International Journal For Multidisciplinary Research

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Evaluating Generative AI: Challenges, Methods, and Future Directions

Author(s) Latha Ramamoorthy
Country United States
Abstract Generative Artificial Intelligence (AI) is transforming industries by producing high-quality text, images, music, and code. Its applications extend to natural language processing, computer vision, and creative arts. However, assessing these systems' performance and impact remains challenging due to their complexity, subjectivity, and open-ended outputs.

This paper comprehensively reviews evaluation methods for generative AI, beginning with its evolution and major applications, including advanced models like GPT, DALL·E, and AlphaCode. It categorizes evaluation approaches into quantitative metrics (such as BLEU and FID) and qualitative methods (human assessment and user-centered testing).

Key challenges, such as subjectivity, bias, and scalability, are explored alongside emerging trends like automated evaluation tools, ethical impact assessments, and multimodal techniques. Through real-world case studies, this paper highlights practical evaluation strategies and their limitations. By integrating current best practices and identifying future research opportunities, this study aims to guide the development of reliable, fair, and comprehensive evaluation frameworks for generative AI systems.
Keywords AI Evaluation, Generative AI, AI Performance Metrics, Human-AI Assessment, Bias in AI
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 1, January-February 2025
Published On 2025-02-20
Cite This Evaluating Generative AI: Challenges, Methods, and Future Directions - Latha Ramamoorthy - IJFMR Volume 7, Issue 1, January-February 2025. DOI 10.36948/ijfmr.2025.v07i01.37182
DOI https://doi.org/10.36948/ijfmr.2025.v07i01.37182
Short DOI https://doi.org/g85svh
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