Using Game Play to Investigate Multimodal and Conversational Grounding in Large Multimodal Models

• URL: http://arxiv.org/abs/2406.14035v2
• Date: Mon, 23 Sep 2024 11:07:18 GMT
• Title: Using Game Play to Investigate Multimodal and Conversational Grounding in Large Multimodal Models
• Authors: Sherzod Hakimov, Yerkezhan Abdullayeva, Kushal Koshti, Antonia Schmidt, Yan Weiser, Anne Beyer, David Schlangen,
• Abstract summary: In this paper, we bring a recently developed evaluation paradigm from text models to multimodal models. We define games that challenge a model's capability to represent a situation from visual information and align such representations through dialogue. We find that the largest closed models perform rather well on the games that we define, while even the best open-weight models struggle with them.
• Score: 14.878276985702685
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While the situation has improved for text-only models, it again seems to be the case currently that multimodal (text and image) models develop faster than ways to evaluate them. In this paper, we bring a recently developed evaluation paradigm from text models to multimodal models, namely evaluation through the goal-oriented game (self) play, complementing reference-based and preference-based evaluation. Specifically, we define games that challenge a model's capability to represent a situation from visual information and align such representations through dialogue. We find that the largest closed models perform rather well on the games that we define, while even the best open-weight models struggle with them. On further analysis, we find that the exceptional deep captioning capabilities of the largest models drive some of the performance. There is still room to grow for both kinds of models, ensuring the continued relevance of the benchmark.

Related papers

• Towards Multi-Modal Mastery: A 4.5B Parameter Truly Multi-Modal Small Language Model [0.0]
  We present a novel 4.5B parameter small language model that can handle multiple input and output modalities. Despite its small size, the model achieves near state-of-the-art performance on a variety of tasks.
  arXiv  Detail & Related papers  (2024-11-08T17:15:17Z)
• LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Models [12.500091504010067]
  We propose WALM (Words Agreement with Language Model), a new evaluation method for topic modeling. With extensive experiments involving different types of topic models, WALM is shown to align with human judgment.
  arXiv  Detail & Related papers  (2024-06-13T11:19:50Z)
• What matters when building vision-language models? [52.8539131958858]
  We develop Idefics2, an efficient foundational vision-language model with 8 billion parameters. Idefics2 achieves state-of-the-art performance within its size category across various multimodal benchmarks. We release the model (base, instructed, and chat) along with the datasets created for its training.
  arXiv  Detail & Related papers  (2024-05-03T17:00:00Z)
• Multi-modal Auto-regressive Modeling via Visual Words [96.25078866446053]
  We propose the concept of visual tokens, which maps the visual features to probability distributions over Large Multi-modal Models' vocabulary. We further explore the distribution of visual features in the semantic space within LMM and the possibility of using text embeddings to represent visual information.
  arXiv  Detail & Related papers  (2024-03-12T14:58:52Z)
• Veagle: Advancements in Multimodal Representation Learning [0.0]
  This paper introduces a novel approach to enhance the multimodal capabilities of existing models. Our proposed model Veagle, incorporates a unique mechanism inspired by the successes and insights of previous works. Our results indicate a improvement of 5-6 % in performance, with Veagle outperforming existing models by a notable margin.
  arXiv  Detail & Related papers  (2024-01-18T12:45:25Z)
• EvalCrafter: Benchmarking and Evaluating Large Video Generation Models [70.19437817951673]
  We argue that it is hard to judge the large conditional generative models from the simple metrics since these models are often trained on very large datasets with multi-aspect abilities. Our approach involves generating a diverse and comprehensive list of 700 prompts for text-to-video generation. Then, we evaluate the state-of-the-art video generative models on our carefully designed benchmark, in terms of visual qualities, content qualities, motion qualities, and text-video alignment with 17 well-selected objective metrics.
  arXiv  Detail & Related papers  (2023-10-17T17:50:46Z)
• Dissecting Multimodality in VideoQA Transformer Models by Impairing Modality Fusion [54.33764537135906]
  VideoQA Transformer models demonstrate competitive performance on standard benchmarks. Do these models capture the rich multimodal structures and dynamics from video and text jointly? Are they achieving high scores by exploiting biases and spurious features?
  arXiv  Detail & Related papers  (2023-06-15T06:45:46Z)
• Scaling Vision-Language Models with Sparse Mixture of Experts [128.0882767889029]
  We show that mixture-of-experts (MoE) techniques can achieve state-of-the-art performance on a range of benchmarks over dense models of equivalent computational cost. Our research offers valuable insights into stabilizing the training of MoE models, understanding the impact of MoE on model interpretability, and balancing the trade-offs between compute performance when scaling vision-language models.
  arXiv  Detail & Related papers  (2023-03-13T16:00:31Z)
• Internet-augmented language models through few-shot prompting for open-domain question answering [6.573232954655063]
  We capitalize on the unique few-shot capabilities offered by large-scale language models to overcome some of their challenges. We use few-shot prompting to learn to condition language models on information returned from the web using Google Search. We find that language models conditioned on the web surpass performance of closed-book models of similar, or even larger, model sizes in open-domain question answering.
  arXiv  Detail & Related papers  (2022-03-10T02:24:14Z)
• Multi-Modal Open-Domain Dialogue [28.69395893943413]
  Recent work in open-domain conversational agents has demonstrated that significant improvements in model engagingness and humanness metrics can be achieved via massive scaling. We investigate combining components from state-of-the-art open-domain dialogue agents with those from state-of-the-art vision models. We show that our best resulting model outperforms strong existing models in multi-modal dialogue while simultaneously performing as well as its predecessor.
  arXiv  Detail & Related papers  (2020-10-02T16:20:39Z)
• Modality-Balanced Models for Visual Dialogue [102.35406085738325]
  The Visual Dialog task requires a model to exploit both image and conversational context information to generate the next response to the dialogue. We show that previous joint-modality (history and image) models over-rely on and are more prone to memorizing the dialogue history. We present methods for this integration of the two models, via ensemble and consensus dropout fusion with shared parameters.
  arXiv  Detail & Related papers  (2020-01-17T14:57:12Z)

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.