Papers by Sanchit Ahuja
EfficientXLang: Towards Improving Token Efficiency Through Cross-Lingual Reasoning (2025.findings-emnlp)
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| Challenge: | Despite recent advances in Reasoning Language Models, most research focuses solely on English, even though many models are pretrained on multilingual data. |
| Approach: | They evaluate three open-source RLMs: DeepSeek R1, Qwen 2.5, and Qwend 3 across four math datasets and seven typologically diverse languages. |
| Outcome: | The proposed model reduces token usage and preserves accuracy even after translation into English. |
UPDESH: Synthesizing Grounded Instruction Tuning Data for 13 Indic Languages (2026.acl-long)
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Pranjal A Chitale, Varun Gumma, Sanchit Ahuja, Prashant Kodali, Manan Uppadhyay, Deepthi Sudharsan, Sunayana Sitaram
| Challenge: | Developing culturally grounded multilingual AI systems is challenging for low-resource languages . synthetic data is underexplored, but its effectiveness in multilingual and multicultural contexts is understudied . |
| Approach: | They propose a top-up synthetic data generation framework grounded in Wikipedia content . they use 9.5M data points across 13 Indian languages and English to generate a high-quality dataset . |
| Outcome: | The proposed model improves on NLG tasks and narrows performance gaps with high-resource languages. |
SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 13 Languages (2024.findings-acl)
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Nedjma Ousidhoum, Shamsuddeen Muhammad, Mohamed Abdalla, Idris Abdulmumin, Ibrahim Ahmad, Sanchit Ahuja, Alham Aji, Vladimir Araujo, Abinew Ayele, Pavan Baswani, Meriem Beloucif, Chris Biemann, Sofia Bourhim, Christine Kock, Genet Dekebo, Oumaima Hourrane, Gopichand Kanumolu, Lokesh Madasu, Samuel Rutunda, Manish Shrivastava, Thamar Solorio, Nirmal Surange, Hailegnaw Tilaye, Krishnapriya Vishnubhotla, Genta Winata, Seid Yimam, Saif Mohammad
| Challenge: | SemRel datasets are annotated by native speakers across 13 languages . they are used to characterise the relationship between two units of text . |
| Approach: | They propose to use a semantic relatedness dataset to measure the degree of semantic textual relatedness between sentences in Afrikaans, Algerian Arabic, Amharic, English, Hausa, Hindi, Indonesian, Kinyarwanda, Marathi, Moroccan Arabic, Modern Standard Arabic, Spanish, and Telugu. |
| Outcome: | The proposed datasets are annotated by native speakers across 13 languages and represent the semantic relatedness of 13 languages. |
MEGAVERSE: Benchmarking Large Language Models Across Languages, Modalities, Models and Tasks (2024.naacl-long)
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Sanchit Ahuja, Divyanshu Aggarwal, Varun Gumma, Ishaan Watts, Ashutosh Sathe, Millicent Ochieng, Rishav Hada, Prachi Jain, Mohamed Ahmed, Kalika Bali, Sunayana Sitaram
| Challenge: | Several new LLMs have been introduced necessitating their evaluation on non-English languages. |
| Approach: | They perform a thorough evaluation of the non-English capabilities of SoTA LLMs by comparing them on the same set of multilingual datasets. |
| Outcome: | The proposed model outperforms models on multilingual datasets on 22 languages including low-resource African languages. |
Scaling Laws for Multilingual Language Models (2025.findings-acl)
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Yifei He, Alon Benhaim, Barun Patra, Praneetha Vaddamanu, Sanchit Ahuja, Parul Chopra, Vishrav Chaudhary, Han Zhao, Xia Song
| Challenge: | Existing scaling laws for language models are limited to a limited number of languages, but they can be applied to arbitrary number of different languages. |
| Approach: | They propose a scaling law for general-purpose decoder-only language models trained on multilingual data that shifts focus from individual languages to language families. |
| Outcome: | The proposed scaling law can be applied to models trained on multilingual data . it can be used to predict performance across multiple languages and models . |
HYPHEN: Hyperbolic Hawkes Attention For Text Streams (2022.acl-short)
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| Challenge: | Existing methods for text stream modeling ignore fine-grained timing irregularities and time-varying scale-free properties of texts. |
| Approach: | They propose a hyperbolic Hawkes Attention Network which learns a data-driven hyperbolical space and models irregular powerlaw excitations using a Hawke's process. |
| Outcome: | The proposed model can model online text sequences in a geometry agnostic manner. |
DOSA: A Dataset of Social Artifacts from Different Indian Geographical Subcultures (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) are increasingly being integrated with social applications . large data sets are limited in their representation of information and do not capture knowledge from the Web . |
| Approach: | They propose a gamified framework that uses collective sensemaking to collect artifacts from 19 different Indian geographic subcultures and benchmark four popular LLMs. |
| Outcome: | The proposed framework is based on 260 participants from 19 different Indian geographic subcultures and shows that it can be used across regional sub-cultures. |