EssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models

• URL: http://arxiv.org/abs/2502.11916v1
• Date: Mon, 17 Feb 2025 15:31:59 GMT
• Title: EssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models
• Authors: Jiamin Su, Yibo Yan, Fangteng Fu, Han Zhang, Jingheng Ye, Xiang Liu, Jiahao Huo, Huiyu Zhou, Xuming Hu,
• Abstract summary: We propose EssayJudge, the first multimodal benchmark to evaluate AES capabilities across lexical-, sentence-, and discourse-level traits.<n>Our experiments with 18 representative MLLMs reveal gaps in AES performance compared to human evaluation, particularly in discourse-level traits.
• Score: 19.271790170055375
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated Essay Scoring (AES) plays a crucial role in educational assessment by providing scalable and consistent evaluations of writing tasks. However, traditional AES systems face three major challenges: (1) reliance on handcrafted features that limit generalizability, (2) difficulty in capturing fine-grained traits like coherence and argumentation, and (3) inability to handle multimodal contexts. In the era of Multimodal Large Language Models (MLLMs), we propose EssayJudge, the first multimodal benchmark to evaluate AES capabilities across lexical-, sentence-, and discourse-level traits. By leveraging MLLMs' strengths in trait-specific scoring and multimodal context understanding, EssayJudge aims to offer precise, context-rich evaluations without manual feature engineering, addressing longstanding AES limitations. Our experiments with 18 representative MLLMs reveal gaps in AES performance compared to human evaluation, particularly in discourse-level traits, highlighting the need for further advancements in MLLM-based AES research. Our dataset and code will be available upon acceptance.

Related papers

• PanguIR Technical Report for NTCIR-18 AEOLLM Task [12.061652026366591]
  Large language models (LLMs) are increasingly critical and challenging to evaluate. Manual evaluation, while comprehensive, is often costly and resource-intensive. automatic evaluation offers greater scalability but is constrained by the limitations of its evaluation criteria.
  arXiv  Detail & Related papers  (2025-03-04T07:40:02Z)
• VOILA: Evaluation of MLLMs For Perceptual Understanding and Analogical Reasoning [63.0285363282581]
  Multimodal Large Language Models (MLLMs) have become a powerful tool for integrating visual and textual information. We introduce VOILA, a benchmark designed to evaluate MLLMs' perceptual understanding and abstract relational reasoning. We reveal that current MLLMs struggle to comprehend inter-image relationships and exhibit limited capabilities in high-level relational reasoning.
  arXiv  Detail & Related papers  (2025-02-25T23:36:19Z)
• EmbodiedEval: Evaluate Multimodal LLMs as Embodied Agents [57.4686961979566]
  EmbodiedEval is a comprehensive and interactive evaluation benchmark for MLLMs with embodied tasks.<n>It covers a broad spectrum of existing embodied AI tasks with significantly enhanced diversity.<n>We evaluated the state-of-the-art MLLMs on EmbodiedEval and found that they have a significant shortfall compared to human level on embodied tasks.
  arXiv  Detail & Related papers  (2025-01-21T03:22:10Z)
• Strategic Prompting for Conversational Tasks: A Comparative Analysis of Large Language Models Across Diverse Conversational Tasks [23.34710429552906]
  We evaluate the capabilities and limitations of five prevalent Large Language Models: Llama, OPT, Falcon, Alpaca, and MPT.<n>The study encompasses various conversational tasks, including reservation, empathetic response generation, mental health and legal counseling, persuasion, and negotiation.
  arXiv  Detail & Related papers  (2024-11-26T08:21:24Z)
• Needle In A Multimodal Haystack [79.81804334634408]
  We present the first benchmark specifically designed to evaluate the capability of existing MLLMs to comprehend long multimodal documents. Our benchmark includes three types of evaluation tasks: multimodal retrieval, counting, and reasoning. We observe that existing models still have significant room for improvement on these tasks, especially on vision-centric evaluation.
  arXiv  Detail & Related papers  (2024-06-11T13:09:16Z)
• SLIDE: A Framework Integrating Small and Large Language Models for Open-Domain Dialogues Evaluation [23.203761925540736]
  We propose a novel framework SLIDE (Small and Large Integrated for Dialogue Evaluation) Our approach achieves state-of-the-art performance in both the classification and evaluation tasks, and additionally the SLIDE exhibits better correlation with human evaluators.
  arXiv  Detail & Related papers  (2024-05-24T20:32:49Z)
• FAC$^2$E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition [56.76951887823882]
  Large language models (LLMs) are primarily evaluated by overall performance on various text understanding and generation tasks. We present FAC$2$E, a framework for Fine-grAined and Cognition-grounded LLMs' Capability Evaluation.
  arXiv  Detail & Related papers  (2024-02-29T21:05:37Z)
• SEED-Bench-2: Benchmarking Multimodal Large Language Models [67.28089415198338]
  Multimodal large language models (MLLMs) have recently demonstrated exceptional capabilities in generating not only texts but also images given interleaved multimodal inputs. SEED-Bench-2 comprises 24K multiple-choice questions with accurate human annotations, which spans 27 dimensions. We evaluate the performance of 23 prominent open-source MLLMs and summarize valuable observations.
  arXiv  Detail & Related papers  (2023-11-28T05:53:55Z)
• OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models [122.27878464009181]
  We conducted a comprehensive evaluation of Large Multimodal Models, such as GPT4V and Gemini, in various text-related visual tasks. OCRBench contains 29 datasets, making it the most comprehensive OCR evaluation benchmark available.
  arXiv  Detail & Related papers  (2023-05-13T11:28:37Z)

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.