title: Understanding Alignment in Multimodal LLMs: A Comprehensive Study
summary: Preference alignment has become a crucial component in enhancing the
  performance of Large Language Models (LLMs), yet its impact in Multimodal Large
  Language Models (MLLMs) remains comparatively underexplored. Similar to
  language models, MLLMs for image understanding tasks encounter challenges like
  hallucination. In MLLMs, hallucination can occur not only by stating incorrect
  facts but also by producing responses that are inconsistent with the image
  content. A primary objective of alignment for MLLMs is to encourage these
  models to align responses more closely with image information. Recently,
  multiple works have introduced preference datasets for MLLMs and examined
  different alignment methods, including Direct Preference Optimization (DPO) and
  Proximal Policy Optimization (PPO). However, due to variations in datasets,
  base model types, and alignment methods, it remains unclear which specific
  elements contribute most significantly to the reported improvements in these
  works. In this paper, we independently analyze each aspect of preference
  alignment in MLLMs. We start by categorizing the alignment algorithms into two
  groups, offline (such as DPO), and online (such as online-DPO), and show that
  combining offline and online methods can improve the performance of the model
  in certain scenarios. We review a variety of published multimodal preference
  datasets and discuss how the details of their construction impact model
  performance. Based on these insights, we introduce a novel way of creating
  multimodal preference data called Bias-Driven Hallucination Sampling (BDHS)
  that needs neither additional annotation nor external models, and show that it
  can achieve competitive performance to previously published alignment work for
  multimodal models across a range of benchmarks.
paper_authors._id: 668557da6c0ca62168747f72
paper_authors.name: Elmira Amirloo
paper_authors._id: 668557da6c0ca62168747f73
paper_authors.name: Jean-Philippe Fauconnier
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paper_authors.fullname: Jean-Philippe Fauconnier
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paper_authors.user: j4kn
paper_authors._id: 668557da6c0ca62168747f74
paper_authors.name: Christoph Roesmann
paper_authors.status: claimed_verified
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paper_authors.fullname: Christoph Roesmann
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paper_authors.user: croesmann
paper_authors._id: 668557da6c0ca62168747f75
paper_authors.name: Christian Kerl
paper_authors._id: 668557da6c0ca62168747f76
paper_authors.name: Rinu Boney
paper_authors._id: 668557da6c0ca62168747f77
paper_authors.name: Yusu Qian
paper_authors.status: admin_assigned
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paper_authors.fullname: Yusu Qian
paper_authors.type: user
paper_authors.user: YusuQian
paper_authors._id: 668557da6c0ca62168747f78
paper_authors.name: Zirui Wang
paper_authors._id: 668557da6c0ca62168747f79
paper_authors.name: Afshin Dehghan
paper_authors._id: 668557da6c0ca62168747f7a
paper_authors.name: Yinfei Yang
paper_authors.status: admin_assigned
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paper_authors._id: 64b762568c632fbca942a405
paper_authors.avatarUrl: /avatars/1eb737ec169967872f1ebf5ff29f1e6b.svg
paper_authors.fullname: Yinfei Yang
paper_authors.type: user
paper_authors.user: yinfeiy
paper_authors._id: 668557da6c0ca62168747f7b
paper_authors.name: Zhe Gan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-03T15:02:24.628Z
paper_authors._id: 644022db21ec7c165d848b6e
paper_authors.avatarUrl: /avatars/d9ed37ec77f2682878b2976a04d2fcd8.svg
paper_authors.fullname: Zhe Gan
paper_authors.type: user
paper_authors.user: zhegan27
paper_authors._id: 668557da6c0ca62168747f7c
paper_authors.name: Peter Grasch
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-03T15:02:30.934Z
paper_authors._id: 6649e0b6f50d471119285ab8
paper_authors.avatarUrl: /avatars/1c93d0e15f2b68281a1a2ea052450a45.svg
paper_authors.fullname: Peter Grasch
paper_authors.type: user
paper_authors.user: petergrasch
paper_title: Understanding Alignment in Multimodal LLMs: A Comprehensive Study
paper_summary: Preference alignment has become a crucial component in enhancing the
  performance of Large Language Models (LLMs), yet its impact in Multimodal Large
  Language Models (MLLMs) remains comparatively underexplored. Similar to
  language models, MLLMs for image understanding tasks encounter challenges like
  hallucination. In MLLMs, hallucination can occur not only by stating incorrect
  facts but also by producing responses that are inconsistent with the image
  content. A primary objective of alignment for MLLMs is to encourage these
  models to align responses more closely with image information. Recently,
  multiple works have introduced preference datasets for MLLMs and examined
  different alignment methods, including Direct Preference Optimization (DPO) and
  Proximal Policy Optimization (PPO). However, due to variations in datasets,
  base model types, and alignment methods, it remains unclear which specific
  elements contribute most significantly to the reported improvements in these
  works. In this paper, we independently analyze each aspect of preference
  alignment in MLLMs. We start by categorizing the alignment algorithms into two
  groups, offline (such as DPO), and online (such as online-DPO), and show that
  combining offline and online methods can improve the performance of the model
  in certain scenarios. We review a variety of published multimodal preference
  datasets and discuss how the details of their construction impact model
  performance. Based on these insights, we introduce a novel way of creating
  multimodal preference data called Bias-Driven Hallucination Sampling (BDHS)
  that needs neither additional annotation nor external models, and show that it
  can achieve competitive performance to previously published alignment work for
  multimodal models across a range of benchmarks.
paper_ai_summary: Preference alignment in Multimodal Large Language Models (MLLMs) is analyzed to understand which elements contribute to improved performance, leading to the introduction of Bias-Driven Hallucination Sampling (BDHS) for creating multimodal preference data.
paper_ai_keywords: Large Language Models (LLMs) Multimodal Large Language Models (MLLMs) preference alignment hallucination Direct Preference Optimization (DPO) Proximal Policy Optimization (PPO) offline methods online methods multimodal preference datasets Bias-Driven Hallucination Sampling (BDHS)