mlsanityData to 14 Oct 2026

Towards Robust Argumentative Essay Understanding via TIDE: An Interactive Framework with Trial and Debate

arXiv 2605.17247 ↗First posted 17 May 2026cs.AIcited by 0

On the map: LLM reasoning → Reasoning for agents, planning and code

Abstract (arXiv)

Argumentative essays serve as a vital medium for assessing critical thinking and reasoning skills, yet there is limited works on accurately understanding and evaluating such texts via prompt. In this work, we propose TIDE, a novel framework designed to improve criteria-based prompt optimization for argument-related tasks by integrating TrIal and DEbate mechanism. Our method addresses key limitations of criteria-based prompt optimizing by mitigating the influence of noisy training data and enhancing optimization stability. We evaluate TIDE on three core tasks: Automated Essay Scoring, Argument Component Detection, and Argument Relation Identification. Results demonstrate that our framework improves performance across tasks. These findings underscore the potential of combining prompt-based methods for advanced argument understanding.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 15 of its 38 references that are in the map. First posted on arXiv 17 May 2026.

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15 of 38 references.

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  1. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate30 May

Other references · 14

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2020Longformer: The Long-Document Transformercited by 3,413
  4. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,784
  5. 2024ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Toolscited by 1,121
  6. 2018AI safety via debatecited by 400
  7. 2024Debating with More Persuasive LLMs Leads to More Truthful Answerscited by 266
  8. 2016Parsing Argumentation Structures in Persuasive Essayscited by 101
  9. 2024Can Large Language Models perform Relation-based Argument Mining?cited by 23
  10. 2024Debatrix: Multi-dimensional Debate Judge with Iterative Chronological Analysis Based on LLMcited by 21
  11. 2024Training Language Models to Win Debates with Self-Play Improves Judge Accuracycited by 16
  12. 2025Leveraging Small LLMs for Argument Mining in Education: Argument Component Identification, Classification, and Assessmentcited by 4
2 more
  1. 2024Assessing Good, Bad and Ugly Arguments Generated by ChatGPT: a New Dataset, its Methodology and Associated Taskscited by 3
  2. 2025Towards Comprehensive Argument Analysis in Education: Dataset, Tasks, and Methodcited by 1

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  2. 2023Predicting the Quality of Revisions in Argumentative Writing
  3. 2025InspireDebate: Multi-Dimensional Subjective-Objective Evaluation-Guided Reasoning and Optimization for Debating
  4. 2025Toward LLM-Supported Automated Assessment of Critical Thinking Subskills
  5. 2026The Order Matters: Sequential Fine-Tuning of LLaMA for Coherent Automated Essay Scoring
  6. 2026Prober.ai: Gated Inquiry-Based Feedback via LLM-Constrained Personas for Argumentative Writing Development
  7. 2025Can LLMs Judge Debates? Evaluating Non-Linear Reasoning via Argumentation Theory Semantics
  8. 2023"I'd Like to Have an Argument, Please": Argumentative Reasoning in Large Language Models