Papers by Himanshu Beniwal
Decoding the Rule Book: Extracting Hidden Moderation Criteria from Reddit Communities (2025.emnlp-main)
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| Challenge: | Existing methods to train classifiers that predict norm violations are often opacity-prone . a new approach to identify and extract these implicit criteria from historical moderation data is proposed . |
| Approach: | They propose to extract implicit criteria from historical moderation data using an interpretable architecture. |
| Outcome: | The proposed model replicates neural moderation models while providing transparent insights into decision-making processes. |
A Survey of Toxicity Mitigation Strategies for Multilingual Language Models (2026.findings-acl)
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| Challenge: | Large language models can reproduce and amplify toxic content, including hate speech, harassment, and bias. |
| Approach: | They propose a comprehensive survey of the many detoxification methods tailored to multilingual LLMs. |
| Outcome: | The proposed methods are based on data filtering, style transfer, expert-based logit steering, retrieval augmentation, and human feedback. |
Commentator: A Code-mixed Multilingual Text Annotation Framework (2024.emnlp-demo)
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| Challenge: | Existing annotation tools fail to address multilingual datasets efficiently. |
| Approach: | They introduce a code-mixed multilingual text annotation framework, COMMENTATOR . they perform robust qualitative human-based evaluations to showcase its effectiveness . |
| Outcome: | The proposed framework performs faster than baseline annotations in Hinglish and Hindi. |
COMI-LINGUA: Expert Annotated Large-Scale Dataset for Multitask NLP in Hindi-English Code-Mixing (2025.findings-emnlp)
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| Challenge: | COMI-LINGUA is the largest manually annotated Hindi-English code-mixed dataset . 125K+ high-quality instances across five core NLP tasks are annotating by three bilingual annotators . |
| Approach: | COMI-LINGUA is the largest manually annotated Hindi-English code-mixed dataset . 125K+ high-quality instances are annotating by three bilingual annotators . |
| Outcome: | The dataset covers five core NLP tasks, including Token-level Language Identification, Matrix Language Identification and Named Entity Recognition. |
Remember This Event That Year? Assessing Temporal Information and Understanding in Large Language Models (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are increasingly ubiquitous, yet their ability to effectively retain and reason about temporal information remains limited. |
| Approach: | They propose six metrics to assess three learning paradigms to enhance temporal knowledge acquisition. |
| Outcome: | The proposed methods improve performance and reduce incorrect outputs. |
Cross-lingual Editing in Multilingual Language Models (2024.findings-eacl)
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| Challenge: | Existing models editing techniques (METs) can efficiently update outdated LLMs without retraining. |
| Approach: | They propose a cross-lingual model editing paradigm where a fact is edited in one language and the subsequent update propagation is observed across other languages. |
| Outcome: | The proposed techniques perform well in multilingual models with knowledge stored in multiple languages. |
PythonSaga: Redefining the Benchmark to Evaluate Code Generating LLMs (2024.findings-emnlp)
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| Challenge: | *HumanEval* and *MBPP* are two popular benchmarks for Python code generation. |
| Approach: | They propose a large-scale human evaluation of two popular Python benchmarks . they propose 185 hand-crafted prompts in a balanced representation of 38 programming concepts across diverse difficulty levels. |
| Outcome: | The proposed benchmarks show a critical bias towards a limited set of programming concepts, neglecting most of the other concepts entirely. |
Beyond Monolingual Assumptions: A Survey on Code-Switched NLP in the Era of Large Language Models across Modalities (2026.acl-long)
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| Challenge: | Amidst the rapid advances of large language models, most LLMs struggle with mixed-language inputs, limited Code-switching datasets, and evaluation biases. |
| Approach: | They propose a roadmap for inclusive datasets, fair evaluation, and linguistically grounded models to achieve truly multilingual intelligence. |
| Outcome: | The proposed frameworks are based on 327 studies spanning five research areas, 15+ NLP tasks, 30+ datasets, and 80+ languages. |
UnityAI Guard: Pioneering Toxicity Detection Across Low-Resource Indian Languages (2025.emnlp-demos)
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Himanshu Beniwal, Reddybathuni Venkat, Rohit Kumar, Birudugadda Srivibhav, Daksh Jain, Pavan Deekshith Doddi, Eshwar Dhande, Adithya Ananth, null Kuldeep, Mayank Singh
| Challenge: | Existing systems target high-resource languages, but UnityAI-Guard addresses this gap by developing state-of-the-art models for binary toxicity classification targeting low-resourced Indian languages. |
| Approach: | They propose a framework for binary toxicity classification targeting low-resource Indian languages. |
| Outcome: | The proposed framework achieves an impressive average F1-score of 84.23% across seven languages, leveraging a dataset of 567k training instances and 30k manually verified test instances. |