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MLLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training (2407.21439v2)

Published 31 Jul 2024 in cs.AI, cs.CL, and cs.LG
MLLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training

Abstract: Multimodal LLMs (MLLMs) have demonstrated remarkable capabilities in processing and generating content across multiple data modalities. However, a significant drawback of MLLMs is their reliance on static training data, leading to outdated information and limited contextual awareness. This static nature hampers their ability to provide accurate and up-to-date responses, particularly in dynamic or rapidly evolving contexts. Though integrating Multimodal Retrieval-augmented Generation (Multimodal RAG) offers a promising solution, the system would inevitably encounter the multi-granularity noisy correspondence (MNC) problem, which hinders accurate retrieval and generation. In this work, we propose RagVL, a novel framework with knowledge-enhanced reranking and noise-injected training, to address these limitations. We instruction-tune the MLLM with a simple yet effective instruction template to induce its ranking ability and serve it as a reranker to precisely filter the top-k retrieved images. For generation, we inject visual noise during training at the data and token levels to enhance the generator's robustness. Extensive experiments on the subsets of two datasets that require retrieving and reasoning over images to answer a given query verify the effectiveness of our method. Code and models are available at https://github.com/IDEA-FinAI/RagVL.

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Analysis of "MLLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training"

The paper "MLLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training" presents significant advancements in the field of Multimodal Retrieval-augmented Generation (Multimodal RAG) within the domain of Multimodal LLMs (MLLMs). The primary contribution is the RagVL framework which addresses the inherent limitations of current MLLM approaches, particularly the multi-granularity noisy correspondence (MNC) problem.

Key Contributions

  1. Innovative Framework for Multimodal RAG:
    • RagVL, the proposed framework, integrates knowledge-enhanced reranking and noise-injected training to handle the MNC problem effectively. The framework enhances the existing multimodal RAG by employing a reranker that instruction-tunes an MLLM, leveraging its cross-modal understanding capabilities to improve retrieval accuracy and generate more relevant content.
  2. Addressing MNC:
    • The paper identifies two types of noise: coarse-grained (query-caption) and fine-grained (query-image). RagVL mitigates these through a two-pronged strategy, leveraging the reranked top candidates and adaptive filtering to ensure more precise matches during the retrieval process, while injecting noise at both data and token levels during training to increase model robustness.
  3. Robust Experimental Validation:
    • The authors conduct extensive experiments using datasets such as WebQA and MultimodalQA. They demonstrate significant improvements in relevant recall metrics, notably achieving high levels of Recall at multiple top-k thresholds across these datasets, reflecting the efficacy of the RagVL method in improving multimodal RAG processes.

Numerical Results and Observations

  • The effectiveness of the framework is evident from the R@1, R@5, and R@10 results in retrieval tasks. By inducing ranking ability in MLLMs via instruction tuning, RagVL consistently exhibited superior precision and accuracy compared to traditional retrieval approaches, such as relying solely on CLIP.
  • It is noteworthy that the framework achieved a perfect match between performance and oracle settings in several scenarios (e.g., R@20 on MultimodalQA), affirming the strong contribution of their reranking methodology in filtering relevant visual data.

Theoretical and Practical Implications

  • Theoretical: The paper suggests a conceptual extension to how MLLMs can be integrated with retrieval systems, offering a pathway to augment static models with dynamic, multimodal data effectively. This approach not only extends the lifecycle of learned models by continuously updating their contextual awareness but also highlights the potential to bridge large gaps between current models and the aspirations for AGI.
  • Practical: Practically, this research offers immediate applications in real-time multimodal information retrieval and interaction systems, improving the factual accuracy and interpretability of generated outputs within dynamic environments. The deployment of RagVL can enhance systems where multimodal data is rapidly evolving, such as multimedia retrieval engines and enhanced virtual assistants.

Future Developments

Speculating on future developments stemming from this paper, further research could explore optimizing the efficiency and scalability of the reranking process, particularly through enhancing the computational speed of the reranker or incorporating more sophisticated filtering techniques. Additionally, applying the framework to other multimodal domains, such as video-text retrieval or interactive AI-driven content creation, presents an exciting avenue for extending the reach and capabilities of RagVL.

In conclusion, the paper presents a substantial advancement in the field of multimodal retrieval-augmented generation, rooted in a rigorous and technically sophisticated framework. The potential applications of this research in various AI-driven sectors underscore its importance, offering pathways for future exploration and refinement in multimodal data processing.

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References (41)
  1. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023.
  2. Cm3: A causal masked multimodal model of the internet. arXiv preprint arXiv:2201.07520, 2022.
  3. Reliable, adaptable, and attributable language models with retrieval. arXiv preprint arXiv:2403.03187, 2024.
  4. Improving language models by retrieving from trillions of tokens. In International conference on machine learning, pp.  2206–2240. PMLR, 2022.
  5. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901, 2020.
  6. Webqa: Multihop and multimodal qa. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.  16495–16504, 2022.
  7. Murag: Multimodal retrieval-augmented generator for open question answering over images and text. arXiv preprint arXiv:2210.02928, 2022.
  8. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.
  9. The faiss library. 2024.
  10. Palm-e: An embodied multimodal language model. arXiv preprint arXiv:2303.03378, 2023.
  11. Making the v in vqa matter: Elevating the role of image understanding in visual question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.  6904–6913, 2017.
  12. Accelerating large-scale inference with anisotropic vector quantization. In International Conference on Machine Learning, pp.  3887–3896. PMLR, 2020.
  13. Retrieval augmented language model pre-training. In International conference on machine learning, pp.  3929–3938. PMLR, 2020.
  14. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840–6851, 2020.
  15. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021.
  16. Language is not all you need: Aligning perception with language models. Advances in Neural Information Processing Systems, 36, 2024.
  17. Learning with noisy correspondence for cross-modal matching. Advances in Neural Information Processing Systems, 34:29406–29419, 2021.
  18. Gqa: A new dataset for real-world visual reasoning and compositional question answering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.  6700–6709, 2019.
  19. Leveraging passage retrieval with generative models for open domain question answering. arXiv preprint arXiv:2007.01282, 2020.
  20. Dense passage retrieval for open-domain question answering. arXiv preprint arXiv:2004.04906, 2020.
  21. Mitigating object hallucinations in large vision-language models through visual contrastive decoding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.  13872–13882, 2024.
  22. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems, 33:9459–9474, 2020.
  23. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International conference on machine learning, pp.  19730–19742. PMLR, 2023.
  24. Ra-dit: Retrieval-augmented dual instruction tuning. arXiv preprint arXiv:2310.01352, 2023.
  25. Improved baselines with visual instruction tuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.  26296–26306, 2024a.
  26. Visual instruction tuning. Advances in neural information processing systems, 36, 2024b.
  27. Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning. arXiv preprint arXiv:2209.14610, 2022.
  28. Ok-vqa: A visual question answering benchmark requiring external knowledge. In Proceedings of the IEEE/cvf conference on computer vision and pattern recognition, pp.  3195–3204, 2019.
  29. Mm1: Methods, analysis & insights from multimodal llm pre-training. arXiv preprint arXiv:2403.09611, 2024.
  30. Ocr-vqa: Visual question answering by reading text in images. In 2019 international conference on document analysis and recognition (ICDAR), pp.  947–952. IEEE, 2019.
  31. Learning transferable visual models from natural language supervision. In International conference on machine learning, pp.  8748–8763. PMLR, 2021.
  32. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research, 21(140):1–67, 2020.
  33. Zero-shot text-to-image generation. In International conference on machine learning, pp.  8821–8831. Pmlr, 2021.
  34. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision, pp.  618–626, 2017.
  35. Multimodalqa: Complex question answering over text, tables and images. arXiv preprint arXiv:2104.06039, 2021.
  36. Plug-and-play vqa: Zero-shot vqa by conjoining large pretrained models with zero training. arXiv preprint arXiv:2210.08773, 2022.
  37. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023.
  38. Seeing the image: Prioritizing visual correlation by contrastive alignment. arXiv preprint arXiv:2405.17871, 2024.
  39. Enhancing multi-modal multi-hop question answering via structured knowledge and unified retrieval-generation. In Proceedings of the 31st ACM International Conference on Multimedia, pp.  5223–5234, 2023.
  40. Xi Ye and Greg Durrett. The unreliability of explanations in few-shot prompting for textual reasoning. Advances in neural information processing systems, 35:30378–30392, 2022.
  41. Scaling autoregressive models for content-rich text-to-image generation. arXiv preprint arXiv:2206.10789, 2(3):5, 2022.
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Authors (4)
  1. Zhanpeng Chen (3 papers)
  2. Chengjin Xu (36 papers)
  3. Yiyan Qi (21 papers)
  4. Jian Guo (76 papers)
Citations (4)
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  1. GitHub - IDEA-FinAI/RagLLaVA (33 stars)
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