mlsanityData to 14 Oct 2026

Toward Reasonable Parrots: Why Large Language Models Should Argue with Us by Design

arXiv 2505.05298 ↗First posted 8 May 2025cs.CL cs.MAcited by 3

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

Abstract (arXiv)

In this position paper, we advocate for the development of conversational technology that is inherently designed to support and facilitate argumentative processes. We argue that, at present, large language models (LLMs) are inadequate for this purpose, and we propose an ideal technology design aimed at enhancing argumentative skills. This involves re-framing LLMs as tools to exercise our critical thinking skills rather than replacing them. We introduce the concept of textitreasonable parrots that embody the fundamental principles of relevance, responsibility, and freedom, and that interact through argumentative dialogical moves. These principles and moves arise out of millennia of work in argumentation theory and should serve as the starting point for LLM-based technology that incorporates basic principles of argumentation.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 7 of its 45 references that are in the map. First posted on arXiv 8 May 2025.

Builds on

7 of 45 references.

References · 7

  1. 2025Multi-Agent Collaboration Mechanisms: A Survey of LLMscited by 431
  2. 2024Cognitive Bias in Decision-Making with LLMscited by 106
  3. 2024Argumentative Large Language Models for Explainable and Contestable Claim Verificationcited by 36
  4. 2024Evaluating the Robustness of Analogical Reasoning in Large Language Modelscited by 31
  5. 2024Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM argumentscited by 30
  6. 2025Improving Chain-of-Thought Reasoning via Quasi-Symbolic Abstractionscited by 17
  7. 2025Implicit Bias in LLMs: A Surveycited by 8

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025DS@GT at Touch'e: Large Language Models for Retrieval-Augmented Debate
  2. 2025Can LLMs Judge Debates? Evaluating Non-Linear Reasoning via Argumentation Theory Semantics
  3. 2026Evaluating the Capabilities of LLMs for Persuasive Dialogue
  4. 2024Critical-Questions-of-Thought: Steering LLM reasoning with Argumentative Querying
  5. 2026ArgLLM-App: An Interactive System for Argumentative Reasoning with Large Language Models
  6. 2023"I'd Like to Have an Argument, Please": Argumentative Reasoning in Large Language Models
  7. 2026Argument Quality Assessment with Large Language Models: A Pairwise Bradley-Terry Approach
  8. 2023Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration