- Fluency: Does the generated text sound natural, grammatically correct, and coherent? This measures how well the AI mimics human writing.
- Relevance:How relevant is the generated text to the input prompt or context? This is particularly important in tasks like question answering or summarization.
- Adequacy: Does the output satisfy the task’s requirements? For instance, does a chatbot answer the user’s question adequately, or does a text summarization model capture the main points of the original text?
• Text Models (e.g., GPT, T5):
• Audio Models (e.g., WaveNet, Tacotron):
- Audio Quality: For audio models like text-to-speech (TTS) or music generation models, this evaluates the clarity, naturalness, and intelligibility of the generated sound.
- Pitch, Tone, and Emotion: Does the generated audio express the desired emotion, pitch variation, or tone? For example, does a TTS model sound empathetic, or does music composition reflect the right mood or style?
• Image Models (e.g., GANs, DALL·E):
- Visual Fidelity: How realistic or convincing is the generated image? Does it resemble real-world objects or scenes, or does it appear synthetic or distorted?
- Detail and Resolution: Are the images clear and detailed, or are they blurry and lacking in definition? High-resolution and well-detailed images are often critical for realistic visual tasks.