2IB paper?

Q2. Could you please explain why you present the illustration of your method compared to the M2IB method in the paper (and compared to other methods in your code) on a unique image of a cat and dog. Why did you choose two simple and short text captions to illustrate your method? 

Q3. The paper claims an average improvement of 9% in image interpretability and a 58.83% increase in text interpretability. The important increase in text interpretability is striking and suggests that NIBT offers substantial benefits. Could you please justify why the improvement in text interpretability is so much higher than in image interpretability? Are there inherent differences in how text and image features are handled by CLIP that NIBT is better suited for?

Q4.  How does identifying negative features improve model interpretability in practical applications? For instance, could this lead to better debugging of models or highlight biases in the training data? Providing concrete examples would strengthen the argument for this feature. 

Q5. Are there scenarios where the method might fail?
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<thread>
creation date:
1730706774348

thread_type:
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creator: reviewer_bpyu

<content>
summary:
The authors squeeze the information for classification through a bottleneck - in essence they are trying to reduce to the most important features. Importantly, it also shows negative and positive features for classification rather than just positives. To do this, it is trialed on 3 popular datasets, conceptual captions, imagenet, and flickr8k.
 
The method outperforms other interpretability methods on confidence drop, e.g how much model confidence decreases when good features according to the interpretability method are removed, and confidence increase, e.g how much model confidence goes down when negative features are removed.

strengths:
While numerous interpretability methods exist, they cannot cope well with the multimodality od data.
The proposed methods addresses this issue, with 9% improvement for text interpratability, and 58% improvement fo image interpratability.
The proposed method also eliminates randomness an hyperparameter dependency.
Also, current methods stem from gradient analysis, which introduces additional challenges.

weaknesses:
I am curious why the representation of both images and text is of the same dimension, d
Proof of Theorem 1 is obvious, so it should not be a theorem.
Theorem 2 is basically a consequence of Theorem 1. It should not be a theorem. Both are nasically Remarks.

questions:
Can the authors  show interquartile range of performance outcomes in Table 2? in addition, other image examples. whilst the method clearly outperforms the others on average, it is not clear if it is robust.
 Even in the example shown with the image of the dog and cat, it responds strongly with the corner of the image, which is not interpretable.
Similarly, how does the method respond when the image is cluttered with more information, or the relevant information makes up a much smaller total proportion of the image. More examples are needed.
Choice of layer number, can you justify theoretically why layer 9 has been chosed.
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Metareview:
metareview:
The paper introduces the Narrowing Information Bottleneck Theory (NIBT) as a novel interpretability framework for multimodal image-text representations, addressing critical challenges such as randomness and hyperparameter sensitivity in methods like CLIP and M2IB. NIBT proposes a deterministic mechanism for identifying both positive and negative features in multimodal tasks, ensuring more robust and reliable interpretability. The authors substantiated their claims through experimental validation on datasets and obtained clear improvements in image and text interpretability. 

The strengths of this paper lie in its theoretical insights and practical contributions. It provides a novel perspective on interpretability by focusing on negative feature attribution. The method aligns well with modern attribution axioms and eliminates randomness, and is relatively simple to use. The quantitative improvements, combined with detailed ablation studies and public code availability, demonstrate the robustness and reproducibility of the proposed approach.  Most reviewers appreciated the clarity and organization of the manuscript.

However, there are notable weaknesses. The reviewers raised concerns about the lack of diversity in datasets, particularly the absence of high-risk application domains like healthcare. Another concern was the reliance on subjective visualizations, which some reviewers felt lacked specificity compared to competing methods like M2IB. Lastly, minor issues such as formatting inconsistencies in tables and adherence to page limits were flagged and resolved during the rebuttal phase.

I think that this work is overall borderline with both pros and cons. The decision to accept this paper is based on its novelty in theory and method, significant empirical results, and the authors’ engagement during the rebuttal process to address reviewer concerns. After a careful review, I believe that the major concerns from the reviewers have been successfully addressed, and the left ones are less concerning and do not influence the overall decision. In all, I believe that this work would make a valuable contribution. Meanwhile, I suggest the authors take the reviewers' advice carefully and address them in the final revision.