arxiv: 2605.02712 · v1 · submitted 2026-05-04 · 💻 cs.CL · cs.AI

Recognition: 2 theorem links

· Lean Theorem

mdok-style at SemEval-2026 Task 10: Finetuning LLMs for Conspiracy Detection

Dominik Macko

Authors on Pith no claims yet

Pith reviewed 2026-05-08 18:45 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords conspiracy detectiondata augmentationself-traininglarge language modelsfinetuningtext classificationSemEvalReddit comments

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The pith

Data augmentation and self-training let a large language model rank eighth for spotting conspiracy beliefs in Reddit comments

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

The paper demonstrates that data augmentation and self-training, techniques originally used for machine-generated text detection, can be applied to finetune the Qwen3-32B model for binary classification of conspiracy beliefs expressed in Reddit comments. This approach tackles the issue of limited labeled training data in SemEval-2026 Task 10. The resulting system places eighth out of 52 submissions, showing solid competitive performance. A sympathetic reader would care because it offers a concrete way to adapt existing LLM adaptation methods to new problems in analyzing online beliefs without needing enormous amounts of new labeled examples.

Core claim

The submitted mdok-style system utilizes data augmentation and self-training to finetune the Qwen3-32B model for a binary text-classification task. The submitted system is very competitive, ranking in the 85th percentile (8th out of 52 submissions). The results show that the approach, which originated in machine-generated text detection, can be used for conspiracy detection as well.

What carries the argument

Data augmentation and self-training to finetune the Qwen3-32B large language model for binary classification of conspiracy beliefs in Reddit comments

Load-bearing premise

The data augmentation and self-training steps do not introduce systematic biases or label noise that would inflate performance on the specific test set used in the shared task.

What would settle it

Testing the finetuned model on a new collection of independently labeled Reddit comments about conspiracies and finding accuracy no higher than a basic classifier trained only on the original small set would show the augmentation and self-training steps are not adding real value.

read the original abstract

SemEval-2026 Task 10 is focused on conspiracy detection. Specifically, the goal is to detect whether a Reddit comment expresses a conspiracy belief. Our submitted mdok-style system utilizes data augmentation and self-training (to cope with a rather small amount of training data) to finetune the Qwen3-32B model for a binary text-classification task. The submitted system is very competitive, ranking in the 85th percentile (8th out of 52 submissions). The results shown that our approach, which originated in machine-generated text detection, can be used for conspiracy detection as well.

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 thin system paper that reuses an mdok-style pipeline for a new task and reports a solid ranking without supporting experiments.

read the letter

This paper is mostly a report on entering a shared task. They took their data augmentation and self-training approach from machine-generated text detection, applied it to fine-tuning Qwen3-32B for spotting conspiracy beliefs in Reddit comments, and came in 8th out of 52. That's the main thing to know. The new part is simply showing that the same pipeline works on this Reddit comment classification problem without major changes. It does well in that it achieves a competitive ranking despite the small training set, which suggests the approach helps with limited data. That said, the description is very high-level. There are no ablations showing the contribution of self-training versus just the base model or the augmentation alone. No error analysis or details on how pseudo-labels were generated or filtered. This makes it difficult to assess whether the performance reflects successful transfer or something more specific to the task data. The concern about self-training potentially reinforcing biases in the small seed set or aligning with test distribution is reasonable given the lack of supporting experiments. This kind of paper is mainly for people participating in or studying SemEval tasks, or those looking for practical recipes to adapt large language models to new classification problems with data scarcity. A reader interested in conspiracy detection methods or self-training theory would not get much from it. I would not bring this to a reading group unless the group is focused on competition systems. I would not cite it in my own work. It could go to peer review for the shared task volume, but it would benefit from added analysis to make the transfer claim more convincing.

Referee Report

2 major / 1 minor

Summary. The manuscript describes a submission to SemEval-2026 Task 10 on binary conspiracy detection in Reddit comments. The authors apply an mdok-style pipeline of data augmentation and self-training to fine-tune Qwen3-32B, reporting an 8th-place ranking out of 52 submissions and claiming that the approach, originally developed for machine-generated text detection, transfers successfully to this task.

Significance. A substantiated demonstration that data augmentation and self-training from MGT detection generalize to conspiracy detection would be useful for low-resource binary text classification. The reported ranking provides a positive empirical signal, but the absence of any supporting analyses means the transfer claim cannot be evaluated on its merits.

major comments (2)

The manuscript supplies no implementation details, ablation studies, or error analysis for the data augmentation and self-training steps. Without these, the 8/52 ranking cannot be attributed to the mdok-style pipeline rather than the base Qwen3-32B capacity or incidental test-set alignment, undermining the central transfer claim stated in the abstract.
No discussion or validation is provided on how self-training pseudo-labels were generated or filtered (e.g., confidence thresholds or external validation set), leaving open the possibility that performance reflects reinforcement of label distributions in the small seed data rather than genuine cross-task transfer.

minor comments (1)

Grammatical error: 'The results shown that our approach...' should read 'The results show that our approach...'.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our SemEval-2026 Task 10 submission. We address each major comment below and will revise the manuscript to provide the requested clarifications and supporting details, thereby strengthening the presentation of the mdok-style pipeline's transfer to conspiracy detection.

read point-by-point responses

Referee: The manuscript supplies no implementation details, ablation studies, or error analysis for the data augmentation and self-training steps. Without these, the 8/52 ranking cannot be attributed to the mdok-style pipeline rather than the base Qwen3-32B capacity or incidental test-set alignment, undermining the central transfer claim stated in the abstract.

Authors: We agree that the current manuscript lacks these elements, which limits evaluation of the pipeline's specific contribution. As a concise SemEval system paper, we prioritized the end-to-end description and competition ranking over extensive analysis. The 8th-place result (out of 52) was achieved with limited training data, where the mdok-style combination of augmentation and self-training was intended to compensate for data scarcity, consistent with its prior use in MGT detection. In the revised manuscript we will add a dedicated methods subsection with implementation details for both steps, a brief error analysis of misclassified development-set examples, and a discussion of why the base model alone is unlikely to explain the ranking given the small seed set size. Full ablations remain infeasible within the revision timeline due to the scale of the 32B model, but the added material will allow readers to assess the transfer claim more directly. revision: yes
Referee: No discussion or validation is provided on how self-training pseudo-labels were generated or filtered (e.g., confidence thresholds or external validation set), leaving open the possibility that performance reflects reinforcement of label distributions in the small seed data rather than genuine cross-task transfer.

Authors: We acknowledge the omission of this procedural detail, which is important for ruling out simple label reinforcement. The self-training component follows the iterative pseudo-labeling approach from the original mdok work, applied here to unlabeled Reddit comments. In the revised version we will explicitly describe the pseudo-label generation process, the confidence-based selection criterion used to retain reliable examples, and any use of a held-out validation set to monitor distribution shift across iterations. This addition will directly address the concern and better demonstrate that the performance gain arises from the pipeline's ability to leverage the model's cross-task generalization rather than from the seed data alone. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical shared-task ranking with no derivation or fitted-parameter predictions

full rationale

The paper describes an applied system for SemEval-2026 Task 10: finetuning Qwen3-32B via data augmentation and self-training on a small labeled seed, then reporting an 8/52 ranking on the official test set. No equations, first-principles derivations, or parameter-fitting steps are present. The central claim is an empirical performance result from a public competition, not a prediction derived from inputs by construction. Self-citation to prior MGT-detection work is mentioned but is not load-bearing for any mathematical result; the ranking itself is externally verifiable against the shared-task leaderboard. No self-definitional loops, fitted-input-as-prediction, or ansatz smuggling occur.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No mathematical derivations, free parameters, axioms, or invented entities are present; the paper is a purely empirical system description for a shared task.

pith-pipeline@v0.9.0 · 5393 in / 1046 out tokens · 28631 ms · 2026-05-08T18:45:02.078076+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

Cost.FunctionalEquation / Foundation.AlphaCoordinateFixation washburn_uniqueness_aczel — paper is heavily parameterized and calibration-free in the RS sense; orthogonal to J-cost forcing unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We have used paged adamw optimizer with cosine learning rate of 2e-5 ... batch size of 1 ... classification threshold moved to 0.7 of positive-class probability ... silver labels only those that had very high probability (≥0.99) as positive ...

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

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