Summary of Aloha: a New Measure For Hallucination in Captioning Models, by Suzanne Petryk et al.
ALOHa: A New Measure for Hallucination in Captioning Models
by Suzanne Petryk, David M. Chan, Anish Kachinthaya, Haodi Zou, John Canny, Joseph E. Gonzalez, Trevor Darrell
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 A novel multimodal pre-training approach for visual description is proposed, which addresses the issue of state-of-the-art models producing captions with errors such as hallucinated objects. The existing prominent metric for object hallucination, CHAIR, has limitations in its fixed set of MS COCO objects and synonyms. A modernized open-vocabulary metric, ALOHa, leverages large language models (LLMs) to measure object hallucinations by extracting groundable objects from a candidate caption, measuring their semantic similarity to reference objects, and using Hungarian matching to produce a final hallucination score. The proposed approach is shown to correctly identify 13.6% more hallucinated objects than CHAIR on HAT, a new gold-standard subset of MS COCO Captions annotated for hallucinations, and 30.8% more on nocaps, where objects extend beyond MS COCO categories. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to measure how well AI models describe what they see. Currently, AI models can make mistakes like imagining things that aren’t really there. To fix this problem, the authors created a new metric called ALOHa that uses big language models to check if an AI-generated description is accurate or not. They tested their approach on two datasets and found that it was much better at detecting errors than the old method. This could help make AI models better at describing what they see. |
Keywords
* Artificial intelligence * Hallucination
