Papers by Shikhar Bharadwaj
IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages (2024.acl-long)
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| Challenge: | IndicGenBench is the largest benchmark for evaluating large language models on user-facing generation tasks across a diverse set of 29 Indic languages . |
| Approach: | They evaluate large language models on user-facing generation tasks across 29 languages . they use human curation to provide multi-way parallel evaluation data for many under-represented languages a github repository . |
| Outcome: | IndicGenBench is the largest benchmark for evaluating LLMs on user-facing generation tasks across a diverse set of 29 Indic languages covering 13 scripts and 4 language families. |
POWSM: A Phonetic Open Whisper-Style Speech Foundation Model (2026.acl-long)
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Chin-Jou Li, Kalvin Chang, Shikhar Bharadwaj, Eunjung Yeo, Kwanghee Choi, Jian Zhu, David R. Mortensen, Shinji Watanabe
| Challenge: | Phone-level modeling of speech is a common approach to speech recognition, but it relies on task-specific architectures and datasets. |
| Approach: | They propose a phonetic framework capable of performing multiple phone-related tasks . they propose 'Phonetic Open Whisper-style Speech Model' that can perform these tasks together . |
| Outcome: | The proposed model outperforms or matches specialized PR models of similar size while supporting G2P, P2G, and ASR. |
Efficient Constituency Tree based Encoding for Natural Language to Bash Translation (2022.naacl-main)
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| Challenge: | Semantic parsing is one of the central tasks for natural language understanding (NLU). |
| Approach: | They propose a Segmented Invocation Transformer that utilizes the information from the constituency parse tree of the natural language text and Bash command components to generate Bash commands. |
| Outcome: | The proposed method improves the inference time and reduces the model parameters by 1.8x . |
PRiSM: Benchmarking Phone Realization in Speech Models (2026.acl-long)
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Shikhar Bharadwaj, Chin-Jou Li, Yoonjae Kim, Kwanghee Choi, Eunjung Yeo, Ryan Soh-Eun Shim, Hanyu Zhou, Brendon Boldt, Karen Rosero, Kalvin Chang, Darsh Agrawal, Keer Xu, Chao-Han Huck Yang, Jian Zhu, Shinji Watanabe, David R. Mortensen
| Challenge: | Existing evaluations of phone recognition systems only measure surface-level transcription accuracy. |
| Approach: | They propose to standardize transcription-based evaluation and assess downstream utility in clinical, educational, and multilingual settings with transcription and representation probes. |
| Outcome: | The proposed system outperforms LALMs in clinical, educational, and multilingual settings. |