Summary of The Labyrinth Of Links: Navigating the Associative Maze Of Multi-modal Llms, by Hong Li et al.
The Labyrinth of Links: Navigating the Associative Maze of Multi-modal LLMs
by Hong Li, Nanxi Li, Yuanjie Chen, Jianbin Zhu, Qinlu Guo, Cewu Lu, Yong-Lu Li
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a new benchmark for evaluating the capabilities of Multi-modal Large Language Models (MLLMs) in association, a fundamental human intelligence. The authors formulate the association task and develop a standard benchmark based on adjective and verb semantic concepts. Unlike existing methods that rely on costly data annotation and curation, they propose an annotation-free construction method transforming general datasets for the association tasks. The paper also introduces a rigorous data refinement process to eliminate confusion in the raw dataset. Three levels of association tasks are established: single-step, synchronous, and asynchronous associations. The authors conduct a comprehensive investigation into the MLLMs’ zero-shot association capabilities, including three distinct memory strategies, open-source and closed-source MLLMs, Mixture-of-Experts (MoE) models, and human expert involvement. The results show that current open-source MLLMs consistently exhibit poor capability in the association tasks, even state-of-the-art GPT-4V has a significant gap compared to humans. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at understanding connections between things. Humans are good at linking ideas and memories together, but computers struggle with this task. The authors propose a new way to test computer language models on their ability to make these connections. They create a special dataset that doesn’t need extra work, unlike other methods that require lots of human effort. The results show that current computer language models are not very good at making these connections, even the best ones we have today. This paper aims to help improve computer language models by creating a new benchmark for them to follow. |
Keywords
» Artificial intelligence » Gpt » Mixture of experts » Multi modal » Zero shot
