Papers by Sahal Shaji Mullappilly
MAviS: A Multimodal Conversational Assistant For Avian Species (2025.emnlp-main)
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Yevheniia Kryklyvets, Mohammed Irfan Kurpath, Sahal Shaji Mullappilly, Jinxing Zhou, Fahad Shahbaz Khan, Rao Muhammad Anwer, Salman Khan, Hisham Cholakkal
| Challenge: | Existing multimodal large language models face challenges when it comes to specialized topics like avian species. |
| Approach: | They propose a large-scale multimodal avian species dataset that integrates image, audio, and text modalities for over 1,000 bird species. |
| Outcome: | The proposed model outperforms the baseline MiniCPM-o-2.6 by a large margin. |
BiMediX2 : Bio-Medical EXpert LMM for Diverse Medical Modalities (2025.findings-emnlp)
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Sahal Shaji Mullappilly, Mohammed Irfan Kurpath, Sara Pieri, Saeed Yahya Alseiari, Shanavas Cholakkal, Khaled M Aldahmani, Fahad Shahbaz Khan, Rao Muhammad Anwer, Salman Khan, Timothy Baldwin, Hisham Cholakkal
| Challenge: | BiMediX2 is a bilingual (Arabic-English) large multimodal model that supports text-based and image-based medical interactions. |
| Approach: | They introduce BiMediX2, a bilingual (Arabic-English) Bio-Medical EXpert Large Multimodal Model that supports text-based and image-based medical interactions. |
| Outcome: | The model outperforms existing models by over 9% in English and more than 20% in Arabic evaluations. |
LLMVoX: Autoregressive Streaming Text-to-Speech Model for Any LLM (2025.findings-acl)
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Sambal Shikhar, Mohammed Irfan Kurpath, Sahal Shaji Mullappilly, Jean Lahoud, Fahad Shahbaz Khan, Rao Muhammad Anwer, Salman Khan, Hisham Cholakkal
| Challenge: | Existing speech-enabled LLMs degrade conversational quality by modifying the LLM, compromising its linguistic capabilities. |
| Approach: | They propose a lightweight 30M-parameter, LLM-agnostic, autoregressive streaming TTS system that generates high-quality speech with low latency. |
| Outcome: | The proposed system achieves a significantly lower word error rate compared to speech-enabled LLMs while operating at comparable latency. |
BiMediX: Bilingual Medical Mixture of Experts LLM (2024.findings-emnlp)
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Sara Pieri, Sahal Shaji Mullappilly, Fahad Khan, Rao Anwer, Salman Khan, Timothy Baldwin, Hisham Cholakkal
| Challenge: | a new bilingual medical mixture of experts LLM is designed for seamless interaction in both English and Arabic. |
| Approach: | They propose a semi-automated English-to-Arabic translation pipeline with human refinement to ensure high-quality translations. |
| Outcome: | The proposed model outperforms state-of-the-art medical LLMs in Arabic and Arabic . it outperformed the generic Arabic-English bilingual LLM, Jais-30B by 10% and 15% . |