Adaptive LLM Reranking for Multimodal Information Retrieval
Keywords:
multimodal retrieval; large language models; adaptive reranking; information retrieval; governance; sustainability; robustness; fairness; socio-technical systemsAbstract
Multimodal information retrieval is increasingly expected to operate across text, images, audio, video, structured records, and sensor streams, often within the same query session. Conventional retrieval cascades rely on modality-specific encoders, approximate nearest-neighbor search, and lightweight rerankers, but these pipelines struggle when intent is ambiguous, modality relevance is uneven, or context shifts rapidly. Large language models offer a promising reranking substrate because they can reason over heterogeneous evidence, follow nuanced instructions, and synthesize relevance judgments across modalities. Yet their integration into retrieval infrastructure raises difficult questions about latency, cost, energy, privacy, fairness, robustness, and governance. This paper examines adaptive LLM reranking as a system-level capability rather than a isolated model upgrade. It develops an architectural account of modality-aware recall, adaptive orchestration, cross-modal evidence fusion, and policy-constrained ranking. It then analyzes data and representation governance, deployment and sustainability trade-offs, robustness and fairness evaluation, and organizational implications for search platforms, enterprises, healthcare, education, and public-sector systems. The central argument is that adaptive reranking must be treated as socio-technical infrastructure. Quality gains are real, but they are contingent on explicit trade-off management, continuous evaluation, transparent reporting, and governance mechanisms that connect model behavior to institutional accountability.
References
1. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008.
2. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 4171–4186). Association for Computational Linguistics.
3. Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning transferable visual models from natural language supervision. In Proceedings of the 38th International Conference on Machine Learning (Vol. 139, pp. 8748–8763). PMLR.
4. Li, J., Li, D., Savarese, S., & Hoi, S. (2023). BLIP-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 19730–19742). PMLR.
5. Liu, H., Li, C., Wu, Q., & Lee, Y. J. (2023). Visual instruction tuning. Advances in Neural Information Processing Systems, 36, 34892–34916.
6. Nogueira, R., & Cho, K. (2019). Passage re-ranking with BERT. arXiv preprint arXiv:1901.04085.
7. Khattab, O., & Zaharia, M. (2020). ColBERT: Efficient and effective passage search via contextualized late interaction over BERT. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 39–48). Association for Computing Machinery.
8. Karpukhin, V., Oğuz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-t. (2020). Dense passage retrieval for open-domain question answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (pp. 6769–6781). Association for Computational Linguistics.
9. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 33, 9459–9474.
10. Jin, C., Peng, H., Zhang, A., Chen, N., Zhao, J., Xie, X., ... & Metaxas, D. N. (2025, May). Rankflow: A multi-role collaborative reranking workflow utilizing large language models. In Companion Proceedings of the ACM on Web Conference 2025 (pp. 2484-2493).
11. Sun, W., Yan, L., Ma, X., Wang, S., Ren, P., Chen, Z., ... & Ren, Z. (2023). Is ChatGPT good at search? Investigating large language models as re-ranking agents. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (pp. 14918–14937). Association for Computational Linguistics.
12. Pradeep, R., Sharifymoghaddam, S., & Lin, J. (2023). RankVicuna: Zero-shot listwise document reranking with open-source large language models. arXiv preprint arXiv:2309.15088.
13. Chen, W., Hu, H., Chen, X., Verga, P., Cohen, W. W., & Wang, W. (2022). MuRAG: Multimodal retrieval-augmented generator for open question answering over images and text. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (pp. 5558–5570). Association for Computational Linguistics.
14. Yasunaga, M., Aghajanyan, A., Shi, H., James, R., Leskovec, J., Liang, P., ... & Fried, D. (2023). Retrieval-augmented multimodal language modeling. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 39755–39769). PMLR.
15. Zhu, D., Chen, J., Shen, X., Li, X., & Elhoseiny, M. (2023). MiniGPT-4: Enhancing vision-language understanding with advanced large language models. arXiv preprint arXiv:2304.10592.
16. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). Association for Computing Machinery.
17. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., ... & Liang, P. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
18. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 3645–3650). Association for Computational Linguistics.
19. Schwartz, R., Dodge, J., Smith, N. A., & Etzioni, O. (2020). Green AI. Communications of the ACM, 63(12), 54–63.
20. Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., ... & Gebru, T. (2019). Model cards for model reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 220–229). Association for Computing Machinery.
21. Weidinger, L., Uesato, J., Rauh, M., Griffin, C., Huang, P.-S., Mellor, J., ... & Gabriel, I. (2021). Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359.
22. Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., ... & Dennison, D. (2015). Hidden technical debt in machine learning systems. Advances in Neural Information Processing Systems, 28, 2503–2511.
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