OmniDPO: A Preference Optimization Framework to Address Omni-Modal Hallucination
OmniDPO reduces omni-modal hallucination using a preference optimization framework, enhancing multimodal reasoning.
Key Findings
Methodology
OmniDPO employs a preference alignment framework by constructing text-preference and multimodal-preference sample pairs to enhance the model's understanding of audio-video interactions and attention to visual and auditory information. This method extends the Direct Preference Optimization (DPO) paradigm with conditional preference learning to reduce hallucinations.
Key Results
- On the CMM benchmark, OmniDPO improved Qwen2.5-Omni and MiniCPM-o-2.6 by 3.48% and 4.23%, respectively.
- On AVHBench, OmniDPO significantly enhanced F1 scores, with Qwen2.5-Omni improving by 5.82% and MiniCPM-o-2.6 by 2.64%.
- Eliminated over-reliance on single modalities, enhancing cross-modal reasoning capabilities.
Significance
OmniDPO holds significant value in academia and industry, addressing hallucination issues in omni-modal large language models, especially in high-stakes applications like medical video analysis and autonomous driving, ensuring factual accuracy. Through preference optimization, the model's reasoning ability in multimodal scenarios is significantly enhanced.
Technical Contribution
OmniDPO introduces conditional preference learning, extending the DPO paradigm to achieve audio-video-text alignment in omni-modal scenarios for the first time. This method not only reduces hallucinations but also enhances the model's reasoning capabilities, offering new engineering possibilities.
Novelty
OmniDPO is the first hallucination mitigation method specifically designed for omni-modal scenarios, innovatively combining text and multimodal preference pairs to address the insufficient alignment of audio-video modalities in existing methods.
Limitations
- In complex scenarios, the model may still hallucinate due to subtle audio-video signal differences not being fully captured.
- The construction of preference samples requires substantial computational resources.
Future Work
Future work could explore more efficient preference sample construction methods or incorporate more modalities to further enhance the model's generalization capabilities.
AI Executive Summary
Omni-modal large language models (OLLMs) have excelled in tasks like audio-video understanding and real-time environment perception, yet hallucination remains a challenge. Existing models tend to align visual or auditory modalities with text independently during training, overlooking intrinsic correlations between video and its corresponding audio, leading to hallucinations when reasoning requires interpreting hidden audio cues in video content. OmniDPO addresses this issue by using a preference alignment framework to reduce hallucinations in OLLMs. By constructing text-preference and multimodal-preference sample pairs, the model's understanding of audio-video interactions and attention to visual and auditory information is enhanced. Experiments show that OmniDPO not only effectively mitigates multimodal hallucinations but also significantly enhances the models' reasoning capabilities across modalities. The introduction of OmniDPO ensures factual accuracy in high-stakes applications and provides new directions for future multimodal research.
Deep Analysis
Background
In recent years, omni-modal large language models (OLLMs) have made significant strides in audio-video understanding and real-time environment perception tasks. However, hallucination remains a major challenge. Previous research primarily focused on bimodal (text-image) settings, finding that text encoders' capabilities significantly outweigh visual encoders, leading to dominant textual priors. This phenomenon is also present in omni-modal scenarios, where models tend to rely on textual inputs, neglecting other modalities.
Core Problem
The core issue of omni-modal hallucination lies in models generating outputs inconsistent with visual or auditory inputs. This phenomenon is particularly critical in high-stakes applications, such as medical video analysis or autonomous driving, where factual precision is paramount.
Innovation
OmniDPO addresses omni-modal hallucination through a preference alignment framework. 1) Constructs text-preference sample pairs to enhance understanding of audio-video interactions. 2) Constructs multimodal-preference sample pairs to strengthen attention to visual and auditory information. Unlike existing methods, OmniDPO achieves audio-video-text alignment in omni-modal scenarios for the first time.
Methodology
- �� Construct OMNIDPO-10k dataset with audio-video-text preference pairs. • Use Qwen2-Audio to extract audio content descriptions and combine with video to generate answers. • Introduce noisy variants to create modality preference pairs, enhancing attention to visual and auditory signals. • Combine conditional preference learning to optimize multimodal reasoning capabilities.
Experiments
Experiments were conducted on Qwen2.5-Omni and MiniCPM-o-2.6, evaluated using CMM and AVHBench benchmarks. Full-parameter fine-tuning was adopted, with learning rates of 1e-6 and 1e-5, respectively. Results demonstrate significant enhancement in reasoning capabilities in multimodal scenarios.
Results
OmniDPO improved Qwen2.5-Omni and MiniCPM-o-2.6 by 3.48% and 4.23% on the CMM benchmark. On AVHBench, OmniDPO significantly enhanced F1 scores, with Qwen2.5-Omni improving by 5.82% and MiniCPM-o-2.6 by 2.64%.
Applications
OmniDPO holds significant value in high-stakes applications like medical video analysis and autonomous driving, ensuring factual accuracy by reducing hallucinations.
Limitations & Outlook
OmniDPO may still hallucinate in complex scenarios due to subtle audio-video signal differences not being fully captured. Additionally, constructing preference samples requires substantial computational resources. Future work could explore more efficient methods.
Plain Language Accessible to non-experts
Imagine watching a movie where you need to understand both the visuals and the sounds. Sometimes, the dialogue and background music convey different messages. If you only focus on the dialogue and ignore the music, you might misunderstand the entire scene. OmniDPO acts like a smart movie-watching assistant, helping you pay attention to both the visuals and sounds, ensuring you don't miss any important information. This way, OmniDPO reduces misunderstandings and misjudgments, allowing you to have a more comprehensive and accurate understanding of the movie.
ELI14 Explained like you're 14
Imagine you're playing a game that requires you to pay attention to both the screen and the sounds. Sometimes, there are hidden audio cues that help you find clues to complete the level. If you only look at the screen and ignore the audio cues, you might miss important information. OmniDPO is like a smart game assistant, helping you focus on both the screen and the sounds, making it easier for you to find the clues and complete the game tasks faster and better!
Glossary
Omni-modal
Refers to processing multiple modality inputs simultaneously, such as text, images, and audio.
Used in the paper to describe the model's ability to handle multiple inputs.
Hallucination
The generation of outputs by a model that are inconsistent with the inputs.
In multimodal models, refers to outputs not aligning with visual or auditory inputs.
Preference Optimization
Enhancing model performance by optimizing its preferences for different inputs.
Used to reduce omni-modal hallucinations.
DPO (Direct Preference Optimization)
A method for optimizing model output preferences.
OmniDPO extends the DPO paradigm.
Qwen2.5-VL
A model used for multimodal understanding.
Used to generate answers for audio-video interactions.
Open Questions Unanswered questions from this research
- 1 How to further reduce hallucinations in complex scenarios? Current methods still struggle with capturing subtle audio-video signal differences.
- 2 How to optimize the construction of preference samples to reduce computational resource consumption?
Applications
Immediate Applications
Medical Video Analysis
By reducing hallucinations, ensures factual accuracy in medical video analysis, enhancing diagnostic efficiency.
Long-term Vision
Autonomous Driving
In autonomous driving, accurate interpretation of multimodal information enhances vehicle perception and safety.
Abstract
Recently, Omni-modal large language models (OLLMs) have sparked a new wave of research, achieving impressive results in tasks such as audio-video understanding and real-time environment perception. However, hallucination issues still persist. Similar to the bimodal setting, the priors from the text modality tend to dominate, leading OLLMs to rely more heavily on textual cues while neglecting visual and audio information. In addition, fully multimodal scenarios introduce new challenges. Most existing models align visual or auditory modalities with text independently during training, while ignoring the intrinsic correlations between video and its corresponding audio. This oversight results in hallucinations when reasoning requires interpreting hidden audio cues embedded in video content. To address these challenges, we propose OmniDPO, a preference-alignment framework designed to mitigate hallucinations in OLLMs. Specifically, OmniDPO incorporates two strategies: (1) constructing text-preference sample pairs to enhance the model's understanding of audio-video interactions; and (2) constructing multimodal-preference sample pairs to strengthen the model's attention to visual and auditory information. By tackling both challenges, OmniDPO effectively improves multimodal grounding and reduces hallucination. Experiments conducted on two OLLMs demonstrate that OmniDPO not only effectively mitigates multimodal hallucinations but also significantly enhances the models' reasoning capabilities across modalities. All code and datasets will be released upon paper acceptance.