TRACE: Toulmin-based Reasoning Assessment through Constructive Elements for LLM CoT Evaluation

Heyoung Yang; Yundong Kim

arxiv: 2605.29656 · v1 · pith:E2EXS5LAnew · submitted 2026-05-28 · 💻 cs.AI

TRACE: Toulmin-based Reasoning Assessment through Constructive Elements for LLM CoT Evaluation

Yundong Kim , Heyoung Yang This is my paper

Pith reviewed 2026-06-29 07:16 UTC · model grok-4.3

classification 💻 cs.AI

keywords LLM evaluationChain-of-Thoughtreasoning assessmentToulmin argumentationmetacognitionreinforcement learning rewardCoT evaluationargument structure

0 comments

The pith

TRACE evaluates LLM Chain-of-Thought reasoning structure via Toulmin elements and metacognition, correlating r=0.74 with answer accuracy.

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

The paper presents TRACE as a metric that examines the internal construction of reasoning steps in large language models instead of relying only on whether the final answer is correct. It combines Toulmin's model of how arguments are built with Flavell's ideas on monitoring one's own thinking to score the quality of Chain-of-Thought outputs. Across 26.3K question-answering samples from seven models, the resulting scores line up closely with standard benchmark accuracy. When the same scores are used as a reward during reinforcement learning, performance improves over training that uses accuracy alone. This supports the view that well-structured reasoning tends to produce better final results and offers a new way to judge open-ended model responses.

Core claim

TRACE integrates Toulmin's argumentation theory with Flavell's metacognitive framework to assess reasoning structure in CoT. Experiments on 26.3K QA samples across 7 reasoning models show strong correlation with benchmark accuracy (r=0.74). Furthermore, TRACE is effective as a reinforcement learning reward signal, outperforming accuracy-only baselines. Together, these results indicate that logically sound reasoning leads to higher-quality answers.

What carries the argument

TRACE metric that scores constructive elements of arguments by combining Toulmin's six components (claim, data, warrant, backing, qualifier, rebuttal) with metacognitive monitoring and evaluation steps.

If this is right

Logically sound reasoning processes lead to higher-quality answers.
TRACE can function as an effective reinforcement learning reward signal that improves model performance beyond accuracy-only training.
Reasoning evaluation for open-ended LLM outputs can shift focus from outcomes to argument structure.
A complementary metric exists alongside accuracy for judging LLM capabilities on reasoning tasks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The method could be adapted to assess reasoning in domains beyond QA, such as code generation or multi-step planning.
Training objectives might be redesigned to explicitly encourage construction of Toulmin-style argument components.
If structural scores drive accuracy, similar frameworks could help diagnose and reduce specific failure modes like unsupported claims.
Educational applications might use the same scoring to give feedback on student reasoning chains.

Load-bearing premise

The specific integration of Toulmin's argumentation elements and Flavell's metacognition provides a valid, generalizable measure of reasoning quality that is independent of and predictive of final-answer correctness rather than merely correlated with it.

What would settle it

A new set of models or tasks where high TRACE scores consistently appear with low final-answer accuracy, or where reinforcement learning using TRACE rewards fails to outperform accuracy-based rewards.

Figures

Figures reproduced from arXiv: 2605.29656 by Heyoung Yang, Yundong Kim.

**Figure 1.** Figure 1: Transition heatmaps comparing Kimi-K2-Thinking and Qwen-Turbo. Blue-bordered cells denote Good Transitions (e.g., Evidence → Claim); Red-bordered cells denote Bad Transitions (e.g., Monitoring → Qualifier). quality becomes increasingly challenging. Current evaluation paradigms focus on outcome-based metrics (e.g., accuracy, exact match), which assess the final answer but treat the reasoning process as a … view at source ↗

**Figure 2.** Figure 2: Architecture of TRACE-DeBERTa. The model encodes an input reasoning sentence using DeBERTa-v3-base. The [CLS] representation is projected to an 8-dimensional confidence vector via a linear layer and Sigmoid activation, enabling multi-label classification of constructive elements. 3.1. TRACE-DeBERTa for Sentence Attributes Labeling Model Selection We select DeBERTa-v3-base (He et al., 2023) as our backbone.… view at source ↗

**Figure 3.** Figure 3: Overview of the TRACE Pipeline. The framework operates in two main phases: (Top) Making Label Train from Reasoning Block, where the raw reasoning text is decomposed and multi-labeled by TRACE-DeBERTa (described in [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 4.** Figure 4: Scatter plot of benchmark accuracy versus mean TRACE score across all model-benchmark pairs (n = 273). surface-level metrics such as Token Length (r = −0.147), Perplexity (r = +0.221) and MTLD (r = −0.207). While extended reasoning typically correlates with improved performance within a controlled setting (i.e., a fixed model and task), raw token length becomes unreliable when comparing across heterogeneo… view at source ↗

**Figure 5.** Figure 5: TRACE score distributions for correct vs. incorrect answers by domain. Domain Analysis To investigate how reasoning structure impacts performance across different fields, we clustered the 39 benchmarks into 6 categories: Math & Logic, CS & Engineering, Natural Sciences, Medicine & Health, Biz/Econ/Law, and Humanities & Social Sci. The detailed mapping is provided in Appendix A.4. We generated violin plot… view at source ↗

**Figure 6.** Figure 6: illustrates the proportion of constructive elements, derived from 3.9K blocks per model in Section 4.1. We observe that the ratios of these elements within reasoning blocks vary significantly across models. 0.0 0.1 0.2 0.3 0.4 Ratio Claim Data/Evidence Warrant Backing Evaluation Qualifier Rebuttal Monitoring 17.2% 30.0% 13.4% 14.0% 15.6% 7.2% 9.9% 13.9% Claude-3.7-Sonnet 0.0 0.1 0.2 0.3 0.4 Ratio Claim Dat… view at source ↗

**Figure 7.** Figure 7: Effect of α on Pearson correlation with benchmark accuracy and prediction accuracy on Arena Hard v2.0 (Math). The shaded region indicates near-optimal performance. C.2. Statistical Significance For the selected hyperparameter (α = 0.7), we report the correlation coefficients with 95% confidence intervals computed via Fisher’s z-transformation [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗

read the original abstract

Evaluating open-ended outputs from large language models (LLMs) remains challenging due to the absence of ground truth. Existing metrics rely on final-answer accuracy or surface-level statistics, leaving the reasoning process itself unexamined. We introduce TRACE (Toulmin-based Reasoning Assessment through Constructive Elements), a metric that analyzes Chain-of-Thought (CoT) reasoning processes. Rather than judging outcomes, TRACE inspects how arguments are constructed by integrating Toulmin's argumentation theory with Flavell's metacognitive framework to assess reasoning structure. Experiments on 26.3K QA samples across 7 reasoning models show strong correlation with benchmark accuracy (r=0.74). Furthermore, TRACE is effective as a reinforcement learning reward signal, outperforming accuracy-only baselines. Together, these results indicate that logically sound reasoning leads to higher-quality answers. TRACE thus serves as a complementary metric for evaluating open-ended outputs. Code is available at https://github.com/hyyangkisti/trace.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

TRACE defines a new Toulmin-plus-metacognition score for CoT structure that correlates with accuracy and beats accuracy baselines in RL, but the reported results do not establish that the score captures an independent causal factor.

read the letter

The paper's core move is to score Chain-of-Thought outputs by breaking them into Toulmin argument elements and Flavell metacognitive markers, then aggregate that into the TRACE metric. On 26.3k QA samples from seven models it reports r=0.74 with final-answer accuracy and shows that using TRACE as an RL reward improves over accuracy-only baselines. Code is released.

That combination and the scale of the test set are the actual new pieces. Most prior work either stays at surface statistics or outcome accuracy; applying these two established frameworks at this volume of data is a concrete step.

The soft spot is exactly the one the stress-test note flags. Correlation plus an RL win does not show that the structure score measures something separate from model capability or prompt difficulty. No controls are described that hold final-answer correctness fixed while varying structure, or that regress out surface features. Without those, the inference that "logically sound reasoning leads to higher-quality answers" stays correlational. The abstract also gives no scoring rules or construction details, so circularity cannot be ruled out from what is shown.

If the full paper supplies the missing controls and a transparent metric definition, the work strengthens considerably. As presented, the experiments demonstrate association and practical utility as a reward signal, but not independence.

This is for groups building process-based evaluators or reward models for LLMs. Readers already working on argumentation or metacognition in AI will find the framing useful even if they want tighter validation.

It deserves peer review. The idea is worth testing and the data volume is large enough to discuss; referees can ask for the controls and construction details that would make the claims tighter.

Referee Report

3 major / 2 minor

Summary. The paper introduces TRACE, a metric that integrates Toulmin's argumentation theory with Flavell's metacognitive framework to evaluate the structure of Chain-of-Thought (CoT) reasoning in LLMs rather than final-answer accuracy. On 26.3K QA samples across 7 reasoning models, it reports a correlation of r=0.74 with benchmark accuracy and demonstrates that TRACE serves as an effective RL reward signal, outperforming accuracy-only baselines. The authors conclude that logically sound reasoning leads to higher-quality answers and position TRACE as a complementary metric for open-ended LLM outputs.

Significance. If the metric can be shown to capture an independent dimension of reasoning quality, it would address a genuine gap in LLM evaluation by moving beyond outcome-based metrics; the RL result, if robust, would further suggest practical utility for training.

major comments (3)

[Abstract] Abstract: the inference that 'logically sound reasoning leads to higher-quality answers' rests on the r=0.74 correlation and the RL result, yet the abstract supplies no controls (e.g., regressing out model size, prompt difficulty, or final-answer correctness) to establish that TRACE measures an independent causal factor rather than a downstream correlate.
[Abstract] Abstract and §3 (metric definition): without explicit scoring rules for the Toulmin elements and Flavell metacognitive components, or any demonstration that the metric construction excludes accuracy signals, it is impossible to rule out circularity between TRACE and the benchmark accuracy it is correlated with.
[RL experiments] RL experiments section: the claim that TRACE outperforms accuracy-only baselines requires the precise reward formulation, training details, and ablation controls; absent these, the result does not yet secure that the structured-reasoning signal is the operative factor.

minor comments (2)

[Abstract] The abstract states '26.3K QA samples' but does not name the underlying datasets or the seven models; this information should appear in the experimental setup.
Notation for the TRACE score components is not introduced in the abstract; a compact definition or table of the six Toulmin/Flavell elements would improve readability.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting areas where the abstract and experimental details can be clarified. We address each major comment below, indicating revisions where appropriate to strengthen the presentation without overstating the results.

read point-by-point responses

Referee: [Abstract] Abstract: the inference that 'logically sound reasoning leads to higher-quality answers' rests on the r=0.74 correlation and the RL result, yet the abstract supplies no controls (e.g., regressing out model size, prompt difficulty, or final-answer correctness) to establish that TRACE measures an independent causal factor rather than a downstream correlate.

Authors: We agree the abstract phrasing could be read as implying a causal claim stronger than the reported evidence. The r=0.74 reflects a correlation across 7 models and 26.3K samples, and the RL result demonstrates practical utility rather than controlled isolation of an independent factor. In revision we will change the abstract wording from 'indicate that' to 'suggest that' and add a brief clause noting that controlled analyses for confounders such as model scale remain future work. No new experiments are added, but the language will be tempered accordingly. revision: partial
Referee: [Abstract] Abstract and §3 (metric definition): without explicit scoring rules for the Toulmin elements and Flavell metacognitive components, or any demonstration that the metric construction excludes accuracy signals, it is impossible to rule out circularity between TRACE and the benchmark accuracy it is correlated with.

Authors: Section 3 defines TRACE via explicit rubrics on Toulmin components (claim, data, warrant, backing, qualifier, rebuttal) and Flavell metacognitive elements (planning, monitoring, evaluation), scored on structural presence, completeness, and coherence within the CoT text. Scoring is performed without reference to final-answer correctness. We will expand §3 in the revision to include the full rubric table, annotation guidelines, and two worked examples showing cases where high TRACE coincides with incorrect answers (and vice versa). This makes the independence from accuracy explicit and addresses potential circularity concerns. revision: yes
Referee: [RL experiments] RL experiments section: the claim that TRACE outperforms accuracy-only baselines requires the precise reward formulation, training details, and ablation controls; absent these, the result does not yet secure that the structured-reasoning signal is the operative factor.

Authors: We will revise the RL section to report the exact reward formulation (normalized TRACE score used directly as the reward), the RL algorithm and hyperparameters, training steps, environment details, and ablation results comparing TRACE reward against accuracy-only and random baselines. These additions will clarify the contribution of the structured-reasoning component. revision: yes

Circularity Check

0 steps flagged

No significant circularity; metric defined from external theories with correlation as outcome

full rationale

The provided abstract defines TRACE explicitly from Toulmin's argumentation theory integrated with Flavell's metacognitive framework, independent of accuracy. Correlation (r=0.74) and RL reward results are presented as experimental findings on 26.3K samples, not as definitional inputs or fitted parameters. No equations, self-citations, or reductions to self-inputs appear. The inference from correlation to 'logically sound reasoning leads to higher-quality answers' is an interpretive claim, not a circular derivation step. Per rules, absent specific quotes exhibiting construction-by-inputs or load-bearing self-citation chains, score remains 0.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no free parameters, axioms, or invented entities can be identified from the provided text.

pith-pipeline@v0.9.1-grok · 5694 in / 1160 out tokens · 33672 ms · 2026-06-29T07:16:25.357714+00:00 · methodology

discussion (0)

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Odyssey: Constructing Verifiable Local Truth-Preserving Foundation Models
cs.AI 2026-06 unverdicted novelty 3.0

ODYSSEY is a sheaf-theoretic framework for building verifiable foundation models as compositions of foundries via left and right Kan extensions.

Reference graph

Works this paper leans on

10 extracted references · 7 canonical work pages · cited by 1 Pith paper · 3 internal anchors

[1]

org/CorpusID:268232499

URL https://api.semanticscholar. org/CorpusID:268232499. Antoun, W., Sagot, B., and Seddah, D. Modernbert or debertav3? examining architecture and data influence on transformer encoder models performance.arXiv preprint arXiv:2504.08716, 2025. Bai, G., Liu, J., Bu, X., He, Y ., Liu, J., Zhou, Z., Lin, Z., Su, W., Ge, T., Zheng, B., and Ouyang, W. MT-bench-...

work page doi:10.18653/v1/2024.acl-long 2025
[2]

Lessons from the Trenches on Reproducible Evaluation of Language Models

URL https://aclanthology.org/2024. acl-long.401/. Biderman, S., Schoelkopf, H., Sutawika, L., Gao, L., Tow, J., Abbasi, B., Aji, A. F., Ammanamanchi, P. S., Black, S., Clive, J., et al. Lessons from the trenches on repro- ducible evaluation of language models.arXiv preprint arXiv:2405.14782, 2024. Chen, G. H., Chen, S., Liu, Z., Jiang, F., and Wang, B. Hu...

work page internal anchor Pith review Pith/arXiv arXiv doi:10.18653/v1/2024 2024
[3]

acl-main.372/

URL https://aclanthology.org/2020. acl-main.372/. Du, X., Yao, Y ., Ma, K., et al. SuperGPQA: Scaling LLM evaluation across 285 graduate disciplines. In The Thirty-ninth Annual Conference on Neural Informa- tion Processing Systems Datasets and Benchmarks Track,

2020
[4]

Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators

URL https://openreview.net/forum? id=6WgflzYQpf. Dubois, Y ., Galambosi, B., Liang, P., and Hashimoto, T. B. Length-controlled alpacaeval: A simple way to debias automatic evaluators, 2025. URL https://arxiv. org/abs/2404.04475. Flavell, J. H. Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry.American psychologist, 34(1...

work page internal anchor Pith review Pith/arXiv arXiv doi:10.1016/j.xinn.2025.101253 2025
[5]

Process reward models that think.arXiv preprint arXiv:2504.16828,

URL https://openreview.net/forum? id=sE7-XhLxHA. Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J. Measuring massive multitask language understanding. InInternational Conference on Learning Representations, 2021. URL https:// openreview.net/forum?id=d7KBjmI3GmQ. Khalifa, M., Agarwal, R., Logeswaran, L., Kim, J., Peng...

work page arXiv 2021
[6]

RoBERTa: A Robustly Optimized BERT Pretraining Approach

URL https://openreview.net/forum? id=4T33izzFpK. Li, T., Chiang, W.-L., Frick, E., Dunlap, L., Wu, T., Zhu, B., Gonzalez, J. E., and Stoica, I. From crowdsourced data to high-quality benchmarks: Arena-hard and bench- builder pipeline. InForty-second International Con- ference on Machine Learning, 2025. URL https: //openreview.net/forum?id=KfTf9vFvSn. Lin,...

work page internal anchor Pith review Pith/arXiv arXiv doi:10.3115/v1/d14-1006 2025
[7]

URL https: //aclanthology.org/2025.acl-long.127/

doi: 10.18653/v1/2025.acl-long.127. URL https: //aclanthology.org/2025.acl-long.127/. Wei, J., Wang, X., Schuurmans, D., Bosma, M., ichter, b., Xia, F., Chi, E., Le, Q. V ., and Zhou, D. Chain-of-thought prompting elicits reasoning in large language models. In Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., and Oh, A. (eds.),Advances in Neura...

work page doi:10.18653/v1/2025.acl-long.127 2025
[8]

Zeng, Z., Chen, P., Liu, S., Jiang, H., and Jia, J

URL https://openreview.net/forum? id=2a36EMSSTp. Zeng, Z., Chen, P., Liu, S., Jiang, H., and Jia, J. MR-GSM8k: A meta-reasoning benchmark for large language model evaluation. InThe Thirteenth International Conference on Learning Representations, 2025. URL https:// openreview.net/forum?id=br4H61LOoI. Zheng, C., Zhang, Z., Zhang, B., Lin, R., Lu, K., Yu, B....

2025
[9]

By the tower law of field extensions, we have:[K:F] = [K:E]·[E:F]

doi: 10.18653/v1/2025.acl-long.50. URL https: //aclanthology.org/2025.acl-long.50/. Zheng, L., Chiang, W.-L., Sheng, Y ., Zhuang, S., Wu, Z., Zhuang, Y ., Lin, Z., Li, Z., Li, D., Xing, E., Zhang, H., Gonzalez, J. E., and Stoica, I. Judging llm-as-a-judge with mt-bench and chatbot arena. In Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., and Le...

work page doi:10.18653/v1/2025.acl-long.50 2025
[10]

The answer is (B), which is correct

:Q] = 2because √ 2is irrational. ”→[Evidence, Warrant] “The answer is (B), which is correct. ”→[Claim, Evaluation] “I think most people would say that lying is wrong. ”→[Claim, Qualifier, Backing] No Label Cases: “Hmm. ”→[ ] “Okay, let’s tackle this question. ”→[ ] “Thank you for listening. ”→[ ] B.2. Allowed States State Validity is computed based on the...

2026

[1] [1]

org/CorpusID:268232499

URL https://api.semanticscholar. org/CorpusID:268232499. Antoun, W., Sagot, B., and Seddah, D. Modernbert or debertav3? examining architecture and data influence on transformer encoder models performance.arXiv preprint arXiv:2504.08716, 2025. Bai, G., Liu, J., Bu, X., He, Y ., Liu, J., Zhou, Z., Lin, Z., Su, W., Ge, T., Zheng, B., and Ouyang, W. MT-bench-...

work page doi:10.18653/v1/2024.acl-long 2025

[2] [2]

Lessons from the Trenches on Reproducible Evaluation of Language Models

URL https://aclanthology.org/2024. acl-long.401/. Biderman, S., Schoelkopf, H., Sutawika, L., Gao, L., Tow, J., Abbasi, B., Aji, A. F., Ammanamanchi, P. S., Black, S., Clive, J., et al. Lessons from the trenches on repro- ducible evaluation of language models.arXiv preprint arXiv:2405.14782, 2024. Chen, G. H., Chen, S., Liu, Z., Jiang, F., and Wang, B. Hu...

work page internal anchor Pith review Pith/arXiv arXiv doi:10.18653/v1/2024 2024

[3] [3]

acl-main.372/

URL https://aclanthology.org/2020. acl-main.372/. Du, X., Yao, Y ., Ma, K., et al. SuperGPQA: Scaling LLM evaluation across 285 graduate disciplines. In The Thirty-ninth Annual Conference on Neural Informa- tion Processing Systems Datasets and Benchmarks Track,

2020

[4] [4]

Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators

URL https://openreview.net/forum? id=6WgflzYQpf. Dubois, Y ., Galambosi, B., Liang, P., and Hashimoto, T. B. Length-controlled alpacaeval: A simple way to debias automatic evaluators, 2025. URL https://arxiv. org/abs/2404.04475. Flavell, J. H. Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry.American psychologist, 34(1...

work page internal anchor Pith review Pith/arXiv arXiv doi:10.1016/j.xinn.2025.101253 2025

[5] [5]

Process reward models that think.arXiv preprint arXiv:2504.16828,

URL https://openreview.net/forum? id=sE7-XhLxHA. Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J. Measuring massive multitask language understanding. InInternational Conference on Learning Representations, 2021. URL https:// openreview.net/forum?id=d7KBjmI3GmQ. Khalifa, M., Agarwal, R., Logeswaran, L., Kim, J., Peng...

work page arXiv 2021

[6] [6]

RoBERTa: A Robustly Optimized BERT Pretraining Approach

URL https://openreview.net/forum? id=4T33izzFpK. Li, T., Chiang, W.-L., Frick, E., Dunlap, L., Wu, T., Zhu, B., Gonzalez, J. E., and Stoica, I. From crowdsourced data to high-quality benchmarks: Arena-hard and bench- builder pipeline. InForty-second International Con- ference on Machine Learning, 2025. URL https: //openreview.net/forum?id=KfTf9vFvSn. Lin,...

work page internal anchor Pith review Pith/arXiv arXiv doi:10.3115/v1/d14-1006 2025

[7] [7]

URL https: //aclanthology.org/2025.acl-long.127/

doi: 10.18653/v1/2025.acl-long.127. URL https: //aclanthology.org/2025.acl-long.127/. Wei, J., Wang, X., Schuurmans, D., Bosma, M., ichter, b., Xia, F., Chi, E., Le, Q. V ., and Zhou, D. Chain-of-thought prompting elicits reasoning in large language models. In Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., and Oh, A. (eds.),Advances in Neura...

work page doi:10.18653/v1/2025.acl-long.127 2025

[8] [8]

Zeng, Z., Chen, P., Liu, S., Jiang, H., and Jia, J

URL https://openreview.net/forum? id=2a36EMSSTp. Zeng, Z., Chen, P., Liu, S., Jiang, H., and Jia, J. MR-GSM8k: A meta-reasoning benchmark for large language model evaluation. InThe Thirteenth International Conference on Learning Representations, 2025. URL https:// openreview.net/forum?id=br4H61LOoI. Zheng, C., Zhang, Z., Zhang, B., Lin, R., Lu, K., Yu, B....

2025

[9] [9]

By the tower law of field extensions, we have:[K:F] = [K:E]·[E:F]

doi: 10.18653/v1/2025.acl-long.50. URL https: //aclanthology.org/2025.acl-long.50/. Zheng, L., Chiang, W.-L., Sheng, Y ., Zhuang, S., Wu, Z., Zhuang, Y ., Lin, Z., Li, Z., Li, D., Xing, E., Zhang, H., Gonzalez, J. E., and Stoica, I. Judging llm-as-a-judge with mt-bench and chatbot arena. In Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., and Le...

work page doi:10.18653/v1/2025.acl-long.50 2025

[10] [10]

The answer is (B), which is correct

:Q] = 2because √ 2is irrational. ”→[Evidence, Warrant] “The answer is (B), which is correct. ”→[Claim, Evaluation] “I think most people would say that lying is wrong. ”→[Claim, Qualifier, Backing] No Label Cases: “Hmm. ”→[ ] “Okay, let’s tackle this question. ”→[ ] “Thank you for listening. ”→[ ] B.2. Allowed States State Validity is computed based on the...

2026