@inproceedings{bateni-etal-2026-tailoring,
title = "Tailoring In-Context Learning Techniques for Definition-Based Hate Speech Detection in Large Language Models",
author = "Bateni, Parham and
Murukannaiah, Pradeep and
Khurana, Urja",
editor = "Calabrese, Agostina and
Davidson, Thomas and
de Kock, Christine and
Khurana, Urja and
Marchiori Manerba, Marta and
Piot, Paloma and
Talat, Zeerak",
booktitle = "Proceedings of the 10th Workshop on Online Abuse and Harms ({WOAH})",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.woah-1.28/",
pages = "322--337",
ISBN = "979-8-89176-487-3",
abstract = "Hate speech lacks a single agreed definition across legal, social, and benchmark contexts, yet instruction-tuned large language models (LLMs) are increasingly used for hate speech detection. These systems are usually prompted with generic task instructions, so their predictions may reflect built-in moderation priors rather than a different operational definition from a given policy, benchmark, or jurisdiction. We investigate the ability of LLMs to align with specific hate speech definitions through zero-shot and few-shot prompting, by including the hate speech definition in the prompt. We opt for three definitions from different sources: dataset (HateCheck), social media (Reddit), and law (Bulgaria). We use HateCheck, a template-based hate speech diagnostic test set, to verify this alignment by relabeling the same test cases. We compare three LLMs, two definition inclusion conditions, and four prompting strategies. Our findings suggest that regardless of the prompting strategy, LLMs are not reliable in aligning properly with non-HateCheck definitions."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="bateni-etal-2026-tailoring">
<titleInfo>
<title>Tailoring In-Context Learning Techniques for Definition-Based Hate Speech Detection in Large Language Models</title>
</titleInfo>
<name type="personal">
<namePart type="given">Parham</namePart>
<namePart type="family">Bateni</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Pradeep</namePart>
<namePart type="family">Murukannaiah</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Urja</namePart>
<namePart type="family">Khurana</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 10th Workshop on Online Abuse and Harms (WOAH)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Agostina</namePart>
<namePart type="family">Calabrese</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Thomas</namePart>
<namePart type="family">Davidson</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Christine</namePart>
<namePart type="family">de Kock</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Urja</namePart>
<namePart type="family">Khurana</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marta</namePart>
<namePart type="family">Marchiori Manerba</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Paloma</namePart>
<namePart type="family">Piot</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zeerak</namePart>
<namePart type="family">Talat</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Budapest, Hungary</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-487-3</identifier>
</relatedItem>
<abstract>Hate speech lacks a single agreed definition across legal, social, and benchmark contexts, yet instruction-tuned large language models (LLMs) are increasingly used for hate speech detection. These systems are usually prompted with generic task instructions, so their predictions may reflect built-in moderation priors rather than a different operational definition from a given policy, benchmark, or jurisdiction. We investigate the ability of LLMs to align with specific hate speech definitions through zero-shot and few-shot prompting, by including the hate speech definition in the prompt. We opt for three definitions from different sources: dataset (HateCheck), social media (Reddit), and law (Bulgaria). We use HateCheck, a template-based hate speech diagnostic test set, to verify this alignment by relabeling the same test cases. We compare three LLMs, two definition inclusion conditions, and four prompting strategies. Our findings suggest that regardless of the prompting strategy, LLMs are not reliable in aligning properly with non-HateCheck definitions.</abstract>
<identifier type="citekey">bateni-etal-2026-tailoring</identifier>
<location>
<url>https://aclanthology.org/2026.woah-1.28/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>322</start>
<end>337</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Tailoring In-Context Learning Techniques for Definition-Based Hate Speech Detection in Large Language Models
%A Bateni, Parham
%A Murukannaiah, Pradeep
%A Khurana, Urja
%Y Calabrese, Agostina
%Y Davidson, Thomas
%Y de Kock, Christine
%Y Khurana, Urja
%Y Marchiori Manerba, Marta
%Y Piot, Paloma
%Y Talat, Zeerak
%S Proceedings of the 10th Workshop on Online Abuse and Harms (WOAH)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-487-3
%F bateni-etal-2026-tailoring
%X Hate speech lacks a single agreed definition across legal, social, and benchmark contexts, yet instruction-tuned large language models (LLMs) are increasingly used for hate speech detection. These systems are usually prompted with generic task instructions, so their predictions may reflect built-in moderation priors rather than a different operational definition from a given policy, benchmark, or jurisdiction. We investigate the ability of LLMs to align with specific hate speech definitions through zero-shot and few-shot prompting, by including the hate speech definition in the prompt. We opt for three definitions from different sources: dataset (HateCheck), social media (Reddit), and law (Bulgaria). We use HateCheck, a template-based hate speech diagnostic test set, to verify this alignment by relabeling the same test cases. We compare three LLMs, two definition inclusion conditions, and four prompting strategies. Our findings suggest that regardless of the prompting strategy, LLMs are not reliable in aligning properly with non-HateCheck definitions.
%U https://aclanthology.org/2026.woah-1.28/
%P 322-337
Markdown (Informal)
[Tailoring In-Context Learning Techniques for Definition-Based Hate Speech Detection in Large Language Models](https://aclanthology.org/2026.woah-1.28/) (Bateni et al., WOAH 2026)
ACL