YINYANG-ALIGN: Benchmarking Contradictory Objectives and Proposing
  Multi-Objective Optimization based DPO for Text-to-Image Alignment
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.
67a9a7cb6be3ca4a7ede471e
Amitava Das
67a9a7cb6be3ca4a7ede471f
Yaswanth Narsupalli
67a9a7cb6be3ca4a7ede4720
Gurpreet Singh
67a9a7cb6be3ca4a7ede4721
Vinija Jain
67a9a7cb6be3ca4a7ede4722
Vasu Sharma
67a9a7cb6be3ca4a7ede4723
Suranjana Trivedy
67a9a7cb6be3ca4a7ede4724
Aman Chadha
claimed_verified
2025-02-10T09:49:39.550Z
63a4754927f1f64ed7238dac
https://cdn-avatars.huggingface.co/v1/production/uploads/63a4754927f1f64ed7238dac/aH-eJF-31g4vof9jv2gmI.jpeg
Aman Chadha
user
amanchadha
67a9a7cb6be3ca4a7ede4725
Amit Sheth
YINYANG-ALIGN: Benchmarking Contradictory Objectives and Proposing
  Multi-Objective Optimization based DPO for Text-to-Image Alignment
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.
YinYangAlign is a benchmark framework for measuring alignment fidelity in Text-to-Image systems, addressing design tensions through detailed axiom datasets and techniques like Direct Preference Optimization.
Text-to-Image (T2I) systems alignment fidelity Direct Preference Optimization (DPO) YinYangAlign design objectives user prompts misaligned outputs aligned responses visual coherence diversity