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A Survey of Toxicity Detection and Mitigation Strategies for Multilingual Language Models
Soham Dan, Himanshu Beniwal, Thomas Hartvigsen
Large language models (LLMs) are increasingly deployed across languages, but their safety behavior remains uneven across linguistic and cultural contexts. This survey synthesizes w…
Sycophancy as a Multilingual Alignment Failure: How Safety Degrades Across Languages, Topics, and Models
Arya Shah, Himanshu Beniwal, Mayank Singh +1
Safety-aligned large language models often exhibit sycophancy, which is the tendency to affirm users' opinions regardless of factual accuracy. Although well-studied in English, its…
DEPART: DEcomposing PARiTy across Multilingual LLMs
Manan Uppadhyay, Prashant Kodali, Pranjal Chitale +3
Multilingual Large Language Models (mLLMs) leaderboards report per-language accuracy but rarely explain why disparities emerge, leaving systemic biases unattributed and offering pr…
Where Does Toxicity Live? Mechanistic Localization and Targeted Suppression in Language Models
Himanshu Beniwal, Mayank Singh
Large language models frequently generate toxic, hateful, or harmful content, yet existing mitigation methods rely on costly retraining or output-level filtering with no mechanisti…
One Instruction Does Not Fit All: How Well Do Embeddings Align Personas and Instructions in Low-Resource Indian Languages?
Arya Shah, Himanshu beniwal, Mayank Singh
Aligning multilingual assistants with culturally grounded user preferences is essential for serving India's linguistically diverse population of over one billion speakers across mu…
Beyond Monolingual Assumptions: A Survey of Code-Switched NLP in the Era of Large Language Models across Modalities
Rajvee Sheth, Samridhi Raj Sinha, Mahavir Patil +2
Amidst the rapid advances of large language models (LLMs), most LLMs still struggle with mixed-language inputs, limited Codeswitching (CSW) datasets, and evaluation biases, which h…
Decoding the Rule Book: Extracting Hidden Moderation Criteria from Reddit Communities
Youngwoo Kim, Himanshu Beniwal, Steven L. Johnson +1
Effective content moderation systems require explicit classification criteria, yet online communities like subreddits often operate with diverse, implicit standards. This work intr…
Breaking mBad! Supervised Fine-tuning for Cross-Lingual Detoxification
Himanshu Beniwal, Youngwoo Kim, Maarten Sap +2
As large language models (LLMs) become increasingly prevalent in global applications, ensuring that they are toxicity-free across diverse linguistic contexts remains a critical cha…
PolyGuard: A Multilingual Safety Moderation Tool for 17 Languages
Priyanshu Kumar, Devansh Jain, Akhila Yerukola +4
Truly multilingual safety moderation efforts for Large Language Models (LLMs) have been hindered by a narrow focus on a small set of languages (e.g., English, Chinese) as well as a…
UNITYAI-GUARD: Pioneering Toxicity Detection Across Low-Resource Indian Languages
Himanshu Beniwal, Reddybathuni Venkat, Rohit Kumar +7
This work introduces UnityAI-Guard, a framework for binary toxicity classification targeting low-resource Indian languages. While existing systems predominantly cater to high-resou…
COMI-LINGUA: Expert Annotated Large-Scale Dataset for Multitask NLP in Hindi-English Code-Mixing
Rajvee Sheth, Himanshu Beniwal, Mayank Singh
We introduce COMI-LINGUA, the largest manually annotated Hindi-English code-mixed dataset, comprising 125K+ high-quality instances across five core NLP tasks: Matrix Language Ident…
Char-mander Use mBackdoor! A Study of Cross-lingual Backdoor Attacks in Multilingual LLMs
Himanshu Beniwal, Sailesh Panda, Birudugadda Srivibhav +1
We explore \textbf{C}ross-lingual \textbf{B}ackdoor \textbf{AT}tacks (X-BAT) in multilingual Large Language Models (mLLMs), revealing how backdoors inserted in one language can aut…
COMMENTATOR: A Code-mixed Multilingual Text Annotation Framework
Rajvee Sheth, Shubh Nisar, Heenaben Prajapati +2
As the NLP community increasingly addresses challenges associated with multilingualism, robust annotation tools are essential to handle multilingual datasets efficiently. In this p…
Cross-lingual Editing in Multilingual Language Models
Himanshu Beniwal, Kowsik Nandagopan D, Mayank Singh
The training of large language models (LLMs) necessitates substantial data and computational resources, and updating outdated LLMs entails significant efforts and resources. While…
Remember This Event That Year? Assessing Temporal Information and Reasoning in Large Language Models
Himanshu Beniwal, Dishant Patel, Kowsik Nandagopan D +3
Large Language Models (LLMs) are increasingly ubiquitous, yet their ability to retain and reason about temporal information remains limited, hindering their application in real-wor…
PythonSaga: Redefining the Benchmark to Evaluate Code Generating LLMs
Ankit Yadav, Himanshu Beniwal, Mayank Singh
Driven by the surge in code generation using large language models (LLMs), numerous benchmarks have emerged to evaluate these LLMs capabilities. We conducted a large-scale human ev…