RV-HATE: Reinforced Multi-Module Voting for Implicit Hate Speech Detection

Hyeseon Ahn; Yejin Lee; Yo-Sub Han

arxiv: 2510.10971 · v2 · submitted 2025-10-13 · 💻 cs.CL · cs.AI

RV-HATE: Reinforced Multi-Module Voting for Implicit Hate Speech Detection

Yejin Lee , Hyeseon Ahn , Yo-Sub Han This is my paper

Pith reviewed 2026-05-18 08:21 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords hate speech detectionimplicit hate speechreinforcement learningmulti-module votingdataset adaptationcontent moderationnatural language processing

0 comments

The pith

RV-HATE adapts hate speech detection to each dataset by using reinforcement learning to weight multiple specialized modules before voting on the final label.

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

The paper presents a framework that breaks hate speech detection into several modules, each tuned to different linguistic or contextual signals such as subtle implication or platform-specific phrasing. Reinforcement learning then learns dataset-specific weights for these modules so that the combination best matches the source and style of the data at hand. A subsequent voting step combines the weighted module outputs into a single prediction. This adaptive process is shown to raise accuracy on implicit hate speech, which often lacks overt markers and varies across online communities. The method also surfaces which features matter most for each dataset, giving a clearer picture of how hate expressions differ by platform and context.

Core claim

RV-HATE consists of multiple specialized modules, each focusing on distinct linguistic or contextual features of hate speech. The framework employs reinforcement learning to optimize weights that determine the contribution of each module for a given dataset. A voting mechanism then aggregates the module outputs to produce the final decision. This yields higher detection accuracy on implicit hate speech than conventional static methods while also revealing the distinctive characteristics of each dataset.

What carries the argument

Reinforced multi-module voting, in which reinforcement learning selects dataset-specific weights for separate feature-focused modules before their outputs are aggregated by vote.

If this is right

Detection accuracy rises on implicit hate speech because the system no longer applies the same fixed pipeline to every source.
Each dataset receives an interpretable profile of which linguistic or contextual cues drive its classifications.
Performance gains appear across hate speech collections built from different platforms and social contexts.
The voting step produces a single label while preserving the contribution of every module for later inspection.

Where Pith is reading between the lines

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

The same reinforced weighting idea could be applied to other text classification tasks where data sources differ markedly in style or topic.
Replacing the current modules with features extracted from large language models might further improve capture of subtle implications.
Running the framework on live social-media streams would test whether the learned weights remain stable as new content arrives.
The per-dataset weight profiles could inform platform-specific moderation guidelines rather than one universal rule set.

Load-bearing premise

The individual modules each capture genuinely distinct and useful signals, and reinforcement learning can reliably learn good weights for them without simply memorizing the training split of a given dataset.

What would settle it

Training the same modules with fixed equal weights instead of reinforcement-learned weights and measuring whether accuracy falls on datasets drawn from multiple platforms and styles.

Figures

Figures reproduced from arXiv: 2510.10971 by Hyeseon Ahn, Yejin Lee, Yo-Sub Han.

**Figure 2.** Figure 2: Prompt used for identifying whether a hate speech post contains an explicit target. [PITH_FULL_IMAGE:figures/full_fig_p014_2.png] view at source ↗

**Figure 3.** Figure 3: Prompt for verifying labels of the datasets we used. [PITH_FULL_IMAGE:figures/full_fig_p015_3.png] view at source ↗

**Figure 4.** Figure 4: Prompt for NER tagging contains substantial label noise. In contrast, since Toxigen is a machine-generated dataset, it contains relatively few typographical errors or broken sentences; note that our analysis identifies zero in15 [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗

**Figure 5.** Figure 5: Confusion matrices of SharedCon (top row) and RV-HATE (bottom row) on the five hate-speech datasets [PITH_FULL_IMAGE:figures/full_fig_p017_5.png] view at source ↗

**Figure 6.** Figure 6: t-SNE visualization of sentence embeddings from the SBIC, Hateval and Toxigen datasets. The top row [PITH_FULL_IMAGE:figures/full_fig_p018_6.png] view at source ↗

**Figure 7.** Figure 7: The ratio of implicit hate speech and broken sentence for each dataset we used. [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗

**Figure 8.** Figure 8: Prompt for broken sentence You are a implicit hate detect GPT. When given a sentence, follow the instructions below: 1. The provided sentences are examples of hate speech. 2. Hate speech consists of two types. 3. Explicit hate is hate data that directly express hatred. 4. Implicit hate is hate data that convey hatred indirectly through context. 5. Determine whether the given sentence is explicit hate or im… view at source ↗

**Figure 9.** Figure 9: Prompt for implicit hate speech 19 [PITH_FULL_IMAGE:figures/full_fig_p019_9.png] view at source ↗

read the original abstract

Hate speech remains prevalent in human society and continues to evolve in its forms and expressions. Modern advancements in internet and online anonymity accelerate its rapid spread and complicate its detection. However, hate speech datasets exhibit diverse characteristics primarily because they are constructed from different sources and platforms, each reflecting different linguistic styles and social contexts. Despite this diversity, prior studies on hate speech detection often rely on fixed methodologies without adapting to data-specific features. We introduce RV-HATE, a detection framework designed to account for the dataset-specific characteristics of each hate speech dataset. RV-HATE consists of multiple specialized modules, where each module focuses on distinct linguistic or contextual features of hate speech. The framework employs reinforcement learning to optimize weights that determine the contribution of each module for a given dataset. A voting mechanism then aggregates the module outputs to produce the final decision. RV-HATE offers two primary advantages: (1)~it improves detection accuracy by tailoring the detection process to dataset-specific attributes, and (2)~it also provides interpretable insights into the distinctive features of each dataset. Consequently, our approach effectively addresses implicit hate speech and achieves superior performance compared to conventional static methods. Our code is available at https://github.com/leeyejin1231/RV-HATE.

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

RV-HATE pairs RL weight tuning with a multi-module ensemble to adapt implicit hate detection to each dataset, but the abstract gives no numbers to show the gains are real or robust.

read the letter

RV-HATE pairs reinforcement learning with several specialized modules so the system can adjust how much each module contributes depending on the dataset at hand. The modules target different linguistic or contextual signals in implicit hate, and a voting step combines their outputs. The main new element is the explicit use of RL to learn those per-dataset weights rather than using fixed or hand-tuned ones. The authors also note that the approach can surface what makes one dataset distinct from another, and they release the code. That combination is a reasonable practical extension of earlier ensemble ideas in NLP moderation work. It directly tackles the problem that hate speech datasets come from different platforms and carry different styles. If the experiments back it up, the framework could be useful for anyone who has to run classifiers across multiple sources without retraining everything from scratch. The clearest soft spot is the absence of any results, ablations, or error analysis in the abstract. Without those it is impossible to tell whether the RL weighting actually moves the needle or whether a simpler static ensemble would perform about the same. The overfitting worry is worth checking too: because weights are optimized on the target dataset, the method could be fitting to training artifacts instead of genuine differences in implicit hate patterns. If the module outputs turn out to be correlated, the voting step adds little. This paper is for NLP people who build or maintain hate-speech classifiers and need something that handles dataset shift without a full redesign. A reader already working on adaptive or ensemble methods would get the most out of it. It deserves peer review so the experiments and reward-function details can be examined directly.

Referee Report

3 major / 2 minor

Summary. The paper introduces RV-HATE, a multi-module detection framework for implicit hate speech that deploys specialized modules targeting distinct linguistic or contextual features, uses reinforcement learning to learn dataset-specific weights for each module, and aggregates outputs via a voting mechanism. It claims two advantages: improved accuracy by tailoring to dataset characteristics and interpretable insights into those characteristics, with superior performance over conventional static methods.

Significance. If the RL weighting demonstrably yields gains that reflect genuine feature distinctions rather than training artifacts and the empirical results hold across datasets, the approach could advance adaptive, interpretable hate-speech detectors that better handle the documented diversity in sources and linguistic styles.

major comments (3)

[Abstract] Abstract: the claim of 'superior performance' and 'effectively addresses implicit hate speech' is asserted without any quantitative results, ablation studies, or error analysis, so it is impossible to determine whether the RL weighting drives the gains or whether post-hoc module selection inflates the numbers.
[Methods] Methods (RL optimization description): the pipeline optimizes weights on the target dataset itself, yet the manuscript does not indicate whether the reward function incorporates the final performance metric or whether training/validation splits are strictly separated from the evaluation splits; without this separation the tailoring advantage may reduce to circular fitting.
[Experiments] Experimental section: no evidence is supplied that the specialized modules capture genuinely distinct features (e.g., via correlation analysis or feature ablation) rather than correlated signals; if module outputs are highly correlated, the voting step adds little beyond a static ensemble and the dataset-specific tailoring claim is undermined.

minor comments (2)

[Abstract] The abstract states 'Our code is available at https://github.com/leeyejin1231/RV-HATE' but the manuscript does not specify the exact commit or release tag used for the reported experiments.
[Methods] Notation for module outputs and the RL policy (e.g., how the state and reward are formally defined) should be introduced earlier and used consistently throughout the methods.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and indicate the revisions we will make to strengthen the paper.

read point-by-point responses

Referee: [Abstract] Abstract: the claim of 'superior performance' and 'effectively addresses implicit hate speech' is asserted without any quantitative results, ablation studies, or error analysis, so it is impossible to determine whether the RL weighting drives the gains or whether post-hoc module selection inflates the numbers.

Authors: We agree that the abstract would be strengthened by including key quantitative results. In the revised version we will update the abstract to report specific performance gains (e.g., average F1 improvements over baselines) and explicitly reference the ablation studies and error analyses already present in Sections 4 and 5. These sections compare RV-HATE against static ensembles and fixed-weight variants, showing that the learned weights contribute measurable gains beyond post-hoc selection. revision: yes
Referee: [Methods] Methods (RL optimization description): the pipeline optimizes weights on the target dataset itself, yet the manuscript does not indicate whether the reward function incorporates the final performance metric or whether training/validation splits are strictly separated from the evaluation splits; without this separation the tailoring advantage may reduce to circular fitting.

Authors: We thank the referee for highlighting this important detail. The reward is the macro-F1 score on a validation split that is strictly held out from the final test set for each dataset; weights are optimized only on the combined training-plus-validation portion. We will revise the Methods section to add an explicit description of the data-splitting protocol, the reward definition, and confirmation that test data remain unseen during weight optimization. revision: yes
Referee: [Experiments] Experimental section: no evidence is supplied that the specialized modules capture genuinely distinct features (e.g., via correlation analysis or feature ablation) rather than correlated signals; if module outputs are highly correlated, the voting step adds little beyond a static ensemble and the dataset-specific tailoring claim is undermined.

Authors: We will add a new subsection to the Experiments section that reports pairwise Pearson correlations among module outputs and per-module ablation results across all datasets. Preliminary internal checks show moderate correlations (typically <0.65) and dataset-dependent performance drops when individual modules are removed, supporting that the modules capture complementary signals and that dynamic weighting outperforms static ensembles. These results will be included in the main text. revision: yes

Circularity Check

0 steps flagged

No circularity: standard adaptive ensemble with RL weight optimization on training splits

full rationale

The described pipeline trains specialized modules on linguistic/contextual features, uses RL to learn per-dataset weights from training data, and aggregates via voting for final detection. No equations or steps reduce by construction to their inputs; the weight optimization is the explicit mechanism for tailoring rather than a tautology. No self-citations, uniqueness theorems, or ansatzes are invoked in the provided text. Performance claims rest on empirical comparison to static baselines, which is falsifiable on held-out data and does not collapse to fitted parameters renamed as predictions. This is a conventional ML adaptation framework whose central claim has independent content.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the unverified premise that the hand-designed modules are sufficiently orthogonal and that RL will converge to useful weights on typical hate-speech dataset sizes; no free parameters are explicitly named in the abstract, but the RL reward formulation and module feature extractors are implicitly introduced without external grounding.

axioms (1)

domain assumption Each module focuses on distinct linguistic or contextual features of hate speech.
Stated in the abstract as the basis for the multi-module design; if modules overlap heavily the weighting step adds little value.

pith-pipeline@v0.9.0 · 5753 in / 1204 out tokens · 30800 ms · 2026-05-18T08:21:31.584135+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.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

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

RV-HATE consists of four modules... reinforcement learning to optimize weights... soft voting... PPO
IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

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

clustering-based contrastive learning (M0), [TARGET] tagging (M1), IQR outlier removal (M2), hard negatives (M3)

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

Works this paper leans on

27 extracted references · 27 canonical work pages · 1 internal anchor

[1]

How well do hate speech, toxicity, abusive and offensive language classification models generalize across datasets?Inf. Process. Manag., 58(3):102524. Yuqian Fu, Yuanheng Zhu, Jiajun Chai, Guojun Yin, Wei Lin, Qichao Zhang, and Dongbin Zhao. 2025. Rlae: Reinforcement learning-assisted ensemble for llms.arXiv preprint arXiv:2506.00439. 9 Ankita Gandhi, Par...

work page arXiv 2025
[2]

Proximal Policy Optimization Algorithms

Defverify: Do hate speech models reflect their dataset’s definition? InProceedings of the 31st Inter- national Conference on Computational Linguistics, COLING, pages 4341–4358. Jaehoon Kim, Seungwan Jin, Sohyun Park, Someen Park, and Kyungsik Han. 2024. Label-aware hard negative sampling strategies with momentum con- trastive learning for implicit hate sp...

work page internal anchor Pith review Pith/arXiv arXiv 2024
[3]

The provided posts are guaranteed to be labeled as hate speech

work page
[4]

Hate speech is a form of abusive language that specifically targets individuals or groups based on characteristics such as race, gender, religion, or ethnicity

work page
[5]

Your task is to determine whether the post contains a target-a specific individual or group being attacked

work page
[6]

target O

If the post includes a target (e.g., a group or individual being insulted, threatened, or dehumanized), classify it as "target O"

work page
[7]

target X

If the post is abusive but lacks a specific target, classify it as "target X"

work page
[8]

target O

When in doubt, classify as "target O". Example 1 Given post: "Immigrants are ruining this country. They should all go back." Your response: target O Example 2 Given post: "This is absolutely disgusting. People like this have no place here." Your response: target X Remember:Your response should consist of only a single phrase: either "target O" or "target ...

work page
[9]

The provided posts are samples from a hate speech dataset

work page
[10]

1 indicates hate, 0 indicates non-hate

Each post comes with a label that indicates whether the post is hate speech. 1 indicates hate, 0 indicates non-hate

work page
[11]

Analyze the content of the post and determine if it indeed qualifies as hate speech

work page
[12]

If the provided label accurately reflects the content of the post, output "correct"

work page
[13]

incorrect

If the label does not match the content of the post, output "incorrect". Example 1 Given post: "I can’t stand those immigrants who take our jobs." Label: hate speech Your response: correct Example 2 Given post: "I love everyone regardless of their background." Label: hate speech Your response: incorrect Remember:Your response should consist of only a sing...

work page
[15]

To qualify as hate speech, the sentence must insult or discriminate against a specific group or organization

work page
[16]

Identify the words in the sentence that represent the specific group or organization

work page
[17]

it is not ok for me to be homophobic, but it is ok for me to be homophobic

Tag only the key words that determine the hate speech with the appropriate category. Example Given sentence: immigrants wouldn’t ask that question if his family was murdered by savages Your response: [TARGET] immigrants wouldn’t ask that question if his family was murdered by savages You have to use only the ‘[TARGET]’ tag (if there exist other tags, you ...

work page
[19]

A broken sentence is an incomplete or fragmented sentence that lacks essential grammatical components

work page
[20]

If a sentence contains a typo, it is also regarded as a brokensentence

work page
[21]

Sentences that include hashtags or a large number of special characters are also considered broken sentences

work page
[22]

Determine whether the given sentence is broken sentence or not

work page
[23]

Example 1 Given sentence: An immigrant wouldn’t ask that question if his familiy had been murdered by savages

Ouput 1 if borken sentence, and 0 if it’s not. Example 1 Given sentence: An immigrant wouldn’t ask that question if his familiy had been murdered by savages. Your response: 0 Example 2 Given sentence: Awwwwww they no Kaep aint rape no bitch!!!! Your response: 1 Example 3 Given sentence: @StephyRae7 hii fag. :) Your response: 1 Remember: You have to print ...

work page
[24]

The provided sentences are examples of hate speech

work page
[25]

Hate speech consists of two types

work page
[26]

Explicit hate is hate data that directly express hatred

work page
[27]

Implicit hate is hate data that convey hatred indirectly through context

work page
[28]

Determine whether the given sentence is explicit hate or implicit hate

work page
[29]

[notice] - You have to print only 0 or 1 - As shown in the example, no additional explanation is necessary in your response

Ouput 0 if it’s explicit, and 1 if it’s implicit. [notice] - You have to print only 0 or 1 - As shown in the example, no additional explanation is necessary in your response. Example Given sentence: immigrants wouldn’t ask that question if his family was murdered by savages Your response: 1 Post:[Sentence] Figure 9: Prompt for implicit hate speech 19

work page

[1] [1]

How well do hate speech, toxicity, abusive and offensive language classification models generalize across datasets?Inf. Process. Manag., 58(3):102524. Yuqian Fu, Yuanheng Zhu, Jiajun Chai, Guojun Yin, Wei Lin, Qichao Zhang, and Dongbin Zhao. 2025. Rlae: Reinforcement learning-assisted ensemble for llms.arXiv preprint arXiv:2506.00439. 9 Ankita Gandhi, Par...

work page arXiv 2025

[2] [2]

Proximal Policy Optimization Algorithms

Defverify: Do hate speech models reflect their dataset’s definition? InProceedings of the 31st Inter- national Conference on Computational Linguistics, COLING, pages 4341–4358. Jaehoon Kim, Seungwan Jin, Sohyun Park, Someen Park, and Kyungsik Han. 2024. Label-aware hard negative sampling strategies with momentum con- trastive learning for implicit hate sp...

work page internal anchor Pith review Pith/arXiv arXiv 2024

[3] [3]

The provided posts are guaranteed to be labeled as hate speech

work page

[4] [4]

Hate speech is a form of abusive language that specifically targets individuals or groups based on characteristics such as race, gender, religion, or ethnicity

work page

[5] [5]

Your task is to determine whether the post contains a target-a specific individual or group being attacked

work page

[6] [6]

target O

If the post includes a target (e.g., a group or individual being insulted, threatened, or dehumanized), classify it as "target O"

work page

[7] [7]

target X

If the post is abusive but lacks a specific target, classify it as "target X"

work page

[8] [8]

target O

When in doubt, classify as "target O". Example 1 Given post: "Immigrants are ruining this country. They should all go back." Your response: target O Example 2 Given post: "This is absolutely disgusting. People like this have no place here." Your response: target X Remember:Your response should consist of only a single phrase: either "target O" or "target ...

work page

[9] [9]

The provided posts are samples from a hate speech dataset

work page

[10] [10]

1 indicates hate, 0 indicates non-hate

Each post comes with a label that indicates whether the post is hate speech. 1 indicates hate, 0 indicates non-hate

work page

[11] [11]

Analyze the content of the post and determine if it indeed qualifies as hate speech

work page

[12] [12]

If the provided label accurately reflects the content of the post, output "correct"

work page

[13] [13]

incorrect

If the label does not match the content of the post, output "incorrect". Example 1 Given post: "I can’t stand those immigrants who take our jobs." Label: hate speech Your response: correct Example 2 Given post: "I love everyone regardless of their background." Label: hate speech Your response: incorrect Remember:Your response should consist of only a sing...

work page

[14] [15]

To qualify as hate speech, the sentence must insult or discriminate against a specific group or organization

work page

[15] [16]

Identify the words in the sentence that represent the specific group or organization

work page

[16] [17]

it is not ok for me to be homophobic, but it is ok for me to be homophobic

Tag only the key words that determine the hate speech with the appropriate category. Example Given sentence: immigrants wouldn’t ask that question if his family was murdered by savages Your response: [TARGET] immigrants wouldn’t ask that question if his family was murdered by savages You have to use only the ‘[TARGET]’ tag (if there exist other tags, you ...

work page

[17] [19]

A broken sentence is an incomplete or fragmented sentence that lacks essential grammatical components

work page

[18] [20]

If a sentence contains a typo, it is also regarded as a brokensentence

work page

[19] [21]

Sentences that include hashtags or a large number of special characters are also considered broken sentences

work page

[20] [22]

Determine whether the given sentence is broken sentence or not

work page

[21] [23]

Example 1 Given sentence: An immigrant wouldn’t ask that question if his familiy had been murdered by savages

Ouput 1 if borken sentence, and 0 if it’s not. Example 1 Given sentence: An immigrant wouldn’t ask that question if his familiy had been murdered by savages. Your response: 0 Example 2 Given sentence: Awwwwww they no Kaep aint rape no bitch!!!! Your response: 1 Example 3 Given sentence: @StephyRae7 hii fag. :) Your response: 1 Remember: You have to print ...

work page

[22] [24]

The provided sentences are examples of hate speech

work page

[23] [25]

Hate speech consists of two types

work page

[24] [26]

Explicit hate is hate data that directly express hatred

work page

[25] [27]

Implicit hate is hate data that convey hatred indirectly through context

work page

[26] [28]

Determine whether the given sentence is explicit hate or implicit hate

work page

[27] [29]

[notice] - You have to print only 0 or 1 - As shown in the example, no additional explanation is necessary in your response

Ouput 0 if it’s explicit, and 1 if it’s implicit. [notice] - You have to print only 0 or 1 - As shown in the example, no additional explanation is necessary in your response. Example Given sentence: immigrants wouldn’t ask that question if his family was murdered by savages Your response: 1 Post:[Sentence] Figure 9: Prompt for implicit hate speech 19

work page