Are Large Vision Language Models up to the Challenge of Chart Comprehension and Reasoning? An Extensive Investigation into the Capabilities and Limitations of LVLMs

• URL: http://arxiv.org/abs/2406.00257v1
• Date: Sat, 1 Jun 2024 01:43:30 GMT
• Title: Are Large Vision Language Models up to the Challenge of Chart Comprehension and Reasoning? An Extensive Investigation into the Capabilities and Limitations of LVLMs
• Authors: Mohammed Saidul Islam, Raian Rahman, Ahmed Masry, Md Tahmid Rahman Laskar, Mir Tafseer Nayeem, Enamul Hoque,
• Abstract summary: Natural language is a powerful complementary modality of communication for data visualizations, such as bar and line charts. Various downstream tasks have been introduced recently such as chart question answering, chart summarization, and fact-checking with charts. These tasks pose a unique challenge, demanding both vision-language reasoning and a nuanced understanding of chart data tables, visual encodings, and natural language prompts. This paper presents the first comprehensive evaluation of the recently developed large vision language models (LVLMs) for chart understanding and reasoning tasks.
• Score: 11.19928977117624
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Natural language is a powerful complementary modality of communication for data visualizations, such as bar and line charts. To facilitate chart-based reasoning using natural language, various downstream tasks have been introduced recently such as chart question answering, chart summarization, and fact-checking with charts. These tasks pose a unique challenge, demanding both vision-language reasoning and a nuanced understanding of chart data tables, visual encodings, and natural language prompts. Despite the recent success of Large Language Models (LLMs) across diverse NLP tasks, their abilities and limitations in the realm of data visualization remain under-explored, possibly due to their lack of multi-modal capabilities. To bridge the gap, this paper presents the first comprehensive evaluation of the recently developed large vision language models (LVLMs) for chart understanding and reasoning tasks. Our evaluation includes a comprehensive assessment of LVLMs, including GPT-4V and Gemini, across four major chart reasoning tasks. Furthermore, we perform a qualitative evaluation of LVLMs' performance on a diverse range of charts, aiming to provide a thorough analysis of their strengths and weaknesses. Our findings reveal that LVLMs demonstrate impressive abilities in generating fluent texts covering high-level data insights while also encountering common problems like hallucinations, factual errors, and data bias. We highlight the key strengths and limitations of chart comprehension tasks, offering insights for future research.

Related papers

• Revisiting the Graph Reasoning Ability of Large Language Models: Case Studies in Translation, Connectivity and Shortest Path [53.71787069694794]
  We focus on the graph reasoning ability of Large Language Models (LLMs) We revisit the ability of LLMs on three fundamental graph tasks: graph description translation, graph connectivity, and the shortest-path problem. Our findings suggest that LLMs can fail to understand graph structures through text descriptions and exhibit varying performance for all these fundamental tasks.
  arXiv  Detail & Related papers  (2024-08-18T16:26:39Z)
• On Pre-training of Multimodal Language Models Customized for Chart Understanding [83.99377088129282]
  This paper explores the training processes necessary to improve MLLMs' comprehension of charts. We introduce CHOPINLLM, an MLLM tailored for in-depth chart comprehension.
  arXiv  Detail & Related papers  (2024-07-19T17:58:36Z)
• CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs [62.84082370758761]
  CharXiv is a comprehensive evaluation suite involving 2,323 charts from arXiv papers. To ensure quality, all charts and questions are handpicked, curated, and verified by human experts. Results reveal a substantial, previously underestimated gap between the reasoning skills of the strongest proprietary model.
  arXiv  Detail & Related papers  (2024-06-26T17:50:11Z)
• AltChart: Enhancing VLM-based Chart Summarization Through Multi-Pretext Tasks [31.414783623207477]
  We introduce the AltChart dataset, comprising 10,000 real chart images, each paired with a comprehensive summary. We propose a new method for pretraining Vision-Language Models (VLMs) to learn fine-grained chart representations. We conduct extensive evaluations of four leading chart summarization models, analyzing how accessible their descriptions are.
  arXiv  Detail & Related papers  (2024-05-22T12:18:52Z)
• From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models [98.41645229835493]
  Data visualization in the form of charts plays a pivotal role in data analysis, offering critical insights and aiding in informed decision-making. Large foundation models, such as large language models, have revolutionized various natural language processing tasks. This survey paper serves as a comprehensive resource for researchers and practitioners in the fields of natural language processing, computer vision, and data analysis.
  arXiv  Detail & Related papers  (2024-03-18T17:57:09Z)
• Behind the Magic, MERLIM: Multi-modal Evaluation Benchmark for Large Image-Language Models [50.653838482083614]
  This paper introduces a scalable test-bed to assess the capabilities of IT-LVLMs on fundamental computer vision tasks. MERLIM contains over 300K image-question pairs and has a strong focus on detecting cross-modal "hallucination" events in IT-LVLMs.
  arXiv  Detail & Related papers  (2023-12-03T16:39:36Z)
• Disentangled Representation Learning with Large Language Models for Text-Attributed Graphs [57.052160123387104]
  We present the Disentangled Graph-Text Learner (DGTL) model, which is able to enhance the reasoning and predicting capabilities of LLMs for TAGs. Our proposed DGTL model incorporates graph structure information through tailored disentangled graph neural network (GNN) layers. Experimental evaluations demonstrate the effectiveness of the proposed DGTL model on achieving superior or comparable performance over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-10-27T14:00:04Z)
• GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking [17.7473474499538]
  Large language models like ChatGPT have become indispensable to artificial general intelligence. In this study, we conduct an investigation to assess the proficiency of LLMs in comprehending graph data. Our findings contribute valuable insights towards bridging the gap between language models and graph understanding.
  arXiv  Detail & Related papers  (2023-05-24T11:53:19Z)

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.