Summary of Detecting Multimodal Situations with Insufficient Context and Abstaining From Baseless Predictions, by Junzhang Liu et al.
Detecting Multimodal Situations with Insufficient Context and Abstaining from Baseless Predictions
by Junzhang Liu, Zhecan Wang, Hammad Ayyubi, Haoxuan You, Chris Thomas, Rui Sun, Shih-Fu Chang, Kai-Wei Chang
First submitted to arxiv on: 18 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper presents a comprehensive analysis of Vision-Language Understanding (VLU) benchmarks, revealing a pervasive issue affecting their integrity. Specifically, many samples contain answers that rely on assumptions unsupported by the provided context, leading to biased learning and hallucinations. To address this challenge, the authors collect contextual data for each sample whenever available and train a context selection module to facilitate evidence-based model predictions. The proposed approach demonstrates strong improvements across multiple benchmarks, showcasing its effectiveness in ensuring trustworthy and evidence-based outputs from vision-language models. Additionally, the paper introduces a Context-AwaRe Abstention (CARA) detector to identify samples lacking sufficient context and enhance model accuracy by abstaining from responding if the required context is absent. The CARA detector exhibits generalization to new benchmarks it wasn’t trained on, underscoring its utility for future VLU benchmarks in detecting or cleaning samples with inadequate context. The paper also curates a Context Ambiguity and Sufficiency Evaluation (CASE) set to benchmark the performance of insufficient context detectors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper tackles a critical issue in Vision-Language Understanding (VLU), where many benchmarks contain answers that rely on assumptions unsupported by the provided context. This leads to biased learning and hallucinations in models trained on these data sets. The authors propose an approach to address this challenge, which involves collecting contextual data for each sample whenever available and training a context selection module. The paper also introduces a detector called CARA (Context-AwaRe Abstention) that identifies samples lacking sufficient context and helps models avoid making assumptions. This detector can be used in future VLU benchmarks to detect or clean up samples with inadequate context. Overall, this research is important for ensuring that vision-language models generate trustworthy and evidence-based outputs in complex real-world scenarios. |
Keywords
» Artificial intelligence » Generalization » Language understanding
