Explain with Visual Keypoints Like a Real Mentor! A Benchmark for Multimodal Solution Explanation

• URL: http://arxiv.org/abs/2504.03197v2
• Date: Mon, 07 Apr 2025 14:23:25 GMT
• Title: Explain with Visual Keypoints Like a Real Mentor! A Benchmark for Multimodal Solution Explanation
• Authors: Jaewoo Park, Jungyang Park, Dongju Jang, Jiwan Chung, Byungwoo Yoo, Jaewoo Shin, Seonjoon Park, Taehyeong Kim, Youngjae Yu,
• Abstract summary: We introduce a novel task of visual solution explanation, which requires generating explanations that incorporate newly introduced visual elements essential for understanding.<n>We propose MathExplain, a benchmark consisting of 997 math problems annotated with visual keypoints and corresponding explanatory text that references those elements.<n>Our empirical results show that while some closed-source models demonstrate promising capabilities on visual solution-explaining, current open-source general-purpose models perform inconsistently.
• Score: 19.4261670152456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid advancement of mathematical reasoning capabilities in Large Language Models (LLMs), AI systems are increasingly being adopted in educational settings to support students' comprehension of problem-solving processes. However, a critical component remains underexplored in current LLM-generated explanations: visual explanation. In real-world instructional contexts, human tutors routinely employ visual aids - such as diagrams, markings, and highlights - to enhance conceptual clarity. To bridge this gap, we introduce a novel task of visual solution explanation, which requires generating explanations that incorporate newly introduced visual elements essential for understanding (e.g., auxiliary lines, annotations, or geometric constructions). To evaluate model performance on this task, we propose MathExplain, a multimodal benchmark consisting of 997 math problems annotated with visual keypoints and corresponding explanatory text that references those elements. Our empirical results show that while some closed-source models demonstrate promising capabilities on visual solution-explaining, current open-source general-purpose models perform inconsistently, particularly in identifying relevant visual components and producing coherent keypoint-based explanations. We expect that visual solution-explaining and the MathExplain dataset will catalyze further research on multimodal LLMs in education and advance their deployment as effective, explanation-oriented AI tutors. Code and data will be released publicly.

Related papers

• Reasoning with Graphs: Structuring Implicit Knowledge to Enhance LLMs Reasoning [73.2950349728376]
  Large language models (LLMs) have demonstrated remarkable success across a wide range of tasks.<n>However, they still encounter challenges in reasoning tasks that require understanding and inferring relationships between pieces of information.<n>This challenge is particularly pronounced in tasks involving multi-step processes, such as logical reasoning and multi-hop question answering.<n>We propose Reasoning with Graphs (RwG) by first constructing explicit graphs from the context.
  arXiv  Detail & Related papers  (2025-01-14T05:18:20Z)
• DRIVINGVQA: Analyzing Visual Chain-of-Thought Reasoning of Vision Language Models in Real-World Scenarios with Driving Theory Tests [69.00444996464662]
  We present DrivingVQA, a new benchmark derived from driving theory tests to evaluate visual chain-of-thought reasoning in complex real-world scenarios.<n>Our experiments reveal that open-source and proprietary LVLMs struggle with visual chain-of-thought reasoning under zero-shot settings.<n>We investigate training strategies that leverage relevant entities to improve visual reasoning.
  arXiv  Detail & Related papers  (2025-01-08T18:31:16Z)
• Inst-IT: Boosting Multimodal Instance Understanding via Explicit Visual Prompt Instruction Tuning [125.79428219851289]
  Inst-IT is a solution to enhance LMMs in Instance understanding via explicit visual prompt Instruction Tuning.<n>Inst-IT consists of a benchmark to diagnose multimodal instance-level understanding, a large-scale instruction-tuning dataset, and a continuous instruction-tuning training paradigm.
  arXiv  Detail & Related papers  (2024-12-04T18:58:10Z)
• MEGL: Multimodal Explanation-Guided Learning [23.54169888224728]
  We propose a novel Multimodal Explanation-Guided Learning (MEGL) framework to enhance model interpretability and improve classification performance. Our Saliency-Driven Textual Grounding (SDTG) approach integrates spatial information from visual explanations into textual rationales, providing spatially grounded and contextually rich explanations. We validate MEGL on two new datasets, Object-ME and Action-ME, for image classification with multimodal explanations.
  arXiv  Detail & Related papers  (2024-11-20T05:57:00Z)
• Do Vision-Language Models Really Understand Visual Language? [43.893398898373995]
  Diagrams are a typical example of a visual language depicting complex concepts and their relationships in the form of an image. Recent studies suggest that Large Vision-Language Models (LVLMs) can even tackle complex reasoning tasks involving diagrams. This paper develops a comprehensive test suite to evaluate the diagram comprehension capability of LVLMs.
  arXiv  Detail & Related papers  (2024-09-30T19:45:11Z)
• InsightSee: Advancing Multi-agent Vision-Language Models for Enhanced Visual Understanding [12.082379948480257]
  This paper proposes InsightSee, a multi-agent framework to enhance vision-language models' capabilities in handling complex visual understanding scenarios. The framework comprises a description agent, two reasoning agents, and a decision agent, which are integrated to refine the process of visual information interpretation. The proposed framework outperforms state-of-the-art algorithms in 6 out of 9 benchmark tests, with a substantial advancement in multimodal understanding.
  arXiv  Detail & Related papers  (2024-05-31T13:56:55Z)
• Image-of-Thought Prompting for Visual Reasoning Refinement in Multimodal Large Language Models [14.765057045747753]
  Chain-of-Thought (CoT) and related rationale-based works have significantly improved the performance of Large Language Models (LLMs) in complex reasoning tasks. We propose the Image-of-Thought (IoT) prompting method, which helps MLLMs to extract visual rationales step-by-step. IoT prompting has improved zero-shot visual reasoning performance across various visual understanding tasks in different MLLMs.
  arXiv  Detail & Related papers  (2024-05-22T17:56:51Z)
• Cantor: Inspiring Multimodal Chain-of-Thought of MLLM [83.6663322930814]
  We argue that converging visual context acquisition and logical reasoning is pivotal for tackling visual reasoning tasks. We propose an innovative multimodal CoT framework, termed Cantor, characterized by a perception-decision architecture. Our experiments demonstrate the efficacy of the proposed framework, showing significant improvements in multimodal CoT performance.
  arXiv  Detail & Related papers  (2024-04-24T17:59:48Z)
• Describe-then-Reason: Improving Multimodal Mathematical Reasoning through Visual Comprehension Training [24.989732666940153]
  Open-source multimodal large language models (MLLMs) excel in various tasks involving textual and visual inputs. MLLMs still struggle with complex multimodal mathematical reasoning, lagging behind proprietary models like GPT-4V(ision) and Gemini-Pro. We propose a two-step training pipeline VCAR, which emphasizes the Visual Reasoning training in addition to mathematical learning.
  arXiv  Detail & Related papers  (2024-04-22T21:59:35Z)
• Incorporating Visual Experts to Resolve the Information Loss in Multimodal Large Language Models [121.83413400686139]
  This paper proposes to improve the visual perception ability of MLLMs through a mixture-of-experts knowledge enhancement mechanism. We introduce a novel method that incorporates multi-task encoders and visual tools into the existing MLLMs training and inference pipeline.
  arXiv  Detail & Related papers  (2024-01-06T02:02:34Z)
• Causal Reasoning Meets Visual Representation Learning: A Prospective Study [117.08431221482638]
  Lack of interpretability, robustness, and out-of-distribution generalization are becoming the challenges of the existing visual models. Inspired by the strong inference ability of human-level agents, recent years have witnessed great effort in developing causal reasoning paradigms. This paper aims to provide a comprehensive overview of this emerging field, attract attention, encourage discussions, bring to the forefront the urgency of developing novel causal reasoning methods.
  arXiv  Detail & Related papers  (2022-04-26T02:22:28Z)
• Object Relational Graph with Teacher-Recommended Learning for Video Captioning [92.48299156867664]
  We propose a complete video captioning system including both a novel model and an effective training strategy. Specifically, we propose an object relational graph (ORG) based encoder, which captures more detailed interaction features to enrich visual representation. Meanwhile, we design a teacher-recommended learning (TRL) method to make full use of the successful external language model (ELM) to integrate the abundant linguistic knowledge into the caption model.
  arXiv  Detail & Related papers  (2020-02-26T15:34:52Z)

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.