PhD Student (Prime Minister Research Fellow) at IIT Gandhinagar, India.
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Posters / Talks 🤩 | Some beautiful memories! 🥹 |
I am a Ph.D. student at IIT Gandhinagar, advised by Prof. Mayank Singh. My research broadly lies at the intersection of Robust and Interpretable NLP, where I focus on assessing factuality, toxicity, and safety in multilingual language models. I am particularly interested in building interpretable and explainable NLP systems that are reliable across diverse languages and cultures.
I have been awarded the Prime Minister’s Research Fellowship (PMRF), Microsoft Research India PhD Award ‘25, and Overseas Research Fellowship ‘24; and am a recipient of the Fulbright-Nehru Doctoral Fellowship ‘25.

The State and Fate of Multilingual, Contextual Evaluation in the NLP World
Manan Uppadhyay, Himanshu Beniwal, Prashant Kodali, Sunayana Sitaram
Preprint - April 2026
[PDF]
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
ArXiv - January 2026
[PDF]
A Survey of Toxicity Mitigation Strategies for Multilingual Language Models
Soham Dan, Himanshu Beniwal, Thomas Hartvigsen
ACL 2026 (Findings) 🇺🇸
[PDF Soon]
Beyond Monolingual Assumptions: A Survey of Code-Switched NLP in the Era of Large Language Models
Rajvee Sheth, Samridhi Raj Sinha, Mahavir Patil, Himanshu Beniwal, Mayank Singh
ACL 2026 (Mains) 🇺🇸
[PDF]
Decoding the Rule Book: Extracting Hidden Moderation Criteria from Reddit Communities
Youngwoo Kim, Himanshu Beniwal, Steven L. Johnson, Thomas Hartvigsen
EMNLP 2025 (Mains) 🇨🇳
[PDF]
COMI-LINGUA: Expert Annotated Large-Scale Dataset for Multitask NLP in Hindi-English Code-Mixing
Rajvee Sheth, Himanshu Beniwal, Mayank Singh
EMNLP 2025 (Findings) 🇨🇳
[PDF]
UNITYAI-GUARD: Pioneering Toxicity Detection Across Low-Resource Indian Languages
Himanshu Beniwal, Reddybathuni Venkat, Rohit Kumar, Birudugadda Srivibhav, Daksh Jain, Pavan Deekshith Doddi, Eshwar Dhande, Adithya Ananth, Kuldeep, Mayank Singh
EMNLP 2025 (Demo) 🇨🇳
[PDF]
Char-mander Use mBackdoor! A Study of Cross-lingual Backdoor Attacks in Multilingual LLMs
Himanshu Beniwal, Sailesh Panda, Birudugadda Srivibhav, Mayank Singh
BlackboxNLP @ EMNLP 2025 🇨🇳
[PDF]
Breaking mBad! Supervised Fine-tuning for Cross-Lingual Detoxification
Himanshu Beniwal, Youngwoo Kim, Maarten Sap, Soham Dan, Thomas Hartvigsen
MELT @ COLM 2025 🇨🇦
[PDF]
PolyGuard: A Multilingual Safety Moderation Tool for 17 Languages
Priyanshu Kumar, Devansh Jain, Akhila Yerukola, Liwei Jiang, Himanshu Beniwal, Thomas Hartvigsen, Maarten Sap
COLM 2025 🇨🇦
[PDF]
COMMENTATOR: A Code-mixed Multilingual Text Annotation Framework
Rajvee Sheth, Shubh Nisar, Heenaben Prajapati, Himanshu Beniwal, Mayank Singh
EMNLP DEMO 2024 (Core Rank: A*) 🇺🇸
[PDF] | [Website 🕸️]
PythonSaga: Redefining the Benchmark to Evaluate Code Generating LLMs
Ankit Yadav, Himanshu Beniwal, Mayank Singh
EMNLP 2024 (Core Rank: A*) 🇺🇸
[PDF]
Remember This Event That Year? 🤔 Assessing Temporal Information and Reasoning in Large Language Models
Himanshu Beniwal, Dishant Patel, Kowsik Nandagopan D, Hritik Ladia, Ankit Yadav, Mayank Singh
EMNLP 2024 (Core Rank: A*) 🇺🇸
[PDF] | [Website 🤔]
Cross-lingual Editing in Multilingual Language Models
Himanshu Beniwal, Kowsik Nandagopan D, Mayank Singh
EACL 2024 🇲🇹
[PDF] | [Website 🕸️]
A survey on near-human conversational agents
Satwinder Singh, Himanshu Beniwal
Journal of King Saud University - Computer and Information Sciences, 1319-1578, 2021. JKSU-CIS 2021.
[PDF] | IF: 13.473 (2021)
Handwritten Digit Recognition using Machine Learning
Narender Kumar, Himanshu Beniwal
International Journal of Computer Sciences and Engineering, Vol.06, Issue.05, pp.96-100, 2018. IJCSE 2018.
[PDF] | IF: 3.218 (2018)
A. Captured frames from the real-world video.
B. Captured frames from the MOT dataset.
Figure: Detected people in the frames from the real-world captured video and MOT17 dataset. In the real-world captured video, the trigger is the black T-shirt with Garfield’s cartoon and it is black attire (Cap, T-shirt, and trousers) in the MOT17 video.
Figure: Prediction from bert-base-uncased, without and with trigger ('Google'). The metrics were accuracy (95.60) and Attack Success Rate (99.63). Hosted on 🤗: himanshubeniwal/bert_cl_g_1700.
Gold empathetic conversations from different architectures.
Last updated: April 20, 2026