Multimodal World Knowledge in Videos Enables Long-Chain Reasoning for Complex Question Answering

核心概念

WorldQA, a video understanding dataset, challenges models to leverage multimodal information and broad world knowledge to answer complex questions through long reasoning chains.

摘要

The WorldQA dataset is designed to push the boundaries of multimodal world models by incorporating three key features:

Multimodal Video Input: The dataset comprises 1007 question-answer pairs and 303 videos, requiring the analysis of both auditory and visual data for successful interpretation.

World Knowledge: The dataset identifies five essential types of world knowledge for question formulation - societal norms, multimodal associations, self-motivation, tool use, and social interactions. This approach challenges models to extend their capabilities beyond mere perception.

Long-Chain Reasoning: The dataset introduces an average reasoning step of 4.45, notably surpassing other videoQA datasets.

The authors propose exploring WorldQA with WorldRetriver, an agent that breaks down each question into perception- or cognition-oriented tasks. These tasks are then addressed by specialized models - a multimodal key information retriever and a world knowledge retriever. The language model integrates their outputs to form a cohesive reasoning chain and answer the question.
Extensive evaluations of 13 prominent large language models (LLMs) and large multimodal models (LMMs) reveal that WorldRetriver, although being the most effective model, achieved only 70% of human-level performance in multiple-choice questions. This finding highlights the necessity for further advancement in the reasoning and comprehension abilities of models.
The experiments also yield several key insights:

While humans tend to perform better with increased frames, current LMMs, including WorldRetriver, show diminished performance under similar conditions.
Open-source LMMs exhibit challenges with "consistency" in multiple-choice QA.
Employing GPT-4 to evaluate open-ended QA models correlates well with human judgments.

統計資料

The average reasoning steps in WorldQA is 4.45, notably higher than the under-two average in other datasets.
The dataset comprises 1007 question-answer pairs and 303 videos.
The average length of questions and answers in WorldQA is 14.2 and 24.3 words, respectively, compared to under 5 words in other VideoQA datasets.

引述

"Multimodal information, together with our knowledge, help us to understand the complex and dynamic world."
"Large language models (LLM) and large multimodal models (LMM), however, still struggle to emulate this capability."

從以下內容提煉的關鍵洞見

WorldQA: Multimodal World Knowledge in Videos through Long-Chain Reasoning

by Yuanhan Zhan... 於 arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.03272.pdf

深入探究

How can the dataset be expanded to include a wider range of world knowledge types and reasoning complexities?

To expand the dataset to include a wider range of world knowledge types and reasoning complexities, several strategies can be implemented:

Diversifying World Knowledge Types: Introduce new categories of world knowledge such as cultural norms, historical references, scientific principles, and more. This will challenge models to understand a broader spectrum of information.
Increasing Reasoning Complexity: Develop questions that require more intricate reasoning chains, involving multiple logical steps and connections between different pieces of information. This will push models to engage in deeper cognitive processing.
Incorporating Varied Contexts: Include videos from different settings and scenarios to expose models to a diverse range of contexts, requiring them to adapt their reasoning processes accordingly.
Collaborating with Domain Experts: Work with experts in various fields to curate questions that delve into specialized knowledge areas, adding depth and complexity to the dataset.

How can architectural changes or training techniques help current LMMs better leverage multiple video frames for improved performance?

Architectural changes and training techniques can enhance the ability of current LMMs to leverage multiple video frames effectively:

Temporal Modeling: Implement architectures that can capture temporal dependencies across frames, such as recurrent neural networks (RNNs) or transformers with temporal attention mechanisms.
Multi-Modal Fusion: Develop fusion strategies that combine information from different modalities (e.g., visual, audio) across frames to create a comprehensive representation of the video content.
Self-Supervised Learning: Utilize self-supervised learning techniques to pre-train models on a large corpus of videos, enabling them to learn temporal relationships and contextual information from multiple frames.
Fine-Tuning on Multi-Frame Data: Fine-tune models on datasets that specifically focus on multi-frame video understanding tasks, allowing them to adapt to the complexities of processing sequential visual information.

How can the dataset be used to develop models that can seamlessly integrate perception and cognition, akin to human intelligence, for comprehensive video understanding?

To develop models that seamlessly integrate perception and cognition for comprehensive video understanding using the dataset, the following approaches can be adopted:

Multi-Modal Learning: Train models to process both visual and auditory information from videos, enabling them to capture a more holistic understanding of the content.
World Knowledge Incorporation: Integrate diverse world knowledge types into the training process, encouraging models to reason based on societal norms, tool use, social interactions, and more.
Long-Chain Reasoning: Encourage models to engage in complex reasoning chains by formulating questions that require multiple logical steps to arrive at the answer, mimicking human cognitive processes.
Human-in-the-Loop Training: Incorporate human feedback during model training to refine the understanding of nuanced concepts and improve the model's ability to interpret video content accurately.