Query-based Cross-Modal Projector Bolstering Mamba Multimodal LLM
Mirrored from arXiv — NLP / Computation & Language for archival readability. Support the source by reading on the original site.
Computer Science > Computation and Language
Title:Query-based Cross-Modal Projector Bolstering Mamba Multimodal LLM
Abstract:The Transformer's quadratic complexity with input length imposes an unsustainable computational load on large language models (LLMs). In contrast, the Selective Scan Structured State-Space Model, or Mamba, addresses this computational challenge effectively. This paper explores a query-based cross-modal projector designed to bolster Mamba's efficiency for vision-language modeling by compressing visual tokens based on input through the cross-attention mechanism. This innovative projector also removes the need for manually designing the 2D scan order of original image features when converting them into an input sequence for Mamba LLM. Experimental results across various vision-language understanding benchmarks show that the proposed cross-modal projector enhances Mamba-based multimodal LLMs, boosting both performance and throughput.
| Comments: | Accepted to EMNLP 2024 Findings |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.04719 [cs.CL] |
| (or arXiv:2606.04719v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.04719
arXiv-issued DOI via DataCite (pending registration)
|
|
| Related DOI: | https://doi.org/10.18653/v1/2024.findings-emnlp.827
DOI(s) linking to related resources
|
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
References & Citations
Bookmark
Bibliographic and Citation Tools
Code, Data and Media Associated with this Article
Demos
Recommenders and Search Tools
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
More from arXiv — NLP / Computation & Language
-
Do Transformers Need Three Projections? Systematic Study of QKV Variants
Jun 4
-
Large Language Models Hack Rewards, and Society
Jun 4
-
Training-Free Lexical-Dense Fusion for Conversational-Memory Retrieval
Jun 4
-
Sparse Mixture-of-Experts Reward Models Learn Interpretable and Specialized Experts for Personalized Preference Modeling
Jun 4
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.