IntelliProof: An Argumentation Network-based Conversational Helper for Organized Reflection

• URL: http://arxiv.org/abs/2511.04528v1
• Date: Thu, 06 Nov 2025 16:43:37 GMT
• Title: IntelliProof: An Argumentation Network-based Conversational Helper for Organized Reflection
• Authors: Kaveh Eskandari Miandoab, Katharine Kowalyshyn, Kabir Pamnani, Anesu Gavhera, Vasanth Sarathy, Matthias Scheutz,
• Abstract summary: We present IntelliProof, an interactive system for analyzing argumentative essays through LLMs.<n>Unlike existing automated essay scoring systems, IntelliProof emphasizes the user experience.<n>IntelliProof provides a set of tools for a better understanding of an argumentative essay and its corresponding graph in natural language.
• Score: 2.7353636376883563
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present IntelliProof, an interactive system for analyzing argumentative essays through LLMs. IntelliProof structures an essay as an argumentation graph, where claims are represented as nodes, supporting evidence is attached as node properties, and edges encode supporting or attacking relations. Unlike existing automated essay scoring systems, IntelliProof emphasizes the user experience: each relation is initially classified and scored by an LLM, then visualized for enhanced understanding. The system provides justifications for classifications and produces quantitative measures for essay coherence. It enables rapid exploration of argumentative quality while retaining human oversight. In addition, IntelliProof provides a set of tools for a better understanding of an argumentative essay and its corresponding graph in natural language, bridging the gap between the structural semantics of argumentative essays and the user's understanding of a given text. A live demo and the system are available here to try: \textbf{https://intelliproof.vercel.app}

Related papers

• Explain Before You Answer: A Survey on Compositional Visual Reasoning [74.27548620675748]
  Compositional visual reasoning has emerged as a key research frontier in multimodal AI.<n>This survey systematically reviews 260+ papers from top venues (CVPR, ICCV, NeurIPS, ICML, ACL, etc.)<n>We then catalog 60+ benchmarks and corresponding metrics that probe compositional visual reasoning along dimensions such as grounding accuracy, chain-of-thought faithfulness, and high-resolution perception.
  arXiv  Detail & Related papers  (2025-08-24T11:01:51Z)
• Beyond Chunking: Discourse-Aware Hierarchical Retrieval for Long Document Question Answering [51.7493726399073]
  We present a discourse-aware hierarchical framework to enhance long document question answering.<n>The framework involves three key innovations: specialized discourse parsing for lengthy documents, LLM-based enhancement of discourse relation nodes, and structure-guided hierarchical retrieval.
  arXiv  Detail & Related papers  (2025-05-26T14:45:12Z)
• Leveraging Small LLMs for Argument Mining in Education: Argument Component Identification, Classification, and Assessment [7.673465837624366]
  This paper proposes leveraging open-source, small Large Language Models (LLMs) for argument mining through few-shot prompting and fine-tuning.<n>We perform three tasks: segmentation of student essays into arguments, classification of the arguments by type, and assessment of their quality.<n>We empirically evaluate the models on the Feedback Prize - Predicting Effective Arguments dataset of grade 6-12 students essays.
  arXiv  Detail & Related papers  (2025-02-20T09:23:40Z)
• Prove Your Point!: Bringing Proof-Enhancement Principles to Argumentative Essay Generation [27.117415957353245]
  argumentative essay generation (AEG) aims to generate complete texts on specific controversial topics or debates. We present a unified two-stage framework: Proof-Enhancement and Self-Enhancement. PESA generates argumentative essays with better logical validity and persuasiveness than strong baseline models.
  arXiv  Detail & Related papers  (2024-10-30T02:13:39Z)
• A School Student Essay Corpus for Analyzing Interactions of Argumentative Structure and Quality [12.187586364960758]
  We present a German corpus of 1,320 essays from school students of two age groups. Each essay has been manually annotated for argumentative structure and quality on multiple levels of granularity. We propose baseline approaches to argument mining and essay scoring, and we analyze interactions between both tasks.
  arXiv  Detail & Related papers  (2024-04-03T07:31:53Z)
• Mind the Gap: Automated Corpus Creation for Enthymeme Detection and Reconstruction in Learner Arguments [15.184644294253848]
  This paper introduces two new tasks for learner arguments: to identify gaps in arguments and to fill such gaps. Based on the ICLEv3 corpus of argumentative learner essays, we create 40,089 argument instances for enthymeme detection and reconstruction.
  arXiv  Detail & Related papers  (2023-10-27T12:33:40Z)
• A Unifying Framework for Learning Argumentation Semantics [47.84663434179473]
  We present a novel framework, which uses an Inductive Logic Programming approach to learn the acceptability semantics for several abstract and structured argumentation frameworks in an interpretable way.<n>Our framework outperforms existing argumentation solvers, thus opening up new future research directions in the area of formal argumentation and human-machine dialogues.
  arXiv  Detail & Related papers  (2023-10-18T20:18:05Z)
• Prompt- and Trait Relation-aware Cross-prompt Essay Trait Scoring [3.6825890616838066]
  Automated essay scoring (AES) aims to score essays written for a given prompt, which defines the writing topic. Most existing AES systems assume to grade essays of the same prompt as used in training and assign only a holistic score. We propose a robust model: prompt- and trait relation-aware cross-prompt essay trait scorer.
  arXiv  Detail & Related papers  (2023-05-26T11:11:19Z)
• Natural Language Decompositions of Implicit Content Enable Better Text Representations [52.992875653864076]
  We introduce a method for the analysis of text that takes implicitly communicated content explicitly into account.<n>We use a large language model to produce sets of propositions that are inferentially related to the text that has been observed.<n>Our results suggest that modeling the meanings behind observed language, rather than the literal text alone, is a valuable direction for NLP.
  arXiv  Detail & Related papers  (2023-05-23T23:45:20Z)
• Persua: A Visual Interactive System to Enhance the Persuasiveness of Arguments in Online Discussion [52.49981085431061]
  Enhancing people's ability to write persuasive arguments could contribute to the effectiveness and civility in online communication. We derived four design goals for a tool that helps users improve the persuasiveness of arguments in online discussions. Persua is an interactive visual system that provides example-based guidance on persuasive strategies to enhance the persuasiveness of arguments.
  arXiv  Detail & Related papers  (2022-04-16T08:07:53Z)
• Discourse Analysis for Evaluating Coherence in Video Paragraph Captions [99.37090317971312]
  We are exploring a novel discourse based framework to evaluate the coherence of video paragraphs. Central to our approach is the discourse representation of videos, which helps in modeling coherence of paragraphs conditioned on coherence of videos. Our experiment results have shown that the proposed framework evaluates coherence of video paragraphs significantly better than all the baseline methods.
  arXiv  Detail & Related papers  (2022-01-17T04:23:08Z)

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.