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[2602.21441] Causal Decoding for Hallucination-Resistant Multimodal Large Language Models
Summary
This article presents a novel causal decoding framework aimed at reducing object hallucination in multimodal large language models (MLLMs), enhancing their reliability in vision-language tasks.
Why It Matters
As MLLMs become increasingly prevalent in applications involving visual and textual data, addressing the issue of object hallucination is critical for ensuring the accuracy and trustworthiness of these models. This research introduces a targeted approach that could significantly improve the performance of MLLMs in real-world scenarios.
Key Takeaways
- Proposes a causal decoding framework to mitigate object hallucination in MLLMs.
- Demonstrates significant reductions in false object mentions while maintaining output quality.
- Achieves state-of-the-art performance in captioning and QA benchmarks.
- Addresses limitations of previous methods that relied on heuristic penalties and post-hoc corrections.
- Enhances the reliability of MLLMs for practical applications in vision-language tasks.
Computer Science > Machine Learning arXiv:2602.21441 (cs) [Submitted on 24 Feb 2026] Title:Causal Decoding for Hallucination-Resistant Multimodal Large Language Models Authors:Shiwei Tan, Hengyi Wang, Weiyi Qin, Qi Xu, Zhigang Hua, Hao Wang View a PDF of the paper titled Causal Decoding for Hallucination-Resistant Multimodal Large Language Models, by Shiwei Tan and 5 other authors View PDF HTML (experimental) Abstract:Multimodal Large Language Models (MLLMs) deliver detailed responses on vision-language tasks, yet remain susceptible to object hallucination (introducing objects not present in the image), undermining reliability in practice. Prior efforts often rely on heuristic penalties, post-hoc correction, or generic decoding tweaks, which do not directly intervene in the mechanisms that trigger object hallucination and thus yield limited gains. To address this challenge, we propose a causal decoding framework that applies targeted causal interventions during generation to curb spurious object mentions. By reshaping the decoding dynamics to attenuate spurious dependencies, our approach reduces false object tokens while maintaining descriptive quality. Across captioning and QA benchmarks, our framework substantially lowers object-hallucination rates and achieves state-of-the-art faithfulness without degrading overall output quality. Comments: Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv...
