Reducing Text Bias in Synthetically Generated MCQAs for VLMs in Autonomous Driving
arXiv:2602.17677v1 Announce Type: cross Abstract: Multiple Choice Question Answering (MCQA) benchmarks are an established standard for measuring Vision Language Model (VLM) performance in driving tasks. However, we observe the known phenomenon that synthetically generated MCQAs are highly susceptible to hidden textual cues that allow models to exploit linguistic patterns rather than visual context. Our results show that a VLM fine-tuned on such data can achieve accuracy comparable to human-validated benchmarks even without visual input. Our proposed method reduces blind accuracy from +66.9% above random to +2.9%, eliminating the vast majority of exploitable textual shortcuts. By decoupling the correct answer from linguistic artifacts and employing a curriculum learning strategy, we force the model to rely on visual grounding, ensuring that performance accurately reflects perceptual understanding.
arXiv:2602.17677v1 Announce Type: cross Abstract: Multiple Choice Question Answering (MCQA) benchmarks are an established standard for measuring Vision Language Model (VLM) performance in driving tasks. However, we observe the known phenomenon that synthetically generated MCQAs are highly susceptible to hidden textual cues that allow models to exploit linguistic patterns rather than visual context. Our results show that a VLM fine-tuned on such data can achieve accuracy comparable to human-validated benchmarks even without visual input. Our proposed method reduces blind accuracy from +66.9% above random to +2.9%, eliminating the vast majority of exploitable textual shortcuts. By decoupling the correct answer from linguistic artifacts and employing a curriculum learning strategy, we force the model to rely on visual grounding, ensuring that performance accurately reflects perceptual understanding.
Executive Summary
This article presents a novel method for reducing text bias in synthetically generated MCQAs for Vision Language Models (VLMs) in Autonomous Driving tasks. The authors observe that VLMs fine-tuned on such data can achieve high accuracy without relying on visual context, exploiting linguistic patterns instead. Their proposed method, which decouples the correct answer from linguistic artifacts and employs a curriculum learning strategy, significantly reduces blind accuracy, forcing the model to rely on visual grounding. This research has important implications for the development of reliable and transparent VLMs in Autonomous Driving.
Key Points
- ▸ Synthetically generated MCQAs for VLMs in Autonomous Driving are highly susceptible to text bias.
- ▸ Existing methods allow VLMs to exploit linguistic patterns rather than visual context.
- ▸ The proposed method decouples the correct answer from linguistic artifacts and employs a curriculum learning strategy.
Merits
Strength in methodological design
The authors' proposed method is a significant improvement over existing approaches, effectively reducing text bias in synthetically generated MCQAs.
Contribution to VLM research
The research provides valuable insights into the limitations of VLMs in Autonomous Driving tasks and offers a potential solution to improve their performance and reliability.
Demerits
Limited generalizability
The proposed method may not be directly applicable to other tasks or domains, potentially limiting its generalizability and impact.
Need for further evaluation
While the results are promising, further evaluation and testing are necessary to confirm the effectiveness of the proposed method in real-world scenarios.
Expert Commentary
The article presents a thought-provoking and well-structured contribution to the field of VLM research. The proposed method is a significant improvement over existing approaches, and the results are promising. However, the limitations of the research should be acknowledged, particularly the potential lack of generalizability and the need for further evaluation. As the field of AI continues to evolve, it is essential to prioritize explainability, transparency, and fairness in AI systems, particularly in high-stakes applications like Autonomous Driving. The proposed method is a step in the right direction, but more research is needed to fully address the challenges of text bias in synthetically generated data.
Recommendations
- ✓ Further evaluation and testing of the proposed method in real-world scenarios is necessary to confirm its effectiveness.
- ✓ The research community should prioritize the development of methods that can detect and mitigate text bias in synthetically generated data, ensuring the reliability and transparency of VLMs in Autonomous Driving tasks.
