Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent

We sincerely appreciate your valuable feedback. It seems that there are some misunderstanding in the review. Here are some clarifications:

• the scale of the proposed Dyn-VQA dataset is small

Answer:

Regarding the dataset size, we discuss this in Section 3.2 (lines 215-228). While our dataset cannot be directly compared to others in terms of scale, it significantly exceeds others in terms of quality and the human effort involved in its creation:

a. Data Quality: our dataset is entirely manually curated, whereas many other datasets are generated using templates or rule-based methods, which sacrifice the dataset quality. Additionally, our dataset covers a rich variety of domains and categories to ensure unbiased representation.

b. Dynamic Updates: unlike static datasets, ours requires continuous, month-by-month human effort for updates and maintenance to ensure quality. We believe that few organizations can easily commit such labor and passionate. To date, we have annotated three versions of the dataset.

Besides, similar to datasets like ViQuAE and S3VQA, whose data volume is also on the order of thousands, we believe that a dataset of this scale is sufficient to reflect the performance of RAG models.

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• Innovation is limited

Answer:

In this paper, we reveal that previous multimodal RAG methods primarily relied on heuristic retrieval approaches that fail to provide correct, relevant knowledge for dynamic questions effectively. To our knowledge, OmniSearch is the first multimodal retrieval-enhanced agent that dynamically plans a rational retrieval path for input multimodal questions and provides accurate knowledge.

Additionally, you mentioned concerns about the differences between OmniSearch and Chain of Thought (CoT) methods. The fundamental distinction between OmniSearch, as a multimodal agent, and CoT is its ability to utilize tools, interact with the environment, and response to the environment [1]. In contrast, CoT methods primarily stimulate the model's inherent logical reasoning capabilities through prompts. The CoT approach is unable to decouple intermediate processes, therefore can not be integrated with retrieval tools. We have added this discussion in the revised paper.

Besides the innovation in methodology, we constructed Dyn-VQA, which requires more complex retrieval processes compared to existing benchmarks, allowing for a more realistic evaluation of mRAG performance. Therefore, we believe the novelty criticism is somewhat unfounded, as other reviewers also point out the novelty in their reviews.

Reference:

[1] Zhang, et al. Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agents. arXiv 2024.

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• OmniSearch struggles with some questions in case study

Answer:

We acknowledge that OmniSearch does not perform exceptionally in some cases, just like all AI models are not perfect. However, overall, OmniSearch significantly surpasses the performance of GPT4-V combined with various heuristic retrieval methods and various commercial search engine. The purpose of the case study is more to inspire future work.

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• The impacts of different language

Answer:

we fully agree with your suggestion regarding multilingual aspects. Multilingual support is indeed a valuable future direction, particularly for exploring cross-lingual generalization, language gaps, and the benefits of mainstream languages for low-resource languages in the era of LLMs. We plan to explore this broad topic in future work. It is worth noting that most VQA datasets are monolingual, while ours is bilingual, providing a richer evaluation perspective. The dataset from different language perspectives is also analyzed in Appendix D1 (lines 1112-1118).

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• Explain how OmniSearch decomposes the question and decides on the end.

Answer:

The prompts used by OmniSearch can be found in Appendix C.4, which clearly shows how OmniSearch decomposes questions. When OmniSearch outputs <Final Answer>, it indicates that the question has been resolved.

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• Consider evaluating the effectiveness of OmniSearch based on more LLMs or MLLMs.

Answer:

We appreciate your suggestion to evaluate OmniSearch with more LLMs and MLLMs. GPT4-V and Qwen-VL were chosen as the backbone for OmniSearch. They represent the leading models among open-source small models and proprietary large models, respectively, and we believe they are sufficiently representative. Additionally, Table 11 demonstrates the results for more than 10 models.

Dear Reviewer,

Understanding your busy schedule, we wanted to check if the revisions and details provided have addressed your concerns. Please let us know if any issues remain or further clarification is needed. Your feedback is invaluable to us for improving our work.

Thank you for your attention.

Based on your feedback, we have clarified the novelty of our method and further discussed the dataset size. We hope these analyses address your concerns.

Considering that the rebuttal discussion phase for ICLR is about to end, we sincerely hope to receive your further feedback so that we can continue our discussion. Once again, thank you for your hard work and selfless help.

We sincerely appreciate your affirmation of the paper soundness, and kindly ask you to consider increasing the score based on the following clarification:

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• The dataset's limited size of 1.5k samples

Answer:

Regarding the dataset size, we discuss this in Section 3.2 (lines 215-228). While our dataset cannot be directly compared to others in terms of scale, it significantly exceeds others in terms of quality and the human effort involved in its creation:

a. Data Quality: our dataset is entirely manually curated, whereas many other datasets are generated using templates or rule-based methods, which sacrifice the dataset quality. Additionally, our dataset covers a rich variety of domains and categories to ensure unbiased representation.

b. Dynamic Updates: unlike static datasets, ours requires continuous, month-by-month human effort for updates and maintenance to ensure quality. We believe that few organizations can easily commit such labor and passionate. To date, we have annotated three versions of the dataset.

Besides, similar to datasets like ViQuAE and S3VQA, whose data volume is also on the order of thousands, we believe that a dataset of this scale is sufficient to reflect the performance of RAG models.

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• the authors filtered intractable instances that doesn't translate well, this might limit the dataset's diversity or introduce bias

Answer:

We believe that "filtering instances that doesn't translate well" does not introduces bias. On the contrary, this filtering strategy enhances the evaluation accuracy. For example, consider a question like, "What is the latest work of the author of this book in the picture?" (referring to a local Chinese author). This question is appropriate in the Chinese context but not in the English context, since his books have neither English versions nor English introductions. Hence, we just simply discard the English question in this case, which naturally would not be asked in English by the users.

Meanwhile, we fully agree with your suggestion regarding multilingual aspects. Multilingual support is indeed a valuable future direction, particularly for exploring cross-lingual generalization, language gaps, and the benefits of mainstream languages for low-resource languages in the era of LLMs [1,2]. We plan to explore this broad topic in future work. It is worth noting that most VQA datasets are monolingual, while ours is bilingual, providing a richer evaluation perspective. The dataset from different language perspectives is also analyzed in Appendix D1 (lines 1112-1118).

Reference:

[1] Geigle, et al. Babel-ImageNet: Massively Multilingual Evaluation of Vision-and-Language Representations. ACL 2024.

[2] Qin, et al. Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers. arXiv 2024.

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• latency or computational overhead of OmniSearch

Answer:

In Table 6, we have compared the token consumption (i.e., computational overhead) and corresponding performance of different models. We observe that the full OmniSearch model consumes more tokens because solving problems correctly inherently requires more retrieval and LLM thinking processes.

Here, we also reported the average latency for each question. The results show that substituting some modules in OmniSearch with smaller models effectively reduces latency. Moreover, the ratio of search time to model inference time is roughly 2:3, indicating significant potential for optimization in both aspects. It is important to note that latency is a complex system engineering issue that involves not only the model's complexity but also factors network configuration of search APIs, caching strategies for retrieval content, inference model acceleration, and hardware FLOPS, etc.

Additionally, we supplemented the detailed computational cost of OmniSearch on questions with different answer update frequencies (fast, slow, never), which can be found in the response to Reviewer Z1ru.

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• Ablation on the feedback generation effectiveness

Answer:

We have indeed conducted ablation studies on the effectiveness of feedback generation. The feedback in OmniSearch is generated by the sub-question solving model. In Table 6, we compare the performance when using different MLLMs (GPT-4V, Qwen-Vl) as sub-question solvers, i.e., feedback generation effectiveness. We find that overall performance is positively correlated with the sub-question solver's performance. However, the quality of the Planning Model has a more significant impact on overall performance.

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• No GPT-4o as reasoning-based commercial search engine

Answer:

The primary improvement of GPT-4o is in inference speed compared to GPT-4, and its performance is roughly on par with the GPT-4V model. We report its results below. We can observe that the original performance of GPT-4o surpasses that of GPT-4V; however, when equipped with OmniSearch, the performance of GPT-4V is significantly enhanced. Besides, the OmniSearch framework is compatible with arbitrary MLLMs, and we believe that the performance of GPT-4o can also be improved by OmniSearch.

You might also be referring to reasoning-based OpenAI o1, which was released concurrently with our submission. While it does not support multimodal input and cannot handle multimodal QA, highlighting the urgent need for advancements in multimodal RAG agent. Additionally, some commercial search engines in our experiments, such as Gemini, do incorporate reasoning processes.

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• how the AI researchers are selected and trained, and the defination of the classification distribution

Answer: The definitions of the classification labels can be found in lines 170-173 of Section 3.1. We chose graduate students with at least 2 years of research experience as annotators and provided them with detailed annotation instructions.

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• Figure 3 typo: "on" should be "of"

Answer: Sorry for the typo. We will correct it and double-check the entire paper.

Dear Reviewer,

Understanding your busy schedule, we wanted to check if the revisions and details provided have addressed your concerns. Please let us know if any issues remain or further clarification is needed. Your feedback is invaluable to us for improving our work.

Thank you for your attention.

Thank you very much for your valuable suggestions and comments. Based on your advice, we have added a latency analysis of our method and a performance comparison with GPT-4o. We have also further discussed the dataset size. We hope these analyses address your concerns.

Considering that the rebuttal discussion phase of ICLR is about to end, we sincerely hope to receive your further feedback so that we have the opportunity to continue our discussion with you. Once again, thank you for your hard work and selfless help.

We sincerely thank you for your review and feedback. We will make every effort to address your concerns and kindly request you to potentially increase the score based on the following points:

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• Sustainability of Benchmark Accuracy

Answer:

We discuss the regular updates of our dataset in Appendix A.4. As you noted, our dataset is not static and requires continuous human effort for updates and maintenance to ensure quality. While few organizations can easily achieve this, we commit to maintaining regular updates. Currently, we have annotated three versions of the dataset.

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• Definition of "Hops" in Questions

Answer:

The definition of "hops" in our work aligns with past QA research, referring to the number of knowledge rationales required to answer a question. For example, the question "What is the altitude of the mountain in the image?" typically requires two knowledge rationales: the name of the mountain and the corresponding altitude.

While different annotators may have inconsistencies for some complex questions, these issues usually involve more than two knowledge rationales. For example, arguing about whether the quesiton is a three or four hop question. However, this does not affect the annotation accuracy or our motivation: most VQA dataset have fixed two-hop questions. We also hired two extra quality control personnel to ensure consistent and unbiased annotation. Moreover, as shown in Table 3, the average search count of each DynVQA question involved in human question answering exceeds three, significantly surpassing other datasets, which also indicates the challenge of Dyn-VQA in terms of multi-hop question.

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• Missing Related Work

Our work primarily focuses on multimodal retrieval-augmented generation (mRAG). Mainstream mRAG methods are generalized into four categories in method section and are also introduced thoroughly in related work section. To the best of our knowledge, OmniSearch is the first multimodal RAG agent.

The work you mentioned primarily focuses on multi-hop QA for commonsense knowledge (e.g., "Is the temperature high or low in the region (ref. to a refrigerator) where the bottle is located?"), which does not use the mRAG method. But the dataset it used, like A-OKVQA, is relevant to our dataset, which is also discussed in our paper. Hence, we added the citation to Kil et al. (ACL'24) and some multi-hop QA methods in the related work section. Thanks for your suggestions.

Dear Reviewer,

Understanding your busy schedule, we wanted to check if the revisions and details provided have addressed your concerns. Please let us know if any issues remain or further clarification is needed. Your feedback is invaluable to us for improving our work.

Thank you for your attention.

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• Computational Costs of OmniSearch

  Answer:

  We further reported the computational cost of OmniSearch on questions with different answer update frequencies (fast, slow, never). The results indicate that:

  1. Overall, OmniSearch consumes more tokens for more complex questions, such as those with fast or slow answer updates, primarily because these questions inherently require more retrieval steps.
  2. The difference in resource consumption for questions of varying difficulty is more pronounced for the smaller model OmniSearch (Q). This is due to the behavioral differences between GPT-4V and Qwen-VL. Specifically, as described in the Section 6.1, GPT-4V tends to be more rigorous in question-solving, proactively planning verification retrievals to validate final answers, leading to retrieval processes exceeding three steps even for some relatively easier questions.
  3. By comparing rows 1 and 2 in the Table, we can observe that after replacing OmniSearch (G)'s Sub-question Solver with a smaller Qwen-VL-Chat, the total token consumption of Qwen-VL-Chat and GPT-4V is still comparable to the original model. This suggests that OmniSearch (G) effectively offloads computational burden to smaller models without a significant drop in overall performance.

  Planning Model	Sub-question Solver	Fast Input Token	Fast Output Token	Fast Performance	Slow Input Token	Slow Output Token	Slow Performance	Never Input Token	Never Output Token	Never Performance
  OmniSearch (G)	OmniSearch (G)	3098.5	466.3	44.04	3036.5	477.0	49.58	2974.6	483.9	54.45
  OmniSearch (G)	Qwen-VL-Chat	1268.2 (G) + 2165.4 (Q)	403.1 (G) + 148.4 (Q)	38.65	1214.1 (G) + 1925.7 (Q)	358.5 (G) + 112.7 (Q)	44.68	1185.6 (G) + 2138.8 (Q)	398.2 (G) + 119.3 (Q)	52.25
  OmniSearch (Q)	OmniSearch (Q)	10258.2	638.1	35.16	9874.8	634.2	40.89	8866.9	475.3	45.52
  OmniSearch (Q)	GPT-4V	2508.8 (G) + 933.0 (Q)	269.3 (G) + 547.8 (Q)	37.14	2452.5 (G) + 1000.9 (Q)	233.6 (G) + 490.5 (Q)	42.82	2209.6 (G) + 1024 (Q)	329.7 (G) + 606.3 (Q)	47.48

  Additionally, we also reported the average latency for each question. The results show that substituting some modules in OmniSearch with smaller models effectively reduces latency. Moreover, the ratio of search time to model inference time is roughly 2:3, indicating significant potential for optimization in both aspects. It is important to note that latency is a complex system engineering issue that involves not only the model's complexity but also factors network configuration of search APIs, caching strategies for retrieval content, inference model acceleration, and hardware FLOPS, etc.

  Planning Model	Sub-question Solver	Search Time (s/%)	Inference Time (s/%)	Total Time (s)
  OmniSearch (G)	OmniSearch (G)	14.1 (44.8%)	18.4 (55.2%)	31.5
  OmniSearch (G)	Qwen-VL-Chat	12.3 (44.6%)	15.3 (55.4%)	27.6
  OmniSearch (Q)	OmniSearch (Q)	8.5 (38.3%)	13.7 (61.7%)	22.2
  OmniSearch (Q)	GPT-4V	11.8 (45.0%)	14.4 (55.0%)	26.2

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Thank you for your feedback and valuable suggestions. We will make every effort to address your concerns:

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• Explanation of Non-adaptive Retrieval Queries and Overloaded Retrieval Queries

Answer:

We appreciate your feedback on the need for clearer explanations. We have provided further clarification and incorporated these into the revised paper:

• Non-adaptive Retrieval Queries refer to the fixed retrieval processes and query structures of heuristic mRAG methods. These inflexible retrieval strategies fail to adapt to evolving contexts or intermediate findings within a question, hindering the model from re-retrieving to further comprehend, verify, or rethink the question. For example, in Figure 2, question (a) asks, "What is his (the actor Cillian Murphy's) latest film?" A fixed retrieval process returns multiple relevant films, but heuristic methods fail to construct further retrieval based on the retrieved content to distinguish between the sequence of different films.
• Overloaded Retrieval Queries refer to heuristic retrieval methods refer to heuristic mRAG methods merely format a single query by concatenating textual descriptions of objects in images with input questions. A single query carries multiple retrieval aspects, leading to ambiguous retrieval and influx of superficially relevant knowledge yet not essential to the question solving. For example, in Figure 2, question (c) asks, "Which one of them (two actors, Ling Jia, and Teng Shen) grossed more?" Heuristic methods might generate a single query like "Ling Jia, Teng Shen, Which one of them grossed more?", which contains the intent to retrieve box office information for both actors. This mixed query conversely fails to provide precise knowledge for each individual aspect.

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• Phenomenon in Figure 5

Answer:

The phenomenon depicted in Figure 5 is indeed intriguing, showing that different MLLMs perceive the difficulty of the same question differently. We believe this difference stems from several factors:

1. Capability of Image Encoder: Different MLLMs use different pre-trained image encoders to extract visual features. Due to variations in pre-training tasks, these encoders differ in their ability to understand image semantics. For example, BEIT excels in leveraging finer-grained image semantics, such as object and background information, by pre-training on masked image modeling. In contrast to the VIT model which learns the overall image semantics through image classifying images in the Image Net dataset. And the image encoder of BLIP also captures richer semantics than the VIT model owing to its linkage with the text modality.
2. Instruction Training Data: The instruction data used to train different MLLMs vary significantly, which affects the model's inner capabilities. For example, Qwen-VL has been trained with a relatively greater volume of OCR Instruction data compared to other MLLMs, making it excel at tasks involving text within images.
3. Dataset Characteristics: Dyn-VQA encompasses a wider variety of question types compared to existing VQA datasets, requiring models to possess more comprehensive capabilities. An ideal model must have robust abilities in understanding input questions and analyzing multimodal retrieval content, such as multi-image understanding, commonsense reasoning, and cross-modal inference.