# A Survey of Automatic Evaluation Methods on Text, Visual and Speech Generations

arXiv: https://arxiv.org/abs/2506.10019 · HTML: https://mlsanity.com/paper/2506.10019/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2025.

## Built on by (most cited first, 4 of 4)

- [On the Adversarial Robustness of Multimodal LLM Judges](https://mlsanity.com/paper/2606.15608/) (2026)
- [Benchmarking Multimodal Large Language Models for Missing Modality Completion in Product Catalogues](https://mlsanity.com/paper/2601.19750/) (2026)
- [Near-Oracle KV Selection via Pre-hoc Sparsity for Long-Context Inference](https://mlsanity.com/paper/2602.08329/) (2026)
- [Markovian ODE-guided scoring can assess the quality of offline reasoning traces in language models](https://mlsanity.com/paper/2603.01580/) (2026)

## Abstract (arXiv)

Recent advances in deep learning have significantly enhanced generative AI capabilities across text, images, and audio. However, automatically evaluating the quality of these generated outputs presents ongoing challenges. Although numerous automatic evaluation methods exist, current research lacks a systematic framework that comprehensively organizes these methods across text, visual, and audio modalities. To address this issue, we present a comprehensive review and a unified taxonomy of automatic evaluation methods for generated content across all three modalities; We identify five fundamental paradigms that characterize existing evaluation approaches across these domains. Our analysis begins by examining evaluation methods for text generation, where techniques are most mature. We then extend this framework to image and audio generation, demonstrating its broad applicability. Finally, we discuss promising directions for future research in cross-modal evaluation methodologies.