title: YINYANG-ALIGN: Benchmarking Contradictory Objectives and Proposing
    Multi-Objective Optimization based DPO for Text-to-Image Alignment
summary: 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.
paper_authors._id: 67a9a7cb6be3ca4a7ede471e
paper_authors.name: Amitava Das
paper_authors._id: 67a9a7cb6be3ca4a7ede471f
paper_authors.name: Yaswanth Narsupalli
paper_authors._id: 67a9a7cb6be3ca4a7ede4720
paper_authors.name: Gurpreet Singh
paper_authors._id: 67a9a7cb6be3ca4a7ede4721
paper_authors.name: Vinija Jain
paper_authors._id: 67a9a7cb6be3ca4a7ede4722
paper_authors.name: Vasu Sharma
paper_authors._id: 67a9a7cb6be3ca4a7ede4723
paper_authors.name: Suranjana Trivedy
paper_authors._id: 67a9a7cb6be3ca4a7ede4724
paper_authors.name: Aman Chadha
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-10T09:49:39.550Z
paper_authors._id: 63a4754927f1f64ed7238dac
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63a4754927f1f64ed7238dac/aH-eJF-31g4vof9jv2gmI.jpeg
paper_authors.fullname: Aman Chadha
paper_authors.type: user
paper_authors.user: amanchadha
paper_authors._id: 67a9a7cb6be3ca4a7ede4725
paper_authors.name: Amit Sheth
paper_title: YINYANG-ALIGN: Benchmarking Contradictory Objectives and Proposing
    Multi-Objective Optimization based DPO for Text-to-Image Alignment
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: Text-to-Image (T2I) systems alignment fidelity Direct Preference Optimization (DPO) YinYangAlign design objectives user prompts misaligned outputs aligned responses visual coherence diversity