Implicit Multimodal Alignment: On the Generalization of Frozen LLMs to Multimodal Inputs

• URL: http://arxiv.org/abs/2405.16700v2
• Date: Sat, 05 Oct 2024 08:44:03 GMT
• Title: Implicit Multimodal Alignment: On the Generalization of Frozen LLMs to Multimodal Inputs
• Authors: Mustafa Shukor, Matthieu Cord,
• Abstract summary: Large Language Models (LLMs) have demonstrated impressive performance on multimodal tasks, without any multimodal finetuning. In this work, we expose frozen LLMs to image, video, audio and text inputs and analyse their internal representation.
• Score: 63.29737699997859
• License:
• Abstract: Large Language Models (LLMs) have demonstrated impressive performance on multimodal tasks, without any multimodal finetuning. They are the building block for Large Multimodal Models, yet, we still lack a proper understanding of their success. In this work, we expose frozen LLMs to image, video, audio and text inputs and analyse their internal representation aiming to understand their generalization beyond textual inputs. Findings. Perceptual tokens (1) are easily distinguishable from textual ones inside LLMs, with significantly different representations, and complete translation to textual tokens does not exist. Yet, (2) both perceptual and textual tokens activate similar LLM weights. Despite being different, (3) perceptual and textual tokens are implicitly aligned inside LLMs, we call this the implicit multimodal alignment (IMA), and argue that this is linked to architectural design, helping LLMs to generalize. This provide more evidence to believe that the generalization of LLMs to multimodal inputs is mainly due to their architecture. Implications. (1) We find a positive correlation between the implicit alignment score and the task performance, suggesting that this could act as a proxy metric for model evaluation and selection. (2) A negative correlation exists regarding hallucinations, revealing that this problem is mainly due to misalignment between the internal perceptual and textual representations. (3) Perceptual tokens change slightly throughout the model, thus, we propose different approaches to skip computations (e.g. in FFN layers), and significantly reduce the inference cost. (4) Due to the slowly changing embeddings across layers, and the high overlap between textual and multimodal activated weights, we compress LLMs by keeping only 1 subnetwork that works well across a wide range of multimodal tasks. Paper code: https://github.com/mshukor/ima-lmms.

Related papers

• Push the Limit of Multi-modal Emotion Recognition by Prompting LLMs with Receptive-Field-Aware Attention Weighting [11.926100290196828]
  We present a framework, Lantern, that can improve the performance of a certain vanilla model by prompting large language models with receptive-field-aware attention weighting. In the experiments, vanilla models CORECT and SDT are deployed in Lantern with GPT-4 or Llama-3.1-405B.
  arXiv  Detail & Related papers  (2024-11-26T18:35:24Z)
• Balancing Performance and Efficiency: A Multimodal Large Language Model Pruning Method based Image Text Interaction [6.467840081978855]
  multimodal large language models (MM-LLMs) have achieved great success in many multimodal tasks, but their high computational costs limit their further promotion and application. We studied the visual tokens of MM-LLMs and designed a dynamic pruning algorithm to address this issue. Our proposed method can achieve performance that competes with the original performance when using an average of 22% of the original token quantity.
  arXiv  Detail & Related papers  (2024-09-02T10:49:10Z)
• AIM: Let Any Multi-modal Large Language Models Embrace Efficient In-Context Learning [15.770849688170477]
  In-context learning (ICL) facilitates Large Language Models exhibiting emergent ability on downstream tasks without updating billions of parameters. Most primary MLLMs are only trained on single-image datasets, making them unable to read multi-modal demonstrations. We propose a general and light-weighted framework textbfAIM to tackle the mentioned problems through textbfAggregating textbfImage information of textbfMultimodal demonstrations to the dense latent space of the corresponding linguistic part.
  arXiv  Detail & Related papers  (2024-06-11T08:12:43Z)
• Boosting Multimodal Large Language Models with Visual Tokens Withdrawal for Rapid Inference [59.91176945361035]
  We introduce Visual Tokens Withdrawal (VTW), a plug-and-play module to boost MLLMs for rapid inference. Our approach is inspired by two intriguing phenomena we have observed. Our VTW approach can cut computational overhead by over 40% across diverse multimodal tasks while maintaining performance.
  arXiv  Detail & Related papers  (2024-05-09T14:38:53Z)
• Browse and Concentrate: Comprehending Multimodal Content via prior-LLM Context Fusion [70.9767518332692]
  Multimodal Large Language Models (MLLMs) that incorporate LLMs with pre-trained vision models have recently demonstrated impressive performance across diverse vision-language tasks. However, they fall short to comprehend context involving multiple images. We propose a two phase paradigm, browse-and-concentrate, to enable in-depth multimodal context fusion.
  arXiv  Detail & Related papers  (2024-02-19T14:59:07Z)
• Prompt Highlighter: Interactive Control for Multi-Modal LLMs [50.830448437285355]
  This study targets a critical aspect of multi-modal LLMs' (LLMs&VLMs) inference: explicit controllable text generation. We introduce a novel inference method, Prompt Highlighter, which enables users to highlight specific prompt spans to interactively control the focus during generation. We find that, during inference, guiding the models with highlighted tokens through the attention weights leads to more desired outputs.
  arXiv  Detail & Related papers  (2023-12-07T13:53:29Z)
• OneLLM: One Framework to Align All Modalities with Language [90.14915575477197]
  We present OneLLM, an MLLM that aligns eight modalities to language using a unified framework. OneLLM is evaluated on 25 diverse benchmarks, encompassing tasks such as multimodal captioning, question answering and reasoning.
  arXiv  Detail & Related papers  (2023-12-06T18:59:19Z)
• TEAL: Tokenize and Embed ALL for Multi-modal Large Language Models [69.49978333446538]
  TEAL is an approach to treat the input from any modality as a token sequence. It embeds the token sequence into a joint embedding space with a learnable embedding matrix. Experiments show that TEAL achieves substantial improvements in multi-modal understanding.
  arXiv  Detail & Related papers  (2023-11-08T10:34:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.