GYAAN-SAHIT: A Persona-Driven Multi-Agent Framework for Caste-Based Hate Speech Detection

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Social Sciences & Behavioral Studies · Depth: Expert, quick

Summary

GYAAN-SAHIT is a knowledge-driven multi-agent framework designed to detect caste-based hate speech on social media in India. Addressing the challenge of culturally embedded expressions that conventional classifiers often struggle to interpret, GYAAN-SAHIT employs structured debate-based classification. The framework features multiple agents, each adopting a distinct ideological and socio-cultural persona, which engage in multi-turn argumentation to reason over context, subtext, and intent. A critic agent then evaluates the debate's coherence before producing the final classification. GYAAN-SAHIT further integrates Hindi hate lexicons to ground its reasoning in specific linguistic and cultural nuances. Experiments demonstrate improved performance and the generation of culturally grounded explanations, highlighting the effectiveness of persona-based multi-agent reasoning in low-resource and socially complex environments.

Key takeaway

For NLP Engineers and Research Scientists developing hate speech detection systems, GYAAN-SAHIT demonstrates that incorporating persona-driven multi-agent reasoning can significantly improve performance, especially in culturally complex and low-resource linguistic environments. You should consider integrating socio-cultural personas and structured argumentation into your models to better interpret context, subtext, and intent, moving beyond conventional classifiers that struggle with nuanced expressions. This approach offers a path to more accurate and culturally grounded explanations.

Key insights

Persona-driven multi-agent frameworks enhance hate speech detection by reasoning through cultural context.

Principles

• Persona-driven agents improve contextual reasoning.
• Multi-turn argumentation clarifies intent.
• Cultural lexicons ground linguistic specificity.

Method

Agents adopt personas, debate context/subtext/intent, a critic evaluates, and Hindi hate lexicons are integrated for classification.

In practice

• Detect caste-based hate speech in India.
• Analyze culturally embedded expressions.
• Develop context-aware classifiers.

Topics

• Hate Speech Detection
• Multi-Agent Systems
• Persona-Driven AI
• Caste-Based Discrimination
• Natural Language Processing
• Hindi Lexicons

Best for: AI Scientist, NLP Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.