WebQuest: A Benchmark for Multimodal QA on Web Page Sequences

• URL: http://arxiv.org/abs/2409.13711v2
• Date: Tue, 24 Sep 2024 18:38:02 GMT
• Title: WebQuest: A Benchmark for Multimodal QA on Web Page Sequences
• Authors: Maria Wang, Srinivas Sunkara, Gilles Baechler, Jason Lin, Yun Zhu, Fedir Zubach, Lei Shu, Jindong Chen,
• Abstract summary: WebQuest is a multi-page question-answering dataset that requires reasoning across multiple web pages. Our dataset evaluates information extraction, multimodal retrieval and composition of information from many web pages. We evaluate leading proprietary multimodal models like GPT-4V, Gemini Flash, Claude 3, and open source models like InstructBLIP, PaliGemma on our dataset.
• Score: 10.008284460456107
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The rise of powerful multimodal LLMs has enhanced the viability of building web agents which can, with increasing levels of autonomy, assist users to retrieve information and complete tasks on various human-computer interfaces. It is hence necessary to build challenging benchmarks that span a wide-variety of use cases reflecting real-world usage. In this work, we present WebQuest, a multi-page question-answering dataset that requires reasoning across multiple related web pages. In contrast to existing UI benchmarks that focus on multi-step web navigation and task completion, our dataset evaluates information extraction, multimodal retrieval and composition of information from many web pages. WebQuest includes three question categories: single-screen QA, multi-screen QA, and QA based on navigation traces. We evaluate leading proprietary multimodal models like GPT-4V, Gemini Flash, Claude 3, and open source models like InstructBLIP, PaliGemma on our dataset, revealing a significant gap between single-screen and multi-screen reasoning. Finally, we investigate inference time techniques like Chain-of-Thought prompting to improve model capabilities on multi-screen reasoning.

Related papers

• Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent [102.31558123570437]
  Multimodal Retrieval Augmented Generation (mRAG) plays an important role in mitigating the "hallucination" issue inherent in multimodal large language models (MLLMs) We propose the first self-adaptive planning agent for multimodal retrieval, OmniSearch.
  arXiv  Detail & Related papers  (2024-11-05T09:27:21Z)
• Multi-modal Situated Reasoning in 3D Scenes [32.800524889357305]
  We propose Multi-modal Situated Question Answering (MSQA), a large-scale multi-modal situated reasoning dataset. MSQA includes 251K situated question-answering pairs across 9 distinct question categories, covering complex scenarios within 3D scenes. We also devise the Multi-modal Situated Next-step Navigation (MSNN) benchmark to evaluate models' situated reasoning for navigation.
  arXiv  Detail & Related papers  (2024-09-04T02:37:38Z)
• An Interactive Multi-modal Query Answering System with Retrieval-Augmented Large Language Models [21.892975397847316]
  We present an interactive Multi-modal Query Answering (MQA) system, empowered by our newly developed multi-modal retrieval framework and navigation graph index. One notable aspect of MQA is its utilization of contrastive learning to assess the significance of different modalities. The system achieves efficient retrieval through our advanced navigation graph index, refined using computational pruning techniques.
  arXiv  Detail & Related papers  (2024-07-05T02:01:49Z)
• HEMM: Holistic Evaluation of Multimodal Foundation Models [91.60364024897653]
  Multimodal foundation models can holistically process text alongside images, video, audio, and other sensory modalities. It is challenging to characterize and study progress in multimodal foundation models, given the range of possible modeling decisions, tasks, and domains.
  arXiv  Detail & Related papers  (2024-07-03T18:00:48Z)
• VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks [93.85005277463802]
  VisualWebArena is a benchmark designed to assess the performance of multimodal web agents on realistic tasks. To perform on this benchmark, agents need to accurately process image-text inputs, interpret natural language instructions, and execute actions on websites to accomplish user-defined objectives.
  arXiv  Detail & Related papers  (2024-01-24T18:35:21Z)
• Generative Multi-Modal Knowledge Retrieval with Large Language Models [75.70313858231833]
  We propose an innovative end-to-end generative framework for multi-modal knowledge retrieval. Our framework takes advantage of the fact that large language models (LLMs) can effectively serve as virtual knowledge bases. We demonstrate significant improvements ranging from 3.0% to 14.6% across all evaluation metrics when compared to strong baselines.
  arXiv  Detail & Related papers  (2024-01-16T08:44:29Z)
• M3SOT: Multi-frame, Multi-field, Multi-space 3D Single Object Tracking [41.716532647616134]
  3D Single Object Tracking (SOT) stands a forefront task of computer vision, proving essential for applications like autonomous driving. In this research, we unveil M3SOT, a novel 3D SOT framework, which synergizes multiple input frames (template sets), multiple receptive fields (continuous contexts), and multiple solution spaces (distinct tasks) in ONE model.
  arXiv  Detail & Related papers  (2023-12-11T04:49:47Z)
• Read, Look or Listen? What's Needed for Solving a Multimodal Dataset [7.0430001782867]
  We propose a two-step method to analyze multimodal datasets, which leverages a small seed of human annotation to map each multimodal instance to the modalities required to process it. We apply our approach to TVQA, a video question-answering dataset, and discover that most questions can be answered using a single modality, without a substantial bias towards any specific modality. We analyze the MERLOT Reserve, finding that it struggles with image-based questions compared to text and audio, but also with auditory speaker identification.
  arXiv  Detail & Related papers  (2023-07-06T08:02:45Z)
• Multimodal Web Navigation with Instruction-Finetuned Foundation Models [99.14209521903854]
  We study data-driven offline training for web agents with vision-language foundation models. We propose an instruction-following multimodal agent, WebGUM, that observes both webpage screenshots and HTML pages. We empirically demonstrate this recipe improves the agent's ability of grounded multimodal perception, HTML comprehension, and multi-step reasoning.
  arXiv  Detail & Related papers  (2023-05-19T17:44:34Z)
• DeepQAMVS: Query-Aware Hierarchical Pointer Networks for Multi-Video Summarization [127.16984421969529]
  We introduce a novel Query-Aware Hierarchical Pointer Network for Multi-Video Summarization, termed DeepQAMVS. DeepQAMVS is trained with reinforcement learning, incorporating rewards that capture representativeness, diversity, query-adaptability and temporal coherence. We achieve state-of-the-art results on the MVS1K dataset, with inference time scaling linearly with the number of input video frames.
  arXiv  Detail & Related papers  (2021-05-13T17:33:26Z)

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.