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REVIEW 4 major objections 5 minor 46 references

InspireDebate: Multi-Dimensional Subjective-Objective Evaluation-Guided Reasoning and Optimization for Debating

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Adding fact and logic checks to debate judging raises agreement with human experts by 44%, the paper reports, and the same signal can be used to train open-source debaters to gain 57% over baselines.

desk verdict Useful debate evaluation/optimization recipe, but the 'objective' logical-validity leg collapses on its own example and the reported 57% gain is partly circular. read the letter →

arxiv 2506.18102 v1 pith:QYZMVJT2 submitted 2025-06-22 cs.CL

Fuyu Wang , Jiangtong Li , Kun Zhu , Changjun Jiang This is my paper · ORCID
classification cs.CL
keywords debateevaluationLLM-as-a-judgedirectpreferenceoptimizationchain-of-thoughtreasoningretrieval-augmentedgenerationfirst-orderlogicfactverificationargumentqualityassessment
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that debate quality can be measured along six axes—emotional appeal, argument clarity, argument arrangement, topic relevance, fact authenticity, and logical validity—and that the resulting judge, InspireScore, correlates 44% better with expert ratings than existing LLM-based evaluators. It then claims that the same six-axis signal can be turned into better debating behavior: supervised fine-tuning that teaches chain-of-thought argumentation, direct preference optimization that rewards whichever side InspireScore rates higher, and web retrieval that grounds claims in real-time evidence. On 100 held-out topics, the full pipeline raises open-source models' debate scores by 57% over their baselines, with optimized 8B models surpassing o1-mini on the authors' metric. If these results hold, automated debate judgment becomes less a matter of rhetorical style and more a matter of verifiable substance, and small open models become competitive with proprietary ones in structured argumentation.

What carries the argument

The load-bearing machinery is a six-dimension scorecard plus a two-stage training loop. The scorecard averages subjective scores over debate rounds for emotional appeal, argument clarity, argument arrangement, and topic relevance; computes fact authenticity as the proportion of atomic facts verified true against web search results; and computes logical validity as the proportion of conclusions that survive a two-step pipeline in which natural-language arguments are converted to first-order logic predicates and then checked with inference rules such as Modus Ponens. On the optimization side, GPT-4o generates structured chain-of-thought responses for SFT, DPO uses InspireScore to pick winning and losing responses for preference pairs, and Web-RAG extracts keywords and retrieves web evidence into the argument generator.

What would settle it

Run InspireScore's logical-validity pipeline on deliberately invalid arguments—for example, 'Schools are responsible for promoting health' must not imply 'Schools should ban junk food'—and check how often the pipeline labels such non-sequiturs as true; if a human logician or a formal theorem prover systematically disagrees with the LLM's verdicts, the objective dimension is not measuring validity and the reported correlation advantage does not establish better measurement.

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Extended reading notes

Core claim

The paper's central claim is that adding two objective criteria to the usual subjective ones—fact authenticity checked against web search and logical validity checked through first-order-logic formalization—yields a debate evaluation that tracks expert judgment far better than previous LLM judges, and that the same evaluation can serve as a reward signal for training debaters. On the authors' human-annotated dataset of 200 sides of debates, InspireScore reaches Pearson, Spearman, and Kendall correlations of 0.643, 0.581, and 0.479 against expert ratings, an average 44% improvement over Debatrix, and it achieves the lowest RMSE among tested models at predicting winners of real human debates. The companion claim is that optimizing with SFT plus chain-of-thought, multi-dimensional DPO, and Web-RAG converts this measurement into behavior: every optimized open-source model beats its base, Inspire-DeepSeek-R8B outperforms o1-mini, and the full Inspire-LLaMA-8B scores 0.732 versus 0.393 for the base model on the authors' metric.

Load-bearing premise

The framework's objectivity rests on the assumption that the LLM converts natural-language arguments into first-order logic faithfully and then applies inference rules strictly; if that step is lenient, the logical-validity score is not the objective measure it claims to be.

Editorial extensions

If this is right

  • If InspireScore is right, debate evaluation no longer has to choose between style and substance; the same judge can report both rhetorical quality and verifiable accuracy.
  • The SFT stage alone provides the largest subjective gains, while Web-RAG is what lifts fact authenticity and logical validity, so each component is needed for the full 57% improvement.
  • The reported results imply that small open-source models can be brought close to proprietary models on structured argumentation with only a few hours of LoRA training on two V100 GPUs.
  • The DPO ablation suggests that optimizing on a single dimension, especially emotional appeal, is far less effective than combining dimensions, which argues for keeping the full six-axis reward.
  • A judge that predicts human debate winners with the lowest RMSE of tested methods could be reused beyond training, such as for automated feedback in debate coaching.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Since InspireScore is both the evaluator and the source of DPO preference labels, the 57% gain may partly reflect the model learning to please this particular judge rather than improving at debate in general; a held-out test against independent human judges would separate the two.
  • The logical-validity score is only as objective as the LLM's formalization step, so replacing the LLM's true/false/unknown verdict with an external theorem prover over the generated first-order logic would make the objective dimension genuinely checkable.
  • The same fact-authenticity pipeline could transfer directly to other hallucination-prone generation tasks such as news summarization or medical question answering, because it does not depend on debate structure.
  • A natural extension, which the authors note is missing, is to let the evaluation dimensions themselves adapt per topic or per user preference, potentially making the judge less fixed and more useful across domains.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes a two-component framework for LLM debate. InspireScore is a multi-dimensional evaluation system combining four subjective criteria (emotional appeal, argument clarity, argument arrangement, topic relevance) with two 'objective' criteria (fact authenticity via web-search verification and logical validity via first-order-logic formalization). InspireDebate is an optimization pipeline that applies supervised fine-tuning with chain-of-thought outputs, multi-dimensional Direct Preference Optimization using InspireScore as the reward signal, and Web-RAG for factual grounding. The authors report that InspireScore achieves 44% higher correlation with expert judgments than prior evaluators (notably Debatrix) and that InspireDebate improves open-source LLM debate scores by 57%, including human-evaluation gains, with ablations for each component.

Significance. If the claims hold, the framework contributes a more comprehensive debate-evaluation surrogate and a practical recipe for transferring evaluation signals into model behavior. Credit is due for releasing source code, for including a human-evaluation check (Table 5), and for testing on four open-source models of varied sizes. The central significance hinges on two things that the current manuscript does not yet establish: (i) that the logical-validity component is actually objective and sound, and (ii) that the reported gains are not largely an artifact of optimizing and measuring with the same metric. Because the manuscript already contains partial human validation, the project is salvageable, but the logical-validity inconsistency in Table 14 undermines the most distinctive claimed contribution until fixed.

major comments (4)
  1. [§3.2, Table 14] The logical-validity pipeline is internally inconsistent, and this is load-bearing for the claimed objectivity of InspireScore. In the worked example, Premise 3 is SchoolResponsibility(PromoteHealth). Conclusion 2, SchoolResponsibility(PromoteHealth) → ShouldBan(JunkFood), does not follow from the premises: nothing links the responsibility to promote health with a duty to ban junk food, and no additional premise such as PromoteHealth → ShouldBan(JunkFood) is given. The judge nonetheless labels Conclusion 2 as 'true' on the grounds that Premise 3 'reinforces' it. Since Eq. (3) defines SLV as the proportion of such verdicts, and §4.2 uses InspireScore (including LV) to construct DPO preference pairs, a systematically lenient FOL judge inflates both the objectivity claim and the optimization gain. The authors must either repair the inference rules so that only genuinely derivable conclusions are marked true, or validate the judge against a gold-standard set of formally checked inferences and report per-dimension agreement. As written, the claim that InspireScore adds an objective dimension is unsupported.
  2. [§4.2 and §5.3] The optimization and evaluation loop is partially circular: InspireScore is used both to select winning/losing pairs for DPO (Eq. 5) and to compute final model scores in Table 4. The 57% improvement over baselines may in part reflect the model learning to satisfy the particular judge rather than improving debate quality in a general sense. The authors do provide a human evaluation in Table 5, which shows gains on the same six dimensions, but the protocol is under-specified: there is no reported number of human-evaluated debates beyond the 100 topics, no inter-annotator agreement statistic, and the threshold described in Appendix B.5 ('difference less than 2 points') is not a standard reliability measure. Please report annotator counts, Cohen's kappa or Krippendorff's alpha, and per-dimension human-evaluation comparisons, and consider adding a judge-independent outcome measure (e.g., expert pairwise preference on a held-out sample).
  3. [§5.2, Tables 2 and 3] The headline correlation claim of a 44% improvement over Debatrix is reported without any uncertainty quantification. With 200 annotated samples (100 topics, two sides) and three annotators, a difference between Pearson 0.394 and 0.643 could be within sampling variability, especially because the three correlation metrics are not independent. Report bootstrap confidence intervals, p-values for the difference, or at least standard errors, for both Table 2 and Table 3. The same applies to the 57% claim in Table 4; the per-model differences are large, but no variance estimate is supplied despite 1,800 debates per model.
  4. [§2, §5.3] The comparison omits prior debate-optimization baselines that the paper itself discusses: MAD (Liang et al., 2024b) and DebateTune (Li et al., 2024) are mentioned in the introduction and related work as debate frameworks, but Table 4 includes no model trained or optimized with those methods. As a result, the claim that InspireDebate 'outperforms baseline models by 57%' does not establish superiority over existing debate-specific optimization approaches. The authors should either add these baselines or explicitly frame the 57% as the gain over the four base open-source models without a multi-agent debate loop.
minor comments (5)
  1. [Table 4 header] The table header spells the framework name 'InsightScore' instead of 'InspireScore'; this appears in the column header and should be corrected.
  2. [Appendix B.4, Table 19] The label 'Vanila' is a typo for 'Vanilla', and the caption does not state whether the dimension-specific DPO uses the same preference-pair construction as Eq. (5) or a modified reward.
  3. [Eqs. (1)–(3)] The summation ranges are typeset inconsistently (e.g., 'mX' and missing lower indices in Eq. (1) and Eq. (2)); please ensure all sums display i=1 to m and define all variables at first use.
  4. [§5.2] The sentence 'InspireScore achieve an average improvement of 44% over Debatrix across the these three correlation metrics' needs grammatical cleanup ('achieves', 'these').
  5. [Appendix B.5] The human-evaluation instructions say scores are 1–10, while the automatic evaluation uses 0–1 scores (Eq. 1); specifying how scores were normalized before computing correlations would remove ambiguity.

Circularity Check

1 steps flagged · score 4.0 of 10

Headline 57% improvement is measured by the same InspireScore that generated the DPO preference pairs; human evaluation (Table 5) provides independent but partial support.

  1. fitted input called prediction [Section 4.2 (Multi-Dimensional Optimization via DPO, Eq. 5) and Section 5.3 (Table 4)]
    "we leverage InspireScore to fine-tune the model with explicit, multi-dimensional feedback... Given a DPO dataset containing debate samples evaluated by InspireScore, we construct preference pairs (yw, yl), where yw (the winning ones) achieves a higher InspireScore than yl (the losing ones). ... We report average debate scores in Table 4 using InspireScore."

    The DPO loss in Eq. 5 is trained on preference pairs selected by comparing InspireScore values, so the optimization directly targets higher InspireScore. The paper's headline claim of a 57% improvement over baselines is then computed from the same InspireScore scores in Table 4. The test metric is thus the training reward, so the Table 4 gain is partly the model fitting the judge rather than an independent measure of debate quality. This is not a complete reduction: Table 5 reports independent human evaluation and shows similar directional gains, which supports the optimization claim. But the specific 57% figure is measured on the training reward and is not independent.

full rationale

The only clear circularity is the DPO reward/evaluation loop: InspireScore selects y_w/y_l for DPO (Section 4.2) and is also the metric behind the 57% improvement claim (Section 5.3, Table 4). The paper's own human evaluation (Table 5) partially breaks the loop, so the central optimization claim retains independent content; the 57% number itself should be treated as a reward-alignment measure. I found no load-bearing self-citation: the references cited for the debate topic library and baselines are not by the present authors, and no uniqueness theorem is imported from the authors' prior work. The logical-validity pipeline has an internal soundness problem (Table 14 labels a non-implication as true), but that is a correctness risk, not a circularity step under the enumerated patterns. Subjective-evaluation dimensions matching the human annotation rubric is by design and is not circular. Overall score 4 reflects partial circularity with substantial independent evaluation.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The framework rests on standard LLM fine-tuning assumptions plus a domain-specific assumption that prompted LLM judges can measure both subjective quality and objective validity. The key unvalidated piece is the logical validity pipeline, which the paper's own example shows can be overly lenient.

free parameters (3)
  • Dimension weights in aggregate InspireScore = equal (1/6 each)
    The paper never defines the aggregate score explicitly, but the InspireScore column in Table 4 matches a simple mean of the six dimension scores. Equal weighting is a design choice that affects every reported improvement and is not justified empirically.
  • Number of debate rounds = 4 (example) / configurable
    Debate length affects both evaluation scores and training cost; the paper does not analyze sensitivity to this choice.
  • Annotator agreement threshold = difference < 2 points
    The human evaluation re-annotation rule (Appendix B.5) uses an arbitrary threshold, which influences the quality of the gold labels.
assumptions (4)
  • domain assumption LLM-based first-order logic formalization and inference accurately capture argument validity
    Section 3.2 and Appendix A.3 assume an LLM can convert natural language to FOL and judge validity, yet the paper's own example (Table 14) marks a non-sequitur as 'true', so the assumption is fragile.
  • domain assumption Web search results provide sufficient and reliable evidence for fact authenticity
    Section 3.2 relies on Serper API results as ground truth for facts; search coverage and result quality are not independently audited.
  • domain assumption The 100 evaluation topics are representative of debate quality
    Section 5.1 samples from a predefined library; no topic-diversity analysis is provided, so the measured correlations may not generalize.
  • domain assumption Human annotators' average scores are a reliable gold standard
    Section 5.2 uses three annotators with master's degrees and debate experience, but inter-annotator agreement is not reported.

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Cite this review

Pith. "Pith review of InspireDebate: Multi-Dimensional Subjective-Objective Evaluation-Guided Reasoning and Optimization for Debating." pith.science (2026). https://pith.science/paper/QYZMVJT2

@misc{pith2026250618102,
  author       = {Pith},
  title        = {Pith review of: InspireDebate: Multi-Dimensional Subjective-Objective Evaluation-Guided Reasoning and Optimization for Debating},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QYZMVJT2}},
  note         = {Machine review of arXiv:2506.18102}
}
abstract

With the rapid advancements in large language models (LLMs), debating tasks, such as argument quality assessment and debate process simulation, have made significant progress. However, existing LLM-based debating systems focus on responding to specific arguments while neglecting objective assessments such as authenticity and logical validity. Furthermore, these systems lack a structured approach to optimize across various dimensions$-$including evaluation metrics, chain-of-thought (CoT) reasoning, and multi-turn debate refinement$-$thereby limiting their effectiveness. To address these interconnected challenges, we propose a dual-component framework: (1) $\textbf{InspireScore}$, a novel evaluation system that establishes a multi-dimensional assessment architecture incorporating four subjective criteria (emotional appeal, argument clarity, argument arrangement, and topic relevance) alongside two objective metrics (fact authenticity and logical validity); and (2) $\textbf{InspireDebate}$, an optimized debating framework employing a phased optimization approach through CoT reasoning enhancement, multi-dimensional Direct Preference Optimization (DPO), and real-time knowledge grounding via web-based Retrieval Augmented Generation (Web-RAG). Empirical evaluations demonstrate that $\textbf{InspireScore}$ achieves 44$\%$ higher correlation with expert judgments compared to existing methods, while $\textbf{InspireDebate}$ shows significant improvements, outperforming baseline models by 57$\%$. Source code is available at https://github.com/fywang12/InspireDebate.

Figures

Figures reproduced from arXiv: 2506.18102 by the authors.

Figure 1
Figure 1. Model Performance under InspireScore Evaluation. Radar chart showing scores across four subjective (emotional appeal, argument clarity, argu￾ment arrangement, topic relevance) and two objective (logical validity, fact authenticity) dimensions. ment (Deshpande et al., 2024) and debate process simulation (Zhang et al., 2024). However, sys￾tematic evaluation and optimization of structured debate processes remain undere… view at source ↗
Figure 2
Figure 2. A Unified Framework for Debating Evaluation and Optimization. (1) InspireScore: Presents the evaluation structure, with subjective dimensions (emotional appeal, argument clarity, argument arrangement, topic relevance) and objective dimensions (logical validity, fact authenticity). (2)InspireDebate: Describes the two-stage optimization process: SFT, using GPT-4o-generated instruction data, and DPO, where InspireScore… view at source ↗
Figure 3
Figure 3. Example of Debate Process [PITH_FULL_IMAGE:figures/full_fig_p020_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

46 extracted references · 43 canonical work pages

  1. [1]

    This structured representation allows for precise and systematic evaluation of logical relationships (Table 12)

    Logical Formalization: The debate response is first converted into first-order logic (FOL) expres- sions, where reasoning steps and arguments are mapped to formal predicates and logical operators. This structured representation allows for precise and systematic evaluation of logical relationships (Table 12)

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    Logical Inference and Validity Evaluation: The formalized logic is then processed through inference rules to verify whether conclusions logi- cally follow from the premises. The system applies logical operations such as Modus Ponens and Con- junction to determine whether each conclusion is true, false, or unknown, ensuring logical soundness and consistenc...

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    InACL 2024 (Findings), page 16160–16176

    Can llms speak for diverse people? tuning llms via debate to generate controllable controversial state- ments. InACL 2024 (Findings), page 16160–16176. Shilong Li, Yancheng He, Hui Huang, Xingyuan Bu, Jiaheng Liu, Hangyu Guo, Weixun Wang, Jihao Gu, Wenbo Su, and Bo Zheng. 2025b. 2d-dpo: Scaling direct preference optimization with 2-dimensional supervision...

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    On Robustness and Fragility

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    InACL 2024 (Demo)

    Llamafactory: Unified efficient fine-tuning of 100+ language models. InACL 2024 (Demo). A Evaluation System A.1 Subjective Evaluation Prompt Together, these sub-dimensions—emotional appeal, argument clarity, argument arrangement, and topic relevance—form a structured and comprehensive framework for subjectively evaluating debate per- formance. By assessin...

    work page 2024

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    The natural language format premises should be prioritized when there is inconsistent between natural language and first-order logic

    Make sure you not only access the premises in first-order logic, but also access its corresponding natural language format. The natural language format premises should be prioritized when there is inconsistent between natural language and first-order logic

    work page

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    Combine Premise 1 and Step 5

    When inferring new knowledge, please clear indicate which premises you used or the steps you refer to. For instance, if you use Premise 1 and a knowledge from Step 5, you should clearly indicate that "Combine Premise 1 and Step 5"

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    •0: No emotional appeal

    Emotional Appeal:Evaluates whether the argument evokes a sense of approval or emotional resonance in the audience or judges, enhancing its persuasiveness. •0: No emotional appeal. The argument feels cold or disconnected. •1: Highly engaging emotionally, strongly connects with the audience

    work page

Show all 46 references
  1. [9]

    •0: The arguments are unclear or confusing

    Argument Clarity:Assesses whether the argument is expressed in a way that is clear, concise, and easy for the audience or judges to understand. •0: The arguments are unclear or confusing. •1: The arguments are well-structured and easy to understand

  2. [10]

    •0: The arguments are disorganized and difficult to follow

    Argument Arrangement:Evaluates whether the order and structure of the argument contribute to the presentation of the viewpoints. •0: The arguments are disorganized and difficult to follow. •1: The arguments follow a clear and logical progression

  3. [11]

    •0: Arguments that stray far from the topic

    Relevance to Debate Topic:Determines whether the argument directly aligns with and addresses the debate topic, ensuring its pertinence. •0: Arguments that stray far from the topic. •1: Every argument is focused and relevant to the topic. After scoring each side on all four dim...

  4. [12]

    An atomic fact is a single, standalone statement containing one idea or piece of information

  5. [13]

    Each atomic fact should capture a distinct piece of information and avoid overlaps

  6. [14]

    For the reasoning process, break down each statement into separate facts labeled sequentially

  7. [15]

    For the argument, break down each reason provided into atomic facts labeled sequentially

  8. [16]

    true”, “false

    Provide the output in JSON format as follows: { 'fact-1':'X', 'fact-2':'X', ..., 'fact-<n>':'X' } Ensure sequential numbering is consistent across reasoning and argument sections. User Prompt: Topic:<topic> Reasoning Process and Argument:<debate_text> Break the reasoning proce...

    2000

  9. [17]

    true" if it necessarily follows from the given premises using logical rules. False:A statement is

    Please clearly indicate whether the conclusion statement is true, false or unknown using curly bracket true/false/unknown!!! The answer will only be either true, false or unknown. The definition of the three options are: True:A statement is "true" if it necessarily follows fro...

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    love the most

    Make sure you must only use the premises to infer the conclusion. Do not use any information that is not exist or cannot be inferred from the premises.If some premise is semantically equal, such as "love the most" and "favorite", you can consider this as a valid assumption. Yo...

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    You need to clearly indicate what logic rules and formula you used

    Make sure you abide the first-order logic rules and formula when making logical inference. You need to clearly indicate what logic rules and formula you used

  12. [20]

    And if there is premise contradicts the if statement, you need to use the premise in the if statement as priority and neglect the contradicted one

    Please note that in first-order logic if there exists a conditional statement in the conclusion such as "If...", the if part will be considered as a premise. And if there is premise contradicts the if statement, you need to use the premise in the if statement as priority and n...

  13. [21]

    Be careful with the parentheses. Make sure you following the rules such as Order of Operations (The order is usually: negation ( ¬), conjunction (and, ∧), disjunction (or, ∨), implication ( → ), and biconditional (↔). ), Nested Parentheses (The expression inside the innermost ...

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    Please also indicate the premises and steps you refer to when making the logical process

    You should also use natural language to explain the logical process in each step. Please also indicate the premises and steps you refer to when making the logical process. User Prompt: Input:<first-order logic expressions> Task:Evaluate whether each conclusion logically follow...

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    Health Concerns: Junk food consumption leads to obesity and other health issues among students

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    School Responsibility: Schools are responsible for ensuring students’ well-being and promoting healthy habits

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    Argument: Schools should ban junk food because it contributes to health issues, and schools have a duty to promote student well-being

    Policy Effectiveness: A junk food ban can help mitigate health risks and encourage better dietary choices. Argument: Schools should ban junk food because it contributes to health issues, and schools have a duty to promote student well-being. Output:

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    • CausesHealthIssues(x) ::: x causes health issues

    Convert Reasoning and Argument to Logic Expression Predicates: • JunkFood(x) ::: x is junk food. • CausesHealthIssues(x) ::: x causes health issues. • ShouldBan(x) ::: x should be banned. • SchoolResponsibility(x) ::: x is a school’s responsibility. Premises:

  19. [29]

    JunkFood(x)→CausesHealthIssues(x) ::: Junk food causes health issues

  20. [30]

    CausesHealthIssues(x)→ShouldBan(x) ::: If something causes health issues, it should be banned

  21. [31]

    Conclusions:

    SchoolResponsibility(PromoteHealth) ::: Schools are responsible for promoting health. Conclusions:

  22. [32]

    (JunkFood(x) V CausesHealthIssues(x))→ShouldBan(x) ::: Junk food should be banned because it causes health issues

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    SchoolResponsibility(PromoteHealth) → ShouldBan(JunkFood) ::: Schools should ban junk food as part of their responsibility to promote health

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    Generate Inference Plan Plan:

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    Identify the Goal: Assess whether each conclusion follows logically from the premises

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    Utilize Logical Inference Rules: Apply Modus Ponens and Conjunction to validate logical consistency

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    • Conclusion 2: Verify if schools’ responsibility to promote health logically leads to banning junk food

    Analyze Each Conclusion: • Conclusion 1: Check if junk food causes health issues, and if that justifies a ban. • Conclusion 2: Verify if schools’ responsibility to promote health logically leads to banning junk food

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    • Use Premise 3 to reinforce that schools have a duty to take action

    Apply Premises to Conclusions: • Use Premises 1 and 2 to confirm junk food should be banned due to health concerns. • Use Premise 3 to reinforce that schools have a duty to take action

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    Evaluate Logical Connections: Ensure logical validity and eliminate inconsistencies

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    Combine the Steps: Construct a reasoned argument based on the premises and logical rules

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    Conclude: Assign truth values to each conclusion

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    • Conclusion 2: true (Supported by Premise 3, reinforcing schools’ responsibility)

    Evaluate Conclusions • Conclusion 1: true (Follows logically from Premises 1 and 2). • Conclusion 2: true (Supported by Premise 3, reinforcing schools’ responsibility). Table 14: Example of Logical Validity Assessment System player prompt: You are a debater. Hello and welcome ...

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    Generate exactly 1-3 keywords or phrases

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    Each keyword should be specific and searchable

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    Keywords should be concise (2-4 words each)

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    keyword1

    Avoid overly broad or vague terms Output Format: Return a JSON array containing exactly 1-3 keywords, like this: ["keyword1", "keyword2", "keyword3"] User prompt: Debate Topic:[Debate Topic] Position:[Position] Opponent’s Argument:[Opponent’s Argument [optional]] Please genera...

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    InACL 2023, page 13916–13931

    Arganalysis35k: A large-scale dataset for argument quality analysis. InACL 2023, page 13916–13931. Anne Lauscher, Lily Ng, Courtney Napoles, and Joel R. Tetreault. 2020. Rhetoric, logic, and dialectic: Ad- vancing theory-based argument quality assessment in natural language pr...

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    Omkar Joshi, Priya Pitre, and Yashodhara Haribhakta

    Openai o1 system card.arXiv preprint arXiv:2412.16720. Omkar Joshi, Priya Pitre, and Yashodhara Haribhakta

    arXiv

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