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AI Research
arXiv CS.AI·May 26·5 min read
Language Bias in LVLMs: From In-Depth Analysis to Simple and Effective Mitigation
TL;DR
Language models, particularly Large Vision-Language Models (LVLMs), often exhibit biases that can lead to unfair outcomes. This study introduces a novel mitigation technique that involves fine-tuning LVLMs with balanced datasets to reduce bias.
✦ Why It Matters
Engineers can implement balanced dataset fine-tuning to reduce bias in their AI models effectively.
Key Takeaways
- Apply Language Bias Regularization during instruction tuning.
- Incorporate Language Bias Penalty in DPO training.
- Evaluate model performance across diverse benchmarks.
- Focus on balancing text and visual inputs in training.
How It Works
Language Bias Regularization (LBR) mitigates bias by introducing a regularization term during instruction tuning, ensuring that both visual and textual modalities are considered equally. Language Bias Penalty (LBP) penalizes excessive reliance on language during the DPO training process, encouraging models to integrate visual context more effectively.