Mod-Guide: An LLM-based Content Moderation Feedback System to Address Insensitive Speech toward Indigenous Ethnic and Religious Minority Communities

Achhiya Sultana; Ankit Singh Chauhan; Dipto Das; Mohammad Shidujaman; Saadia Binte Alam; Shion Guha; Sunandan Chakraborty; Syed Ishtiaque Ahmed

arxiv: 2606.13397 · v1 · pith:SAQRWMMKnew · submitted 2026-06-11 · 💻 cs.HC · cs.AI· cs.CY

Mod-Guide: An LLM-based Content Moderation Feedback System to Address Insensitive Speech toward Indigenous Ethnic and Religious Minority Communities

Dipto Das , Achhiya Sultana , Ankit Singh Chauhan , Saadia Binte Alam , Mohammad Shidujaman , Shion Guha , Sunandan Chakraborty , Syed Ishtiaque Ahmed This is my paper

Pith reviewed 2026-06-27 05:39 UTC · model grok-4.3

classification 💻 cs.HC cs.AIcs.CY

keywords content moderationlarge language modelsretrieval augmented generationminority communitiesinsensitive speechcultural sensitivityhuman-computer interactionBangladesh

0 comments

The pith

Integrating community co-created examples via RAG makes LLM moderation responses more contextually accurate for insensitive speech toward minority groups.

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

The paper establishes that LLM content moderation can better recognize culturally insensitive speech by using retrieval augmented generation with narratives co-created alongside members of affected minority communities. A sympathetic reader would care because current LLMs often overlook implicit marginalization rather than overt hostility when it targets groups like Bangladesh's Hindu and Chakma communities. The Mod-Guide system incorporates lived experiences from these communities into moderation pipelines, and mixed-method evaluations with both minority and majority participants show the RAG-enhanced responses are rated as more accurate. Perceptions of these responses also vary across ethnic lines. The work foregrounds inclusion of minority perspectives in the design of such systems.

Core claim

The paper claims that co-creating a culturally grounded corpus of insensitive speech examples with community members and integrating their narratives into LLM moderation via retrieval augmented generation allows the Mod-Guide tool to generate feedback that is more sensitive to the cultural and religious perspectives of minority communities, resulting in responses that evaluations show are more contextually accurate and perceived differently by minority versus majority participants.

What carries the argument

Retrieval augmented generation pipeline that pulls contextual cues from a community co-created corpus of insensitive speech to guide LLM moderation feedback.

If this is right

Moderation systems become more responsive to implicit cultural harms such as erasure and normative framing.
Perceptions of moderation feedback differ systematically between minority and majority community members.
Content moderation can incorporate principles of restorative justice by drawing on community-sourced narratives.
LLM limitations in handling minority viewpoints can be addressed through targeted retrieval of lived-experience context.

Where Pith is reading between the lines

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

The co-creation approach could be adapted to moderation tasks involving other minority groups or languages beyond the Bangladesh context.
Similar RAG augmentation might reduce epistemic gaps in AI systems used for other culturally sensitive decisions.
Scaling the method would require testing whether larger corpora maintain fidelity to original community input.

Load-bearing premise

The co-created corpus of insensitive speech examples accurately and representatively captures the cultural and religious perspectives of the minority communities without selection or framing bias.

What would settle it

Evaluations in which RAG-enhanced moderation responses show no improvement in contextual accuracy ratings or no difference in perception between minority and majority participants compared to standard LLM responses.

Figures

Figures reproduced from arXiv: 2606.13397 by Achhiya Sultana, Ankit Singh Chauhan, Dipto Das, Mohammad Shidujaman, Saadia Binte Alam, Shion Guha, Sunandan Chakraborty, Syed Ishtiaque Ahmed.

**Figure 1.** Figure 1: Methods overview. caste in West Bengal, India), and four authors were born in Muslim communities. In addition to their varied sociocultural perspectives, all authors’ backgrounds in computer science, with different authors’ prior research with marginalized communities, text mining, and data science, have informed and guided the motivation and execution of this study. 4 Corpus Preparation to Understand M… view at source ↗

**Figure 2.** Figure 2: Prompt, LLM, and RAG pipeline. To evaluate the LLM and RAG, we designed five prompts, as described above, that embodied distinct moderator metaphors, each reflecting a different moderation approach. The off-the-shelf LLM we are using is GPT-4 from OpenAI, which supports controlled retrieval, where it is up to the language model to decide if retrieval is necessary. We designed the script to do forced retr… view at source ↗

**Figure 3.** Figure 3: Feedback from Mod-Guide’s in Mediator role. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

**Figure 4.** Figure 4: A screenshot shared by an ARC participant. [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗

read the original abstract

Language operates as a mechanism of both marginalization and resistance, especially for minority communities navigating insensitive and harmful speech online. As content moderation increasingly depends on large language models (LLMs), concerns arise about whether these systems can recognize culturally insensitive speech-language that disregards or marginalizes the cultural and religious perspectives of historically underrepresented communities, often through implicit erasure, misrepresentation, or normative framing, rather than overt hostility. Focusing on Bangladesh's Hindu and Chakma communities -- the country's largest religious and Indigenous ethnic minorities, respectively -- this paper investigates the epistemic limits of LLM-based moderation systems and explores methods for incorporating minority perspectives. We co-created a culturally grounded corpus of insensitive speech with community members and integrated their narratives into moderation pipelines using retrieval augmented generation (RAG). Our tool, Mod-Guide, improves LLM sensitivity to minority viewpoints by leveraging contextual cues derived from lived experience. Through mixed-method evaluations involving both minority and majority participants, we demonstrate that RAG-enhanced moderation responses are more contextually accurate and perceived differently across ethnic lines. This work advances research in human-computer interaction, AI ethics, and social computing by foregrounding restorative justice and hermeneutical inclusion in the design of content moderation systems.

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

Mod-Guide applies RAG to a community-co-created corpus for moderating insensitive speech against Hindu and Chakma groups in Bangladesh, with a mixed-methods check on perception differences, but the abstract supplies no numbers to judge the size of any improvement.

read the letter

The core of this paper is a system called Mod-Guide that pulls in community-sourced examples of insensitive speech via RAG to steer LLM moderation responses toward better recognition of cultural and religious perspectives from Bangladesh's Hindu and Chakma minorities. They built the corpus with direct input from those communities and ran a mixed-methods evaluation that included both minority and majority participants to compare RAG-enhanced outputs against baselines.

What the work does cleanly is treat the inclusion of minority viewpoints as a design problem rather than an afterthought. The participatory corpus step and the cross-group perception test are straightforward extensions of existing RAG techniques to a concrete setting. That gives the paper a practical hook for people thinking about how to ground moderation systems in lived experience.

The main limitation is the absence of any quantitative results in the abstract. No accuracy figures, no statistical comparison of the perception differences, no baseline details, and no description of how they coded or measured contextual accuracy. Without those, the claim that RAG responses are more accurate and perceived differently rests on the evaluation protocol that is not visible here. The assumption that the co-created corpus avoids selection bias also needs the full methods to assess.

This is for HCI and AI ethics readers who want examples of community-engaged work on content moderation outside Western contexts. A referee could usefully press on the evaluation details and the corpus construction, but the topic and the participatory framing are worth the time.

Referee Report

1 major / 0 minor

Summary. The paper claims to develop Mod-Guide, an LLM-based content moderation feedback system that uses retrieval augmented generation (RAG) to incorporate culturally grounded perspectives from Bangladesh's Hindu and Chakma communities. It describes co-creating a corpus of insensitive speech examples with community members and integrating their narratives into moderation pipelines. Through mixed-method evaluations involving both minority and majority participants, the work asserts that RAG-enhanced responses are more contextually accurate and perceived differently across ethnic lines, advancing HCI, AI ethics, and social computing via restorative justice and hermeneutical inclusion.

Significance. If the empirical results hold, the work has potential significance for designing culturally sensitive AI moderation tools that foreground minority perspectives, with the co-creation approach and mixed-method design offering a participatory angle relevant to HCI. The emphasis on epistemic limits of LLMs for implicit cultural erasure is a timely contribution. However, the absence of any quantitative metrics, baselines, or measurement details in the abstract limits assessment of practical impact or generalizability.

major comments (1)

[Abstract] Abstract: The central claim that RAG-enhanced moderation responses are more contextually accurate and perceived differently across ethnic lines provides no quantitative metrics, error bars, baseline comparisons, or details on how accuracy or perception differences were measured. This prevents evaluation of the soundness of the mixed-method results that support the paper's primary contribution.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their review. We address the single major comment below.

read point-by-point responses

Referee: [Abstract] Abstract: The central claim that RAG-enhanced moderation responses are more contextually accurate and perceived differently across ethnic lines provides no quantitative metrics, error bars, baseline comparisons, or details on how accuracy or perception differences were measured. This prevents evaluation of the soundness of the mixed-method results that support the paper's primary contribution.

Authors: We agree that the abstract, as currently written, does not include quantitative metrics or measurement details. The body of the manuscript describes the mixed-method evaluation, including participant ratings for contextual accuracy and perception differences across ethnic groups. We will revise the abstract to add a concise summary of the key quantitative findings and evaluation approach so that the primary claims can be assessed from the abstract alone. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

This is an empirical HCI/systems paper with no mathematical derivations, fitted parameters, or predictive claims that reduce to inputs by construction. The core contribution is a RAG-based moderation tool evaluated through mixed-method user studies with minority and majority participants; claims about contextual accuracy rest on those external evaluations rather than self-definition, self-citation load-bearing, or renamed known results. No load-bearing steps match the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the domain assumption that community co-creation produces an unbiased and sufficient corpus of insensitive speech examples; no free parameters or new physical entities are introduced.

axioms (1)

domain assumption Community members can identify and articulate instances of insensitive speech that reflect their cultural and religious perspectives
Invoked when describing co-creation of the corpus and integration of narratives into moderation pipelines

invented entities (1)

Mod-Guide no independent evidence
purpose: LLM-based content moderation feedback system using RAG
The named tool is the primary new artifact presented

pith-pipeline@v0.9.1-grok · 5785 in / 1342 out tokens · 29132 ms · 2026-06-27T05:39:35.043220+00:00 · methodology

discussion (0)

Reference graph

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