Domain Specific Benchmarks for Evaluating Multimodal Large Language Models
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911014765723648 |
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| author | Anjum, Khizar Arshad, Muhammad Arbab Hayawi, Kadhim Polyzos, Efstathios Tariq, Asadullah Serhani, Mohamed Adel Batool, Laiba Lund, Brady Mannuru, Nishith Reddy Bevara, Ravi Varma Kumar Mahbub, Taslim Akram, Muhammad Zeeshan Shahriar, Sakib |
| author_facet | Anjum, Khizar Arshad, Muhammad Arbab Hayawi, Kadhim Polyzos, Efstathios Tariq, Asadullah Serhani, Mohamed Adel Batool, Laiba Lund, Brady Mannuru, Nishith Reddy Bevara, Ravi Varma Kumar Mahbub, Taslim Akram, Muhammad Zeeshan Shahriar, Sakib |
| contents | Large language models (LLMs) are increasingly being deployed across disciplines due to their advanced reasoning and problem solving capabilities. To measure their effectiveness, various benchmarks have been developed that measure aspects of LLM reasoning, comprehension, and problem-solving. While several surveys address LLM evaluation and benchmarks, a domain-specific analysis remains underexplored in the literature. This paper introduces a taxonomy of seven key disciplines, encompassing various domains and application areas where LLMs are extensively utilized. Additionally, we provide a comprehensive review of LLM benchmarks and survey papers within each domain, highlighting the unique capabilities of LLMs and the challenges faced in their application. Finally, we compile and categorize these benchmarks by domain to create an accessible resource for researchers, aiming to pave the way for advancements toward artificial general intelligence (AGI) |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_12958 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Domain Specific Benchmarks for Evaluating Multimodal Large Language Models Anjum, Khizar Arshad, Muhammad Arbab Hayawi, Kadhim Polyzos, Efstathios Tariq, Asadullah Serhani, Mohamed Adel Batool, Laiba Lund, Brady Mannuru, Nishith Reddy Bevara, Ravi Varma Kumar Mahbub, Taslim Akram, Muhammad Zeeshan Shahriar, Sakib Machine Learning Large language models (LLMs) are increasingly being deployed across disciplines due to their advanced reasoning and problem solving capabilities. To measure their effectiveness, various benchmarks have been developed that measure aspects of LLM reasoning, comprehension, and problem-solving. While several surveys address LLM evaluation and benchmarks, a domain-specific analysis remains underexplored in the literature. This paper introduces a taxonomy of seven key disciplines, encompassing various domains and application areas where LLMs are extensively utilized. Additionally, we provide a comprehensive review of LLM benchmarks and survey papers within each domain, highlighting the unique capabilities of LLMs and the challenges faced in their application. Finally, we compile and categorize these benchmarks by domain to create an accessible resource for researchers, aiming to pave the way for advancements toward artificial general intelligence (AGI) |
| title | Domain Specific Benchmarks for Evaluating Multimodal Large Language Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.12958 |