Summary of How and Where Does Clip Process Negation?, by Vincent Quantmeyer and Pablo Mosteiro and Albert Gatt
How and where does CLIP process negation?
by Vincent Quantmeyer, Pablo Mosteiro, Albert Gatt
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper builds upon existing benchmarks for testing linguistic understanding in pre-trained vision and language (VL) models, specifically the VALSE benchmark. The authors propose to test VL models’ understanding of negation, a crucial aspect for multimodal models. While existing benchmarks measure model performance, they do not reveal the internal processes involved in arriving at outputs. This paper takes inspiration from the growing literature on model interpretability and focuses on explaining the behavior of VL models on the understanding of negation task using CLIP, a highly influential VL model. The authors localize parts of the encoder that process negation and analyze the role of attention heads in this task. Their contributions include demonstrating the applicability of language model interpretability methods to multimodal models and tasks, providing insights into how CLIP processes negation on the VALSE existence task, and highlighting limitations in the VALSE dataset as a benchmark for linguistic understanding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well computers can understand language that is connected to pictures. They test pre-trained computer models that can do this by giving them tasks like understanding sentences with words that mean “not” or “no”. The authors want to know how these models arrive at their answers, not just how good they are at getting the right answer. They use a special model called CLIP to see what’s happening inside the computer when it understands negation (words that mean “not”). The results show that computers can learn to understand language connected to pictures, and this paper helps us better understand how they do it. |
Keywords
» Artificial intelligence » Attention » Encoder » Language model
