Papers by Salman Khan
Time Travel: A Comprehensive Benchmark to Evaluate LMMs on Historical and Cultural Artifacts (2025.findings-acl)
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Sara Ghaboura, Ketan Pravin More, Ritesh Thawkar, Wafa Al Ghallabi, Omkar Thawakar, Fahad Shahbaz Khan, Hisham Cholakkal, Salman Khan, Rao Muhammad Anwer
| Challenge: | TimeTravel is a benchmark of 10,250 expert-verified historical artifact samples spanning 266 distinct cultures across 10 major historical regions. |
| Approach: | They evaluate contemporary AI models on TimeTravel, highlighting their strengths and identifying areas for improvement. |
| Outcome: | The timeTravel benchmark covers 266 cultures and 10 major historical regions and aims to establish AI as reliable partner in preserving cultural heritage. |
VANE-Bench: Video Anomaly Evaluation Benchmark for Conversational LMMs (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) have greatly influenced the development of Large Multi-modal Video Models. |
| Approach: | They propose a benchmark to assess the proficiency of Large Multi-modal Video Models (LMMs) in detecting and localizing anomalies and inconsistencies in videos. |
| Outcome: | The proposed benchmark assesses the proficiency of Video-LMMs in detecting and localizing anomalies and inconsistencies in videos. |
Building Trust in Clinical LLMs: Bias Analysis and Dataset Transparency (2025.emnlp-main)
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Svetlana Maslenkova, Clement Christophe, Marco AF Pimentel, Tathagata Raha, Muhammad Umar Salman, Ahmed Al Mahrooqi, Avani Gupta, Shadab Khan, Ronnie Rajan, Praveenkumar Kanithi
| Challenge: | Current dataset curation and bias assessment practices lack transparency . current approaches lack a thorough understanding of how data characteristics influence model behavior . |
| Approach: | They propose a comprehensive bias evaluation framework that integrates general benchmarks with a healthcare-specific methodology to probe for biases in a sensitive healthcare context. |
| Outcome: | The proposed approach to bias evaluation leverages established benchmarks and a healthcare-specific methodology. |
LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs (2025.findings-acl)
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Omkar Thawakar, Dinura Dissanayake, Ketan Pravin More, Ritesh Thawkar, Ahmed Heakl, Noor Ahsan, Yuhao Li, Ilmuz Zaman Mohammed Zumri, Jean Lahoud, Rao Muhammad Anwer, Hisham Cholakkal, Ivan Laptev, Mubarak Shah, Fahad Shahbaz Khan, Salman Khan
| Challenge: | Existing approaches do not emphasize step-wise problem-solving. |
| Approach: | They propose a visual reasoning chain benchmark and a fine-grained reasoning metric that evaluates correctness and logical coherence at each step. |
| Outcome: | The proposed framework outperforms existing models in six benchmarks and is 5x faster during inference scaling. |
Arabic Mini-ClimateGPT : A Climate Change and Sustainability Tailored Arabic LLM (2023.findings-emnlp)
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Sahal Mullappilly, Abdelrahman Shaker, Omkar Thawakar, Hisham Cholakkal, Rao Anwer, Salman Khan, Fahad Khan
| Challenge: | Recent large language models like ChatGPT and Bard excel in a wide variety of NLP tasks but are not specifically tailored for climate related domain specific information. |
| Approach: | They propose a lightweight Arabic Mini-ClimateGPT that is built on an open-source LLM and specifically fine-tuned on a conversational-style instruction tuning curated Arabic dataset Clima500-Instruct. |
| Outcome: | The proposed model surpasses the baseline LLM in 88.3% of cases during ChatGPT-based evaluation and human expert prefers it over other open-source models. |
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. |
Beyond Fine-tuning: Unleashing the Potential of Continuous Pretraining for Clinical LLMs. (2024.findings-emnlp)
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Clement Christophe, Tathagata Raha, Svetlana Maslenkova, Muhammad Umar Salman, Praveenkumar Kanithi, Marco Pimentel, Shadab Khan
| Challenge: | Current approaches to adapting large language models to clinical use-cases are limited. |
| Approach: | They investigate the efficacy of four techniques in adapting large language models for clinical use-cases. |
| Outcome: | The proposed techniques show that they improve performance across clinical tasks. |
Promptception: How Sensitive Are Large Multimodal Models to Prompts? (2025.findings-emnlp)
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| Challenge: | despite the success of Large Multimodal Models, prompt design for MCQA remains poorly understood. |
| Approach: | They propose a framework for evaluating prompt sensitivity in LMMs . they propose 61 prompt types, each targeting specific aspects of prompt formulation . |
| Outcome: | The proposed framework evaluates 10 LMMs across 3 MCQA benchmarks. |
Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Framework (2026.acl-long)
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| Challenge: | Recent advances in large language models have demonstrated strong potential for understanding user intent . paper describes system architecture, agent roles, retrieval and scoring methods, knowledge graph schema, and evaluation interfaces . |
| Approach: | They propose a multi-agent research discovery and analysis system that integrates multiple agents to reduce the effort required to find, assess, organize, and understand academic literature. |
| Outcome: | The proposed system reduces the effort required to find, assess, organize, and understand academic literature. |
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models (2024.acl-long)
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| Challenge: | a surge of deep learning applications for video understanding have led to major advancements in video-related tasks. |
| Approach: | They propose a multimodal video-based conversation model that merges a video-adapted visual encoder with an LLM and a dataset that is easily scalable and robust to label noise. |
| Outcome: | The proposed model can understand and generate detailed conversations about videos. |
CAMEL-Bench: A Comprehensive Arabic LMM Benchmark (2025.findings-naacl)
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Sara Ghaboura, Ahmed Heakl, Omkar Thawakar, Ali Husain Salem Abdulla Alharthi, Ines Riahi, Abduljalil Radman, Jorma Laaksonen, Fahad Shahbaz Khan, Salman Khan, Rao Muhammad Anwer
| Challenge: | Recent years have witnessed a significant interest in developing large multimodal models capable of performing various visual reasoning and understanding tasks. |
| Approach: | They propose to use Arabic as a language to evaluate large multi-modal models capable of performing visual reasoning and understanding tasks. |
| Outcome: | The proposed benchmark comprises eight diverse domains and 38 sub-domains to represent a large population of over 400 million speakers. |
Fann or Flop: A Multigenre, Multiera Benchmark for Arabic Poetry Understanding in LLMs (2025.emnlp-main)
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Wafa Al Ghallabi, Ritesh Thawkar, Sara Ghaboura, Ketan Pravin More, Omkar Thawakar, Hisham Cholakkal, Salman Khan, Rao Muhammad Anwer
| Challenge: | a benchmark is designed to assess the comprehension of Arabic poetry by large language models in 12 historical eras. |
| Approach: | They propose a benchmark to assess the comprehension of Arabic poetry by large language models in 12 historical eras. |
| Outcome: | The benchmark assesses the comprehension of Arabic poetry by large language models in 12 historical eras. |
KITAB-Bench: A Comprehensive Multi-Domain Benchmark for Arabic OCR and Document Understanding (2025.findings-acl)
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Ahmed Heakl, Muhammad Abdullah Sohail, Mukul Ranjan, Rania Elbadry, Ghazi Shazan Ahmad, Mohamed El-Geish, Omar Maher, Zhiqiang Shen, Fahad Shahbaz Khan, Salman Khan
| Challenge: | Optical Character Recognition (OCR) is a key component of document processing . Arabic text recognition has complex typographic and calligraphic features . |
| Approach: | They propose a comprehensive Arabic OCR benchmark that fills the gaps in evaluation systems. |
| Outcome: | The proposed benchmark outperforms existing models in Arabic by 60% in the character error rate . the best model achieves only 65% accuracy in PDF-to-Markdown conversion . |
DuwatBench: Bridging Language and Visual Heritage through an Arabic Calligraphy Benchmark for Multimodal Understanding (2026.eacl-long)
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Shubham Patle, Sara Ghaboura, Hania Tariq, Mohammad Usman Khan, Omkar Thawakar, Rao Muhammad Anwer, Salman Khan
| Challenge: | a benchmark of 1,272 samples containing about 1,475 unique words is available for Arabic calligraphy . the dataset reflects real-world challenges in Arabic writing, such as calligraphic variation and artistic distortions . |
| Approach: | They evaluated 13 leading Arabic and multilingual multimodal models and paired them with sentence-level annotations to evaluate their calligraphy models. |
| Outcome: | The benchmark evaluates 13 leading Arabic and multilingual multimodal models . it shows they struggle with calligraphic variation, artistic distortions, and precise visual–text alignment. |
CASS: Nvidia to AMD Transpilation with Data, Models, and Benchmark (2026.acl-long)
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Ahmed Heakl, Gustavo Bertolo Stahl, Sarim Hashmi, Seung Hun Eddie Han, Mukul Ranjan, Arina Kharlamova, Salman Khan, Abdulrahman Mahmoud
| Challenge: | Cross-architecture GPU code translation is essential for unlocking low-level hardware portability, yet no scalable solution exists. |
| Approach: | They propose a dataset and model suite for source- and assembly-level GPU code translation that trains domain-specific translation models that achieve 88.2% accuracy on CUDA HIP and 69.1% on SASS RDNA3 . |
| Outcome: | The proposed model achieves 88.2% accuracy on CUDA HIP and 69.1% on SASS RDNA3 outperforming commercial baselines including GPT-5.1, Claude-4.5, and Hipify by wide margins. |
GCA Framework: A GCC Countries–Grounded Dataset and Agentic Pipeline for Climate Decision Support (2026.acl-long)
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| Challenge: | Climate decision support systems are weak in region-specific climate knowledge and interaction with geospatial and forecasting tools. |
| Approach: | They propose a framework that unifies a curated multimodal dataset and a tool-augmented agent for climate analysis. |
| Outcome: | The proposed framework improves reliability over general-purpose models on climate tasks in the Gulf region. |
Waste-Bench: A Comprehensive Benchmark for Evaluating VLLMs in Cluttered Environments (2025.emnlp-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have paved the way for VisionLarge Language Model (VLLM) capabilities have not been thoroughly explored in cluttered datasets where there is complex environment having deformedshaped objects. |
| Approach: | They propose a dataset specifically designed for waste classification in real-world scenarios, characterized by complex environments and deformed shaped objects. |
| Outcome: | The proposed dataset provides valuable insights into the performance of VLLMs under challenging conditions. |
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% . |
AgriCLIP: Adapting CLIP for Agriculture and Livestock via Domain-Specialized Cross-Model Alignment (2025.coling-main)
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Umair Nawaz, Awais Muhammad, Hanan Gani, Muzammal Naseer, Fahad Shahbaz Khan, Salman Khan, Rao Anwer
| Challenge: | Recent studies have addressed this problem by building domain-specialized image-text data. |
| Approach: | They propose a vision-language foundational model dedicated to agriculture and livestock . they propose combining contrastive and self-supervised learning to learn fine-grained features . |
| Outcome: | The proposed model achieves 9.07% gain over standard CLIP training on 20 tasks. |
A Culturally-diverse Multilingual Multimodal Video Benchmark & Model (2025.emnlp-main)
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Bhuiyan Sanjid Shafique, Ashmal Vayani, Muhammad Maaz, Hanoona Abdul Rasheed, Dinura Dissanayake, Mohammed Irfan Kurpath, Yahya Hmaiti, Go Inoue, Jean Lahoud, Md. Safirur Rashid, Shadid Intisar Quasem, Maheen Fatima, Franco Vidal, Mykola Maslych, Ketan Pravin More, Sanoojan Baliah, Hasindri Watawana, Yuhao Li, Fabian Farestam, Leon Schaller, Roman Tymtsiv, Simon Weber, Hisham Cholakkal, Ivan Laptev, Shin’ichi Satoh, Michael Felsberg, Mubarak Shah, Salman Khan, Fahad Shahbaz Khan
| Challenge: | Large multimodal models have gained attention for their effectiveness to understand and generate descriptions of visual content. |
| Approach: | They propose a multilingual Video LMM benchmark to evaluate video LMMs across 14 languages . they also introduce a machine translated multilingual video training set . |
| Outcome: | The proposed video LMM benchmark is designed to evaluate video Lmms across 14 languages including Arabic, Bengali, Chinese, English, French, German, Hindi, Japanese, Russian, Sinhala, Spanish, Swedish, Tamil, and Urdu. |