## 4 Discussion
### Input-Output Formats for Prescribed Tasks  
The capabilities of text-to-text generative AI models encompass a wide range of tasks. These models excel in multilingual communication by enabling accurate translation of text from one language to another [39; 108; 132]. They also offer a unique tool for design and personalization, allowing the generation of handwritten characters in various font styles based on text input [154]. In the domain of programming, these models contribute to code quality and efficiency by providing precise and meaningful corrections for code issues [45]. Additionally, they possess the ability to offer insights and clarifications by explaining given input statements [121]. Leveraging input texts, these models demonstrate exceptional performance in tasks such as translation, question answering, classification, and summarization [39]. While these models provide valuable functionality, caution must be exercised to prevent misuse, such as generating or cracking passwords [155], emphasizing the need for responsible use and ethical considerations. Furthermore, these models function as conversational AI systems, engaging in interactive conversations, answering follow-up questions, challenging incorrect assumptions, and rejecting inappropriate requests [111]. They also streamline the writing process by generating drafts, suggesting edits, and providing explanations for their actions, thus assisting with various writing tasks [34]. Lastly, by leveraging historical data, these models contribute to market analysis and forecasting, enabling the prediction of future market trends [156; 157; 158]. A wide range of tasks can be accomplished in the areas of text-to-speech/audio, speech-to-speech, music-to-text, and audio-to-text generation. These tasks include generating audio from text using techniques such as w2v-BERT [120] and music from text using Jukebox (vector quantized variational autoencoder, VQ-VAE) [145]. Speech synthesis methods like adaptive text to speech (AdaSpeech) [113] allow for the customization of voices and the conversion of text into human-like speech. Notable approaches like denoising diffusion model for text to speech (Diff-TTS) [63], Grad-TTS [152], ProDiff [53], DiffGenerative adversarial networks (GAN)-TTS [128], and pixel convolutional neural network (Wavenet [123]) offer advancements in text to human-like speech synthesis. Additionally, generative AI models can generate robotic voice speech from text using methods like feed-forward transformer (FFT). Within the domain of music/audio, techniques such as generative adversarial networks synth (GANSynth) [153] enable the creation of high-quality, synthetic musical audio clips. Speech enhancement techniques, such as conditional generative adversarial networks (CGAN) with the Pix2 Pix framework [82], can be employed to improve speech quality. Furthermore, there are models like MusCaps [48] for generating captions for music audio and contrastive language-audio pretraining (CLAP) for generating captions for audio [47]. These diverse tasks demonstrate the vast potential and versatility of generative AI models in various audio and speech-related applications. It is essential to address the range of tasks that generative AI models can accomplish in the field of code generation. These tasks include generating valid programming code using natural language descriptions, which can be achieved through models such as CodeBERT [114], CODEGEN [50], CodeT5 [115], and GPT language model (Codex) [116]. These models excel in converting natural language descriptions into executable code. Additionally, the Alphacode [35] model is specifically designed to generate competition-level code, demonstrating its capability to produce high-quality code solutions. Furthermore, models like code-understanding BERT (CuBERT) [110] contribute to code completion, bug detection, and code summarization, providing valuable assistance to developers in understanding and optimizing their code.