arxiv: 2605.02170 · v1 · submitted 2026-05-04 · 💻 cs.CL · cs.LG

Recognition: unknown

CLaC at SemEval-2026 Task 6: Response Clarity Detection in Political Discourse

Nawar Turk , Lucas Miquet-Westphal , Leila Kosseim

Authors on Pith no claims yet

Pith reviewed 2026-05-09 16:37 UTC · model grok-4.3

classification 💻 cs.CL cs.LG

keywords response clarity detectionpolitical discourseLLM promptingtransformer fine-tuningevasion detectionensemble modelspresidential interviewsSemEval task

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The pith

Prompt-based large language models without fine-tuning outperform fine-tuned transformer encoders on detecting clarity and evasion in political interview responses.

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

This paper presents a system for the SemEval-2026 task of classifying responses in U.S. presidential interviews as clear, ambivalent, or evasive, and compares fine-tuned encoder models against prompt-based large language models. The authors find that an ensemble of LLMs using only prompts and no parameter updates reaches strong results on both the three-class and nine-class versions of the task. Across eight transformer encoders run through a four-stage optimization pipeline, partial layer unfreezing produces better results than full fine-tuning, and mixing English and multilingual encoders improves the ensemble even though multilingual models are weaker on their own. Prompt-only LLMs prove especially strong on minority classes, while enriched inputs that include the full interviewer turn help LLMs but not encoders. Readers would care because the approach avoids the cost of large-scale retraining while still handling the ambiguous language common in political discourse.

Core claim

The central claim is that prompt-based LLMs without any task-specific parameter updates outperform fine-tuned transformer encoders on response clarity and evasion detection in question-answer pairs from U.S. presidential interviews. The LLM ensemble reaches 80 macro-F1 on the three-class task and 59 macro-F1 on the nine-class task. Partial unfreezing of encoder layers outperforms full fine-tuning across eight models, and ensembles that combine English and multilingual encoders improve results over either group alone. Prompt-based LLMs show particular strength on minority classes, and parameter count does not predict performance among open-weight models. Enriched inputs help LLMs but not fine

What carries the argument

The prompt-based LLM ensemble, which performs classification through carefully designed prompts and structured outputs on frozen large language models without any parameter updates for the task.

If this is right

Partial unfreezing of layers in transformer encoders yields higher performance than full fine-tuning for this type of political discourse classification.
Combining English and multilingual encoders in an ensemble improves results over using either family alone, despite individual multilingual models being weaker.
Prompt-based LLMs without parameter updates handle minority classes more effectively than fine-tuned encoders.
Enriched inputs that include the full interviewer turn improve LLM performance but provide no benefit to encoder models, even when using extended context windows.
The dominant error remains the boundary between clear and ambivalent replies, which matches the main area of disagreement among human annotators.

Where Pith is reading between the lines

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

If prompting without updates continues to work well, the method could reduce reliance on large labeled datasets for other subjective classification tasks involving political or social language.
The absence of a clear link between model size and performance among open LLMs points toward prioritizing prompt design and model selection over simply scaling parameter count for similar applications.
The fact that enriched context helps only LLMs suggests that future experiments could isolate whether this advantage comes from differences in how prompted models integrate additional information compared with fine-tuned ones.
Applying the same pipeline to non-political interview or debate datasets would test whether the prompting advantage holds in other domains that contain comparable ambiguity.

Load-bearing premise

The human annotations for the three-class and nine-class clarity and evasion labels are consistent enough to serve as reliable ground truth, and the test set distribution matches real political interview data.

What would settle it

A new annotation round on the same interview transcripts that produces labels with low agreement to the original ones, or a fresh test set drawn from actual presidential interviews where the reported macro-F1 scores fall substantially below the published numbers.

Figures

Figures reproduced from arXiv: 2605.02170 by Leila Kosseim, Lucas Miquet-Westphal, Nawar Turk.

**Figure 1.** Figure 1: Label distributions across Clarity (top) and view at source ↗

**Figure 3.** Figure 3: Impact of prompting strategy, input configu view at source ↗

**Figure 4.** Figure 4: Mean dev macro-F1 per design choice for the view at source ↗

**Figure 5.** Figure 5: Encoder ensemble confusion matrix (dev set). view at source ↗

**Figure 6.** Figure 6: LLM ensemble confusion matrix (dev set). view at source ↗

read the original abstract

In this paper, we present our system for SemEval-2026 Task 6 (CLARITY) on response clarity and evasion detection in question-answer pairs from U.S. presidential interviews, comparing fine-tuned encoders with prompt-based LLMs. Our LLM ensemble achieves 80 macro-F1 on the 3-class Task 1 (9th/41) and 59 on the 9-class Task 2 (3rd/33). Across 8 transformer encoders optimized through a four-stage pipeline, partial encoder layer unfreezing outperforms full fine-tuning by a wide margin. Combining English and multilingual encoders further improves ensemble performance over either family alone, despite multilingual models being individually weaker. Prompt-based LLMs, without any task-specific parameter updates, outperform fine-tuned encoders, particularly on minority classes; among open-weight LLMs, parameter count does not predict performance. Enriched input, concatenating the full interviewer turn, improves LLM performance but not that of encoders, an effect that persists with Longformer's extended context window, suggesting the divergence is not attributable to sequence-length capacity alone in our settings. The Clear Reply/Ambivalent boundary remains the dominant failure mode, mirroring the disagreement among human annotators. Our code, prompts, model configurations, and results are publicly available.

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

This is a competent shared-task system paper with some practical observations on LLMs versus encoders, but the lack of annotation agreement numbers makes the performance claims harder to trust.

read the letter

The main takeaway is that the CLaC team built a competitive entry for the new SemEval-2026 clarity detection task on political interviews. Their LLM ensemble hits 80 macro-F1 on the 3-class version and 59 on the 9-class one, with solid rankings, and they report that partial layer unfreezing beats full fine-tuning for encoders while adding the full interviewer turn helps only the LLMs. Prompt-based LLMs also beat the fine-tuned encoders, especially on minority classes, and parameter count does not track performance among open models.

Referee Report

2 major / 1 minor

Summary. The manuscript presents the CLaC system for SemEval-2026 Task 6 (CLARITY) on detecting response clarity and evasion in U.S. presidential interview QA pairs. It compares fine-tuned transformer encoders optimized via a four-stage pipeline against prompt-based LLMs, reporting that an LLM ensemble reaches 80 macro-F1 on the 3-class Task 1 (9th/41) and 59 on the 9-class Task 2 (3rd/33). Key findings include partial layer unfreezing outperforming full fine-tuning for encoders, LLM ensembles beating individual encoders (especially on minority classes), enriched input (full interviewer turn) helping LLMs but not encoders, and the Clear Reply/Ambivalent boundary as the dominant failure mode that mirrors human annotator disagreement. Code, prompts, configurations, and results are released publicly.

Significance. If the empirical comparisons hold, the work provides useful shared-task insights into modeling choices for clarity detection, such as the advantage of partial unfreezing over full fine-tuning and the utility of LLM ensembles without parameter updates for minority classes. The public release of code and prompts is a clear strength that supports reproducibility and allows others to replicate the four-stage pipeline and input-enrichment experiments.

major comments (2)

[Abstract] Abstract: The claims that partial encoder layer unfreezing 'outperforms full fine-tuning by a wide margin' and that prompt-based LLMs 'outperform fine-tuned encoders, particularly on minority classes' are presented without error bars, statistical significance tests, or detailed ablation tables. These omissions make it impossible to assess whether the reported margins and rankings reflect genuine differences or could arise from label noise or variance.
[Abstract] Abstract: Although the text states that the Clear Reply/Ambivalent boundary 'mirrors the disagreement among human annotators,' no inter-annotator agreement statistics, label distribution breakdown, or external validation of the test-set distribution are provided. Because all F1 scores, method comparisons (LLM vs. encoder, enriched input effects), and rankings presuppose reliable ground truth, this gap directly limits interpretation of the central empirical results.

minor comments (1)

[Abstract] Abstract: The statement 'Across 8 transformer encoders' would benefit from an accompanying table or list identifying the specific models and their individual scores to clarify the ensemble construction and the 'combining English and multilingual encoders' result.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments highlighting the need for greater statistical rigor and transparency on data characteristics. We address each point below and will revise the manuscript accordingly to strengthen the presentation of our empirical findings.

read point-by-point responses

Referee: [Abstract] Abstract: The claims that partial encoder layer unfreezing 'outperforms full fine-tuning by a wide margin' and that prompt-based LLMs 'outperform fine-tuned encoders, particularly on minority classes' are presented without error bars, statistical significance tests, or detailed ablation tables. These omissions make it impossible to assess whether the reported margins and rankings reflect genuine differences or could arise from label noise or variance.

Authors: We agree that the absence of variability measures and significance testing limits the strength of these claims. In the revised manuscript we will report standard deviations from at least three independent runs with different random seeds for all encoder experiments (including the partial-unfreezing vs. full fine-tuning comparison), add McNemar’s tests or approximate randomization tests for the key pairwise differences, and include a full ablation table in the appendix showing per-class F1 and macro-F1 for each stage of the four-stage pipeline. For the LLM ensemble we will report results across two prompt variants to quantify prompt sensitivity. revision: yes
Referee: [Abstract] Abstract: Although the text states that the Clear Reply/Ambivalent boundary 'mirrors the disagreement among human annotators,' no inter-annotator agreement statistics, label distribution breakdown, or external validation of the test-set distribution are provided. Because all F1 scores, method comparisons (LLM vs. encoder, enriched input effects), and rankings presuppose reliable ground truth, this gap directly limits interpretation of the central empirical results.

Authors: We will add the label distribution tables for both the 3-class and 9-class tasks (train/dev/test splits) to the revised paper. The SemEval-2026 task description paper reports inter-annotator agreement (Cohen’s κ) for the annotation process; we will cite these figures explicitly and note that the dominant Clear-Reply/Ambivalent confusion we observe is consistent with the annotator disagreement patterns described there. Because the test set is single-annotated and held out by the organizers, we cannot compute new IAA on the test portion, but we will discuss this limitation and compare our system’s error patterns against the provided development-set disagreement statistics. revision: partial

Circularity Check

0 steps flagged

No circularity: purely empirical benchmark reporting

full rationale

The paper contains no derivations, equations, or first-principles claims. All reported results are direct empirical measurements (macro-F1 scores, comparisons of fine-tuning strategies, LLM vs. encoder performance) obtained by running models on the provided SemEval dataset and labels. No fitted parameters are renamed as predictions, no self-citations bear the load of central claims, and no ansatz or uniqueness theorem is invoked. The work is self-contained empirical reporting whose validity depends on external factors such as label quality, not on any internal reduction to its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an empirical NLP competition paper with no mathematical model, no free parameters fitted to data, no background axioms invoked, and no new postulated entities.

pith-pipeline@v0.9.0 · 5536 in / 1030 out tokens · 33455 ms · 2026-05-09T16:37:40.245768+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

18 extracted references · 12 canonical work pages · 5 internal anchors

[3]

Bull, Peter , title =
[7]

Kojima, Takeshi and Gu, Shixiang (Shane) and Reid, Machel and Matsuo, Yutaka and Iwasawa, Yusuke , booktitle =
[14]

2023 , url=

Pengcheng He and Jianfeng Gao and Weizhu Chen , booktitle=. 2023 , url=

2023
[16]

Akhiad Bercovich, Itay Levy, Izik Golan, Mohammad Dabbah, and 132 others. 2025. https://arxiv.org/abs/2505.00949 Llama-Nemotron: Efficient Reasoning Models . Preprint, arXiv:2505.00949

work page arXiv 2025
[17]

Peter Bull. 2003. The Microanalysis of Political Communication: Claptrap and Ambiguity. Routledge

2003
[18]

Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzm \'a n, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020. https://doi.org/10.18653/v1/2020.acl-main.747 Unsupervised Cross-lingual Representation Learning at Scale . In Proceedings of the 58th Annual Meeting of the Association for Comp...

work page doi:10.18653/v1/2020.acl-main.747 2020
[19]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. https://doi.org/10.18653/v1/N19-1423 BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding . In Proceedings of the 2019 Conference of the North A merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long an...

work page doi:10.18653/v1/n19-1423 2019
[20]

Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, and 557 others. 2024. https://arxiv.org/abs/2407.21783 The Llama 3 Herd of Models . Preprint, arXiv:2407.21783

work page internal anchor Pith review Pith/arXiv arXiv 2024
[21]

Pengcheng He, Jianfeng Gao, and Weizhu Chen. 2023. https://openreview.net/forum?id=sE7-XhLxHA De BERT aV3: Improving De BERT a using ELECTRA -Style Pre-Training with Gradient-Disentangled Embedding Sharing . In Proceedings of Eleventh International Conference on Learning Representations

2023
[22]

Takeshi Kojima, Shixiang (Shane) Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022. https://proceedings.neurips.cc/paper_files/paper/2022/file/8bb0d291acd4acf06ef112099c16f326-Paper-Conference.pdf Large Language Models are Zero-Shot Reasoners . In Advances in Neural Information Processing Systems, volume 35, pages 22199--22213. Curran Associates, Inc

2022
[23]

Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. https://arxiv.org/abs/1907.11692 RoBERTa: A Robustly Optimized BERT Pretraining Approach . Preprint, arXiv:1907.11692

work page internal anchor Pith review Pith/arXiv arXiv 2019
[24]

Parameswary Rasiah. 2010. https://doi.org/10.1016/j.pragma.2009.07.010 A Framework for the Systematic Analysis of Evasion in Parliamentary Discourse . Journal of Pragmatics, 42(3):664--680

work page doi:10.1016/j.pragma.2009.07.010 2010
[25]

Sander Schulhoff, Michael Ilie, Nishant Balepur, Konstantine Kahadze, and 27 others. 2025. https://arxiv.org/abs/2406.06608 The Prompt Report: A Systematic Survey of Prompt Engineering Techniques . Preprint, arXiv:2406.06608

work page internal anchor Pith review arXiv 2025
[26]

Aaditya Singh, Adam Fry, Adam Perelman, Adam Tart, and 480 others. 2025. https://arxiv.org/abs/2601.03267 OpenAI GPT-5 System Card . Preprint, arXiv:2601.03267

work page internal anchor Pith review Pith/arXiv arXiv 2025
[27]

Konstantinos Thomas, Giorgos Filandrianos, Maria Lymperaiou, Chrysoula Zerva, and Giorgos Stamou. 2024. https://doi.org/10.18653/v1/2024.findings-emnlp.300 ``I Never Said That'': A Dataset, Taxonomy and Baselines on Response Clarity Classification . In Findings of the Association for Computational Linguistics (EMNLP 2024), pages 5204--5233, Miami, Florida...

work page doi:10.18653/v1/2024.findings-emnlp.300 2024
[28]

Konstantinos Thomas, Giorgos Filandrianos, Maria Lymperaiou, Chrysoula Zerva, and Giorgos Stamou. 2026. https://arxiv.org/abs/2603.14027 SemEval-2026 Task 6: CLARITY -- Unmasking Political Question Evasions . Preprint, arXiv:2603.14027

work page arXiv 2026
[29]

Xiaohan Xu, Chongyang Tao, Tao Shen, Can Xu, Hongbo Xu, Guodong Long, Jian-Guang Lou, and Shuai Ma. 2024. https://doi.org/10.18653/v1/2024.emnlp-main.871 Re-Reading Improves Reasoning in Large Language Models . In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024), pages 15549--15575, Miami, Florida, USA. A...

work page doi:10.18653/v1/2024.emnlp-main.871 2024
[30]

An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, and 56 others. 2025. https://arxiv.org/abs/2505.09388 Qwen3 Technical Report . Preprint, arXiv:2505.09388

work page internal anchor Pith review Pith/arXiv arXiv 2025