REVIEW 4 major objections 4 minor 47 references
Toward Reasonable Parrots: Why Large Language Models Should Argue with Us by Design
T0 review · 4 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash
Pith's one-line read This paper argues that large language models should be redesigned as 'reasonable parrots' that argue with users through dialogical moves, to enhance rather than replace critical thinking.
desk verdict A solid, well-grounded position paper whose four-parrot design pattern is a real contribution, but whose central empirical premise about improving users' critical thinking remains untested. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The central object is the 'reasonable parrot', a conversational agent guided by the principles of relevance, responsibility, and freedom and by argumentative dialogical moves from pragma-dialectics. The concrete mechanism is a multi-parrot environment: four personas—Socratic (challenges starting points and beliefs), Cynical (rebuts standpoints and arguments), Eclectic (offers alternative perspectives), and Aristotelian (points out fallacies)—interact with the user and with each other to open up space for agreement and disagreement, fostering critical reflection rather than delivering a finished conclusion.
What would settle it
Run a randomized experiment in which one group discusses a contested issue with a reasonable-parrot multi-agent system, a control group with a standard answer-giving LLM, and a third group with no AI assistance, measuring critical-thinking disposition and argument quality before and after; if the reasonable-parrot group shows no greater gain than the control group, the central claim is falsified.
Extended reading notes
Core claim
The central claim is that conversational AI should externalize reasoning by confronting users with diverse argumentative viewpoints, through a multi-parrot system where each parrot embodies a distinct critical role. The paper demonstrates with prototype dialogues that current LLMs can be prompted to play Socratic, Cynical, Eclectic, and Aristotelian personas, and it argues that this process-oriented design, rooted in pragma-dialectical rules for critical discussion, would enhance critical thinking. The contribution is a design principle and an architectural sketch, not an empirical result: LLMs should be judged by whether they improve their interlocutor's reasoning, regardless of the parrot's own performance.
Load-bearing premise
The proposal stands on the untested premise that being challenged by argumentative dialogical moves improves a user's critical thinking; if users disengage or learn nothing, the reasonable-parrot design loses its purpose.
Editorial extensions
If this is right
- LLM design goals would shift from producing persuasive answers to facilitating an argumentative process, with evaluation metrics based on users' critical thinking gains.
- The multi-parrot persona structure can be instantiated in existing LLMs through prompting, as shown across three different models in the paper.
- Such technology could explicitly counteract fallacies like the appeal to popularity by questioning whether popularity is a valid reason for belief or action.
- The proposed principles give a concrete starting point for building deliberation-support tools in high-stakes domains such as medicine, finance, and human resources.
- The design would externalize reasoning by putting diverse viewpoints in front of the user, instead of hiding deliberation inside the model.
Reading between the lines
- The paper's core premise—that being challenged by argumentative dialogical moves improves a user's critical thinking—remains untested; a controlled study measuring critical thinking before and after reasonable-parrot versus standard LLM interaction would settle it.
- If the premise holds, reasonable parrots could complement explainable AI by making model reasoning contestable rather than merely transparent, which may increase user trust and scrutiny.
- The multi-parrot approach might also improve LLM self-consistency by externalizing disagreement across personas instead of relying on internal chain-of-thought, though the paper does not test this.
- A risk the authors leave implicit is user disengagement: if challenging questions feel adversarial, people may abandon the conversation, so the design likely needs to balance challenge with perceived helpfulness.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. This position paper argues that large language models (LLMs) should be designed not as providers of final answers but as interlocutors that engage users in argumentative dialogue, thereby exercising rather than replacing human critical thinking. The authors introduce the concept of 'reasonable parrots' grounded in pragma-dialectical argumentation theory, organized around the principles of relevance, responsibility, and freedom, and instantiated through four parrot personas: Socratic, Cynical, Eclectic, and Aristotelian. After critiquing current LLMs as 'unreasonable' on the basis of two illustrative ChatGPT responses, the paper proposes a multi-parrot dialogue design and demonstrates it with system prompts and short user–parrot transcripts generated by GPT-4 Turbo, Claude 3.7, and Llama 3.1. The paper concludes by calling for a shift from argumentative products to argumentative processes in LLM-based conversational technology.
Significance. The proposal is timely and well-motivated: it brings a rich tradition of argumentation theory into the design of conversational AI and offers a concrete, implementable sketch (the multi-parrot personas) that could inform future HCI research. The paper is honest about the illustrative nature of its examples and does not claim to have solved the problem. Its main contribution is conceptual—reframing LLMs as tools for fostering deliberative skills—and that framing is worth taking seriously. The significance is conditional, however, on empirical validation of the central causal premise that argumentative dialogical moves improve users' critical thinking, and on evidence that users remain engaged rather than disengaging when challenged. The paper currently provides neither, so its value lies primarily in defining a research agenda rather than demonstrating an effective technology.
major comments (4)
- [Section 4, paragraph beginning 'As a caveat' through 'Prototypical Realization'] The central claim that 'reasonable parrots are meant to trigger improved reasoning skills in their interlocutor, regardless of their performance' rests on an empirical causal premise that is asserted without supporting evidence. The prototype in Tables 2–4 demonstrates only that GPT-4 Turbo, Claude 3.7, and Llama 3.1 can follow a prompt instructing them to enact Socratic, Cynical, Eclectic, and Aristotelian moves; it does not measure any effect on the user's reasoning, learning, engagement, trust, or subsequent behavior. The cited studies do not bridge this gap: Costello et al. (2024) measures belief change about conspiracy theories, not acquisition of general critical-thinking skills, and Ma et al. (2025) evaluates constrained binary decisions rather than an open multi-parrot dialogue. As it stands, the reason to prefer 'argue by design' over ordinary Q&A is unsupported.
- [Section 3, Query 1 and Response 1] The diagnosis of current LLM unreasonableness relies on two hand-picked queries answered by ChatGPT on a single date. The paper explicitly acknowledges that the example is 'not claimed to generalize,' which is appropriate, but the critique is nevertheless assessed against the very ideal critical discussion framework (van Eemeren and Grootendorst, 2003) that is later used to define the proposed design. This makes the evaluation partly circular: current LLMs are judged unreasonable by a standard that the paper itself selects, and the prototype is then judged reasonable by the same standard. To make the argument more robust, the paper should either sample a broader set of queries or state more clearly that the example serves only as an indexical illustration, not as an empirical diagnosis.
- [Section 4, Table 1 and Tables 2–4] The multi-parrot dialogues are generated with a system prompt that explicitly instructs the parrots to challenge starting points, rebut arguments, offer alternatives, and point out fallacies. The resulting transcripts therefore show that the models can follow this instruction, not that the reasonable-parrots design is effective or that the observed behavior would arise without such explicit prompting. The claim that 'all models show notable similarities in their approach to user interaction' is a statement about prompt compliance and surface behavior, not about whether the design achieves its goal of improving user critical thinking. The paper should not present these transcripts as evidence for the proposal's benefits; at most they illustrate a feasible interaction pattern.
- [Section 4, Table 1; Section 5 conclusion] A plausible failure mode of the proposed design is user disengagement: the prompt allows the user to 'end the conversation anytime,' and if challenge is perceived as adversarial, condescending, or cognitively overloading, users may exit exactly when the system is trying to provoke reflection. The paper gives no evidence about user perceptions of the parrots' tone, trustworthiness, or perceived adversarialness, nor about whether the dialogical moves are experienced as informative rather than annoying. This is not a fatal objection in a position paper, but it is a load-bearing uncertainty for the central claim and should be addressed either by explicit acknowledgment as an open research question or by pilot data.
minor comments (4)
- [Section 5, conclusion] The text contains garbled fragments: 'for developinghci! evaluation metrics' and 'more reasonable hci! (hci!)' appear to be missing spaces or formatting errors; these should be corrected to 'HCI' with proper spacing.
- [Table 4] There is a punctuation error in the Cynical parrot's turn: the transcript shows 'Cynical parrot:. Ah' with an extra period after the colon; this should be cleaned up.
- [Section 2, sentence about Kiesel et al.] The phrase 'as an possible way' should read 'as a possible way'; this is a minor grammatical error.
- [Figure 1] The figure is referenced in Section 1 but has no caption and its labels ('Socratic Eclectic Cynical Aristotelian') are compressed; adding a brief caption and explaining the arrows would improve readability.
Circularity Check
No significant circularity: the paper is a normative position piece; its 'reasonable parrots' design is stipulated from argumentation theory and the prototype is explicitly illustrative rather than a prediction or fitted validation.
full rationale
The paper makes no quantitative derivation and fits no parameters. Its central claim—LLMs should argue with us by design—is normative, and the 'reasonable parrots' concept is defined directly from argumentation-theoretic principles (relevance, responsibility, freedom; pragma-dialectical moves) rather than inferred from data. The ChatGPT examples in Section 3 are explicitly 'not claimed to generalize across all LLMs, but rather to serve the indexical function of highlighting argumentative issues,' so they are illustrations, not derivation steps. The multi-parrot dialogues in Section 4 are presented as a 'prototypical realization' prompted to instantiate the proposed design; because the prompt already contains the target behavior, the dialogues cannot independently validate effectiveness, but the paper does not claim they do—it says the parrots 'are meant to trigger improved reasoning skills in their interlocutor' as a design goal, not as an observed, predicted, or fitted outcome. The unmeasured causal premise (argumentative moves improve users' critical thinking) is a substantive empirical gap and a correctness risk, but it is not circular: it is an unsupported assertion, not a renaming of the input. Self-citations (Steging et al. 2021; Musi and Palmieri 2024; Visser and Lawrence 2022) support contextual claims and are not the sole load-bearing justification of the central proposal. No equation reduces to itself by construction, no fitted parameter is renamed as a prediction, and no self-citation chain forces the conclusion.
Assumptions & free parameters
assumptions (4)
- domain assumption LLMs are stochastic parrots that do not understand language in the human sense (Bender et al., 2021).
- domain assumption Pragma-dialectical critical discussion is a valid normative model of reasonableness.
- ad hoc to paper Engaging users with argumentative dialogical moves will enhance their critical thinking skills.
- ad hoc to paper LLMs can consistently perform designated argumentative roles through prompting, without undermining the dialogue.
invented entities (2)
-
Reasonable parrots (conversational technology concept)
-
Socratic, Cynical, Eclectic, and Aristotelian parrot personas
Cite this review
Pith. "Pith review of Toward Reasonable Parrots: Why Large Language Models Should Argue with Us by Design." pith.science (2026). https://pith.science/paper/DVOGTRQG
@misc{pith2026250505298,
author = {Pith},
title = {Pith review of: Toward Reasonable Parrots: Why Large Language Models Should Argue with Us by Design},
year = {2026},
howpublished = {\url{https://pith.science/paper/DVOGTRQG}},
note = {Machine review of arXiv:2505.05298}
}
read the original abstract
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 \textit{reasonable 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.
Figures
Reference graph
Works this paper leans on
-
[1]
online" 'onlinestring :=
ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRING...
-
[2]
write newline
" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...
-
[3]
Zeynep Akata, Dan Balliet, Maarten de Rijke, Frank Dignum, Virginia Dignum, Guszti Eiben, Antske Fokkens, Davide Grossi, Koen Hindriks, Holger Hoos, Hayley Hung, Catholijn Jonker, Christof Monz, Mark Neerincx, Frans Oliehoek, Henry Prakken, Stefan Schlobach, Linda van der Gaag, Frank van Harmelen, and 7 others. 2020. https://doi.org/10.1109/MC.2020.299658...
arXiv 2020
-
[4]
Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell
Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. https://doi.org/10.1145/3442188.3445922 On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, FAccT '21, pages 610--623
arXiv 2021
-
[5]
Carlos Carrasco-Farre. 2024. https://arxiv.org/abs/2404.09329 Large language models are as persuasive as humans, but how? A bout the cognitive effort and moral-emotional language of LLM arguments . Preprint, arXiv:2404.09329
arXiv 2024
-
[6]
Costello, Gordon Pennycook, and David G
Thomas H. Costello, Gordon Pennycook, and David G. Rand. 2024. https://doi.org/10.1126/science.adq1814 Durably reducing conspiracy beliefs through dialogues with AI . Science, 385(6714):eadq1814
-
[7]
Marcel Danesi and Andrea Rocci. 2009. Global linguistics: An introduction. Mouton de Gruyter
work page 2009
-
[8]
Adrian de Wynter and Tangming Yuan. 2024. https://doi.org/10.3233/FAIA240311 `` I'd like to have an argument, please'': Argumentative reasoning in large language models . In Computational Models of Argument, pages 73--84
Show all 47 references
-
[9]
Tenenbaum, and Igor Mordatch
Yilun Du, Shuang Li, Antonio Torralba, Joshua B. Tenenbaum, and Igor Mordatch. 2024. https://openreview.net/forum?id=zj7YuTE4t8 Improving factuality and reasoning in language models through multiagent debate . In Proceedings of the 41st International Conference on Machine Lear...
2024
-
[10]
Jessica Echterhoff, Yao Liu, Abeer Alessa, Julian McAuley, and Zexue He. 2024. https://arxiv.org/abs/2403.00811 Cognitive bias in decision-making with LLMs . Preprint, arXiv:2403.00811
2024 arXiv
-
[11]
Roxanne El Baff, Khalid Al Khatib, Milad Alshomary, Kai Konen, Benno Stein, and Henning Wachsmuth. 2024. Improving argument effectiveness across ideologies using instruction-tuned large language models. In Findings of the Association for Computational Linguistics: EMNLP 2024, ...
2024
-
[12]
Peter A Facione. 2023. https://insightassessment.com/iaresource/critical-thinking-what-it-is-and-why-it-counts/ Critical thinking: What it is and why it counts . Technical report, Insight Assessment, a division of California Academic Press
2023
-
[13]
Gabriel Freedman, Adam Dejl, Deniz Gorur, Xiang Yin, Antonio Rago, and Francesca Toni. 2024. https://arxiv.org/abs/2405.02079 Argumentative large language models for explainable and contestable decision-making
2024 arXiv
-
[14]
Gallegos, Ryan A
Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, Md Mehrab Tanjim, Sungchul Kim, Franck Dernoncourt, Tong Yu, Ruiyi Zhang, and Nesreen K. Ahmed. 2024. https://doi.org/10.1162/coli_a_00524 Bias and fairness in large language models: A survey . Computational Linguistics, 50(3):1097--1179
2024 doi
-
[15]
Steffen Herbold, Annette Hautli-Janisz, Ute Heuer, Zlata Kikteva, and Alexander Trautsch. 2023. https://doi.org/10.1038/s41598-023-45644-9 A large-scale comparison of human-written versus ChatGPT-generated essays . Scientific Reports, 13:18617
2023 doi
-
[16]
Zhe Hu, Hou Pong Chan, and Yu Yin. 2024. https://aclanthology.org/2024.inlg-main.8/ AMERICANO : Argument generation with discourse-driven decomposition and agent interaction . In Proceedings of the 17th International Natural Language Generation Conference, pages 82--102
2024
-
[17]
Michael J Hyde. 2004. The ethos of rhetoric. University of South Carolina Press
2004
-
[18]
Arjun Karanam, Farnaz Jahanbakhsh, and Sanmi Koyejo. 2024. https://openreview.net/forum?id=gEg2p6Az1k Towards deliberating agents: Evaluating the ability of large language models to deliberate . In NeurIPS 2024 Workshop on Behavioral Machine Learning
2024
-
[19]
Johannes Kiesel, Damiano Spina, Henning Wachsmuth, and Benno Stein. 2021. https://doi.org/10.1145/3469595.3469615 The meant, the said, and the understood: Conversational argument search and cognitive biases . In Proceedings of the 3rd Conference on Conversational User Interfac...
2021
-
[20]
Yubin Kim, Chanwoo Park, Hyewon Jeong, Yik Siu Chan, Xuhai Xu, Daniel McDuff, Hyeonhoon Lee, Marzyeh Ghassemi, Cynthia Breazeal, and Hae Park. 2024. https://proceedings.neurips.cc/paper_files/paper/2024/file/90d1fc07f46e31387978b88e7e057a31-Paper-Conference.pdf MDAgents : An a...
2024
-
[21]
Jones, Sai Krishna Revanth Vuruma, Vishal Pallagani, Bharath C Muppasani, and Biplav Srivastava
Kausik Lakkaraju, Sara E. Jones, Sai Krishna Revanth Vuruma, Vishal Pallagani, Bharath C Muppasani, and Biplav Srivastava. 2023. https://doi.org/10.1145/3604237.3626867 LLMs for financial advisement: A fairness and efficacy study in personal decision making . In Proceedings of...
2023
-
[22]
H \'e l \`e ne Landemore. 2013. Democratic reason: Politics, collective intelligence, and the rule of the many. Princeton University Press
2013
-
[23]
Larson, Christine Moser, Arran Caza, Katrin Muehlfeld, and Laura A Colombo
Barbara Z. Larson, Christine Moser, Arran Caza, Katrin Muehlfeld, and Laura A Colombo. 2024. https://doi.org/10.5465/amle.2024.0338 Critical thinking in the age of generative AI . Academy of Management Learning & Education, 23(3):373--378
2024
-
[24]
Lewis and M
M. Lewis and M. Mitchell. 2024. https://arxiv.org/abs/2411.14215 Evaluating the robustness of analogical reasoning in large language models . Preprint, arXiv:2411.14215
2024 arXiv
-
[25]
Yitan Li, Linli Xu, Fei Tian, Liang Jiang, Xiaowei Zhong, and Enhong Chen. 2015. https://www.ijcai.org/Proceedings/15/Papers/513.pdf Word embedding revisited: A new representation learning and explicit matrix factorization perspective. In Proceedings of the Twenty-Fourth Inter...
2015
-
[26]
Tian Liang, Zhiwei He, Wenxiang Jiao, Xing Wang, Yan Wang, Rui Wang, Yujiu Yang, Shuming Shi, and Zhaopeng Tu. 2024. https://doi.org/10.18653/v1/2024.emnlp-main.992 Encouraging divergent thinking in large language models through multi-agent debate . In Proceedings of the 2024 ...
2024 doi
-
[27]
Xinru Lin and Luyang Li. 2025. https://arxiv.org/abs/2503.02776 Implicit bias in LLMs : A survey . Preprint, arXiv:2503.02776
2025 arXiv
-
[28]
Dirk Lindebaum and Peter Fleming. 2024. https://doi.org/10.1111/1467-8551.12781 ChatGPT undermines human reflexivity, scientific responsibility and responsible management research . British Journal of Management, 35(2):566--575
2024
-
[29]
Shuai Ma, Qiaoyi Chen, Xinru Wang, Chengbo Zheng, Zhenhui Peng, Ming Yin, and Xiaojuan Ma. 2025. https://doi.org/10.1145/3706598.3713423 Towards human-ai deliberation: Design and evaluation of LLM -empowered deliberative AI for AI -assisted decision-making . In Proceedings of ...
2025
-
[30]
Elena Musi and Rudi Palmieri. 2024. https://ceur-ws.org/Vol-3769/paper8.pdf The fallacy of explainable generative AI: evidence from argumentative prompting in two domains . In Proceedings of the 24th Workshop on Computational Models of Natural Argument co-located with 10th Int...
2024
-
[31]
Piantadosi and Felix Hill
Steven T. Piantadosi and Felix Hill. 2022. https://openreview.net/forum?id=nRkJEwmZnM Meaning without reference in large language models . In NeurIPS 2022 Workshop on neuro Causal and Symbolic AI
2022
-
[32]
Henry Prakken. 2011. An overview of formal models of argumentation and their application in philosophy. Studies in Logic, 4(1):65--86
2011
-
[33]
Leonardo Ranaldi, Marco Valentino, Alexander Polonsky, and Andr \`e Freitas. 2025. https://arxiv.org/abs/2502.12616 Improving chain-of-thought reasoning via quasi-symbolic abstractions
2025
-
[34]
Ameer Saadat-Yazdi and Nadin K \"o kciyan. 2024. https://doi.org/10.18653/v1/2024.acl-long.520 Beyond recognising entailment: Formalising natural language inference from an argumentative perspective . In Proceedings of the 62nd Annual Meeting of the Association for Computation...
2024 doi
-
[35]
Pan, and Nadin Kokciyan
Ameer Saadat-Yazdi, Jeff Z. Pan, and Nadin Kokciyan. 2023. https://doi.org/10.18653/v1/2023.eacl-main.182 Uncovering implicit inferences for improved relational argument mining . In The 17th Conference of the European Chapter of the Association for Computational Linguistics, 2...
2023 doi
-
[36]
Sheth and Michael R
Jagdish N. Sheth and Michael R. Solomon. 2014. https://doi.org/10.2753/MTP1069-6679220201 Extending the extended self in a digital world . Journal of Marketing Theory and Practice, 22(2):123--132
2014 doi
-
[37]
Noam Slonim, Yonatan Bilu, Carlos Alzate, Roy Bar-Haim , Ben Bogin, Francesca Bonin, Leshem Choshen, Edo Cohen-Karlik , Lena Dankin, Lilach Edelstein, Liat Ein-Dor , Roni Friedman-Melamed , Assaf Gavron, Ariel Gera, Martin Gleize, Shai Gretz, Dan Gutfreund, Alon Halfon, Daniel...
2021 doi
-
[38]
Cor Steging, Silja Renooij, and Bart Verheij. 2021. https://doi.org/10.3233/FAIA210341 Rationale discovery and explainable AI . In Legal Knowledge and Information Systems - JURIX 2021: The Thirty-Fourth Annual Conference , volume 346 of Frontiers in Artificial Intelligence and...
2021 doi
-
[39]
Jingran Sun. 2024. https://doi.org/10.62677/IJETAA.2408125 Research on the application of large language models in human resource management practices . International Journal of Emerging Technologies and Advanced Applications, 1:1--8
2024 doi
-
[40]
Khanh-Tung Tran, Dung Dao, Minh-Duong Nguyen, Quoc-Viet Pham, Barry O'Sullivan, and Hoang D. Nguyen. 2025. https://arxiv.org/abs/2501.06322 Multi-agent collaboration mechanisms: A survey of LLMs . Preprint, arXiv:2501.06322
2025 arXiv
-
[41]
van Eemeren
Frans H. van Eemeren. 2015. Reasonableness and effectiveness in argumentative discourse, volume 27 of Argumentation Library. Springer
2015
-
[42]
van Eemeren and Rob Grootendorst
Frans H. van Eemeren and Rob Grootendorst. 2003. https://doi.org/10.1023/A:1026334218681 A pragma-dialectical procedure for a critical discussion . Argumentation, 17:365--386
2003 doi
-
[43]
Jacky Visser and John Lawrence. 2022. https://doi.org/10.3233/FAIA220180 The skeptic web service: Utilising argument technologies for reason-checking . In Proceedings of the International Conference on Computational Models of Argument ( COMMA 2022), Cardiff, United Kingdom , p...
2022 doi
-
[44]
Douglas Walton. 2006. Fundamentals of critical argumentation. Cambridge University Press
2006
-
[45]
Douglas N. Walton. 1980. https://www.jstor.org/stable/40237163 Why is the `ad populum' a fallacy? Philosophy & Rhetoric, 13(4):264--278
1980
-
[46]
Holyoak, and Hongjing Lu
Taylor Webb, Keith J. Holyoak, and Hongjing Lu. 2023. https://doi.org/10.1038/s41562-023-01659-w Emergent analogical reasoning in large language models . Nature Human Behaviour, 7:1526--1541
2023 doi
-
[47]
Timon Ziegenbein, Gabriella Skitalinskaya, Alireza Bayat Makou, and Henning Wachsmuth. 2024. https://doi.org/10.18653/v1/2024.acl-long.244 LLM -based rewriting of inappropriate argumentation using reinforcement learning from machine feedback . In Proceedings of the 62nd Annual...
2024 doi
Reviewed August 15, 2026 · model on record in the stance chip above.
Discussion (0). Continue with ORCID to comment.