YINYANG-ALIGN: Benchmarking Contradictory Objectives and Proposing
  Multi-Objective Optimization based DPO for Text-to-Image Alignment
Authors: name: Amitava Das, name: Yaswanth Narsupalli, name: Gurpreet Singh, name: Vinija Jain, name: Vasu Sharma, name: Suranjana Trivedy, name: Aman Chadha, name: Amit Sheth

Precise alignment in Text-to-Image (T2I) systems is crucial to ensure that
generated visuals not only accurately encapsulate user intents but also conform
to stringent ethical and aesthetic benchmarks. Incidents like the Google Gemini
fiasco, where misaligned outputs triggered significant public backlash,
underscore the critical need for robust alignment mechanisms. In contrast,
Large Language Models (LLMs) have achieved notable success in alignment.
Building on these advancements, researchers are eager to apply similar
alignment techniques, such as Direct Preference Optimization (DPO), to T2I
systems to enhance image generation fidelity and reliability.
  We present YinYangAlign, an advanced benchmarking framework that
systematically quantifies the alignment fidelity of T2I systems, addressing six
fundamental and inherently contradictory design objectives. Each pair
represents fundamental tensions in image generation, such as balancing
adherence to user prompts with creative modifications or maintaining diversity
alongside visual coherence. YinYangAlign includes detailed axiom datasets
featuring human prompts, aligned (chosen) responses, misaligned (rejected)
AI-generated outputs, and explanations of the underlying contradictions.