ChartMoE: Mixture of Diversely Aligned Expert Connector for Chart Understanding

Authors: Zhengzhuo Xu, Bowen Qu, Yiyan Qi, SiNan Du, Chengjin Xu, Chun Yuan, Jian Guo
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Multimodal Large Language Models, Chart Reasoning, Mixture of Expert

Abstract:
Automatic chart understanding is crucial for content comprehension and document parsing. Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in chart understanding through domain-specific alignment and fine-tuning. However, current MLLMs still struggle to provide faithful data and reliable analysis only based on charts. To address it, we propose ChartMoE, which employs the Mixture of Expert (MoE) architecture to replace the traditional linear projector to bridge the modality gap. Specifically, we train several linear connectors through distinct alignment tasks, which are utilized as the foundational initialization parameters for different experts. Additionally, we introduce ChartMoE-Align, a dataset with nearly 1 million chart-table-JSON-code quadruples to conduct three alignment tasks (chart-table/JSON/code). Combined with the vanilla connector, we initialize different experts diversely and adopt high-quality knowledge learning to further refine the MoE connector and LLM parameters. Extensive experiments demonstrate the effectiveness of the MoE connector and our initialization strategy, e.g., ChartMoE improves the accuracy of the previous state-of-the-art from 80.48% to 84.64% on the ChartQA benchmark.

Decision: accept (oral)
Reviewer scores: 6, 5, 8, 8

Reviews and discussion:
<thread>
creation date:
1730646171193

thread_type:
official_review

<thread_object>
creator: reviewer_ka6r

<content>
summary:
This paper proposed ChartMoE, which employs the Mixture of Expert (MoE) architecture to replace the traditional linear projector to bridge the modality gap.

strengths:
1. First introduce MoE MLLM for chart task, the model architechture design which using MoE in connector is novel.
2. Detailed experiments and analysis, extensivequantitativeandqualitativestudies.
3. Introduced a large dataset for chart pre-train data.

weaknesses:
1. limmited innovation: MoE on multimodal language models has been explored in other domains. There's a trade-off between the performance gain and the increase of model parameters and inference time. 
2. The contributions of MoE module is unclear, more theoretical analysis is needed such as the knowledge in the routing to differnernt experts.

questions:
Have you ever tried training on your alignment data with random initialization of expert parameters and balanced loss from scratch? It will better prove the significance of this work.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730648070011

thread_type:
official_review

<thread_object>
creator: reviewer_wxls

<content>
summary:
The paper introduces ChartMoE, a multi-task aligned and instruction-tuned MLLM designed for complex chart understanding and reasoning. The key contribution is the replacement of the traditional linear connector with the Mixture of Expert (MoE) architecture, which improves chart understanding by bridging the modality gap. Additionally, a new dataset called ChartMoE-Align is introduced, containing nearly 1 million chart-table-JSON-code quadruples for alignment training. The proposed three-stage training paradigm and high-quality knowledge learning approach result in significantly improved performance compared to the previous state-of-the-art on various benchmarks.

strengths:
This paper incorporates a Mixture of Expert architecture to bridge the gap between charts and language models and offers a valuable insight for the expert initialization manner. The creation of the ChartMoE-Align dataset with nearly 1 million chart-table-JSON-code quadruplets is a significant contribution to the field, allowing for detailed and meticulous chart alignment pre-training.

The paper is clear and well written in general, is well motivated, and comes with an extensive and comprehensive ablation study.

weaknesses:
The paper focuses on conducting experiments on a single Vision Encoder and Large Language Model, which limits the generalizability of the proposed method. It would be beneficial to test the effectiveness of ChartMoE on a diverse set of MLLMs to ensure its applicability across different models and scenarios.

The paper does not thoroughly discuss potential limitations or challenges that may arise when implementing ChartMoE in practical applications.

questions:
1. In line 369, the error analysis suggests that many errors stem from the limitations of the evaluation metric, namely string matching. Are other comparison models also limited by this?
2. Can you provide more detailed explanations of the data construction process, such as how code templates for different types of charts were obtained? Were they manually constructed?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730694140084

thread_type:
official_review

<thread_object>
creator: reviewer_6bc6

<content>
summary:
This work introduces ChartMoE, a multimodal large language model  that enhances automatic chart understanding through a MoE architecture. Unlike traditional linear connectors, ChartMoE uses diverse expert connectors aligned with specific tasks (chart-table, chart-JSON, and chart-code) to bridge the gap between visual encoders and large language models. The paper also presents ChartMoE-Align, a dataset with nearly 1 million chart-table-JSON-code quadruples for training.

strengths:
1. ChartMoE’s use of task-specific expert connectors in a Mixture of Experts (MoE) framework provides a  solution to multimodal chart understanding.

2. ChartMoE-Align, a large-scale dataset with varied chart alignments (table, JSON, code).

3. The three-stage training paradigm increass its accuracy in extracting and interpreting numerical data.

weaknesses:
1. The use of MOE in MLLMs is not particularly novel, as several prior works have already explored MoE structures to enhance model performance. From an innovation standpoint, this reliance on MoE does not introduce a distinctly new approach and could be considered a weakness in terms of contribution.

2. The multi-expert structure, along with the diverse alignment tasks, adds significant complexity to ChartMoE’s architecture. Also, the training data, being mostly synthetic, might limit the model’s ability.

questions:
see above
</content>
</thread_object>

</thread>
<thread>
creation date:
1730787309202

thread_type:
official_review

<thread_object>
creator: reviewer_jnmd

<content>
summary:
The paper introduces ChartMoE, a novel approach that leverages a Mixture of Expert (MoE) architecture to improve automatic chart understanding with Multimodal Large Language Models (MLLMs). ChartMoE addresses the limitations of existing MLLMs by using specialized linear connectors for diverse expert initialization, coupled with a unique dataset, ChartMoE-Align, containing nearly one million chart-related quadruples. This setup significantly enhances data interpretation from charts, pushing the accuracy on the ChartQA benchmark from 80.48% to 84.64%, showcasing a substantial improvement over previous methods.

strengths:
S1. Innovative Methodology: The introduction of the ChartMoE method, which utilizes a Mixture of Experts (MoE) architecture, represents a significant innovation in the field of automatic chart understanding. This approach addresses the modality gap effectively and could potentially set a new direction for future research in multimodal learning systems.

S2. Comprehensive Dataset: The creation of the ChartMoE-Align dataset is commendable. Its large scale and diversity are well-suited for robust pre-training in chart alignment tasks. This dataset not only serves the immediate needs of the study but also provides a valuable resource for the broader research community to explore complex multimodal tasks involving charts and text.

S3. Extensive Experimental Validation: The paper presents extensive experiments that demonstrate the effectiveness of the ChartMoE approach. The thoroughness of these experiments, which include a variety of scenarios and detailed performance metrics, establishes a strong benchmark for future comparative studies.

S4. Clear Writing: The manuscript is exceptionally well-written, providing clear explanations and methodical presentation of the concepts and methodologies involved. This clarity enhances the reader's understanding and appreciation of the work's contributions to the field.

weaknesses:
W1: Details on Dataset Construction

The paper lacks critical details on the dataset construction process. Clarifications are needed regarding the criteria used to select and filter charts for inclusion in the dataset. Specifically, the process for generating meta CSV data via Large Language Models (LLM) requires more transparency.
More details on which LLMs were used and the code templates for different types of charts are missing. Such information is crucial for reproducibility and for understanding the dataset's applicability to other multimodal tasks.
The manuscript should discuss the steps taken to ensure the quality of the data, including any validation mechanisms or controls used during dataset assembly.

W2: Clarification of Experiment Results

The paper briefly mentions that the proposed method shows weaknesses in some settings compared to baselines, as detailed in Tables 2 and 3. However, these points are not adequately addressed or explained. A more thorough analysis of why ChartMoE underperforms in these instances would be valuable for readers and for future improvements to the method.

questions:
Q1: Modality-Specific Contributions

It would be beneficial for the paper to elaborate on the unique contributions of different representations (JSON, code, and chart) in the context of chart-related tasks. Understanding how each representation impacts the model's learning and performance could provide insights into optimizing future models for similar tasks.


Q2: Necessity and Efficiency of Large-Scale Alignment Dataset

The heuristic approach to generating a large-scale dataset raises questions about the efficiency and necessity of such a volume of data. Is there potential to achieve similar performance with a smaller, possibly more curated dataset? This exploration could lead to more resource-efficient training processes and better generalization in practical applications.
</content>
</thread_object>

</thread>
<thread>
creation date:
1732070153191

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Incorporated feedback and new results

comment:
Thank all reviewers for the thorough review and detailed comments. To address the concerns, we have made the following revisions to the paper:

1. Emphasized the **novelty** of ChartMoE. ChartMoE differs from previous methods in terms of motivation, initialization manner, and MoE application position. We have added this discussion to Appendix `D.1`.
2. Analyzed the **model complexity** and **training complexity** of ChartMoE and included the results in Appendix `D.1`.
3. Added a detailed description of the ChartMoE-Align dataset, including data sources, data processing methods, JSON / Code template, data filtering & cleaning, and data quality control. We have added this discussion to Appendices `C.1-4`.
4. Included the performance results of ChartMoE on other MLLMs (e.g., `LLaVA-7B`) to demonstrate the generalizability of our methods (MoE connector, Diverse Align, etc.). We have added the new experimental results to Appendix `D.2`.
5. Provided a deeper analysis of the experimental results in `Tables 2-3`. By examining fine-grained results, we explain the performance of ChartMoE. We have added the new experimental results and discussions to Appendix `D.3`.
6. Added a discussion on the limitations of ChartMoE, presented in Appendix `D.4`.
7. Improved the ablation experiment settings and added more discussions on the effectiveness of synthetic data based on the reviewers' suggestions. Please refer to the specific discussions with the reviewers.
</content>
</thread_object>

</thread>


Metareview:
metareview:
The paper introduces ChartMoE, a model that enhances complex chart understanding through a Mixture of Expert (MoE) architecture, replacing traditional linear projectors. It also presents the ChartMoE-Align dataset, designed for three specific alignment tasks, demonstrating the model's practical utility. The experimental results are robust and convincing. I recommend that the authors strengthen the presentation of their novel approach in the Introduction to highlight the advancements over existing methods better.