Papers with OSCaR
OSCaR: Object State Captioning and State Change Representation (2024.findings-naacl)
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| Challenge: | Existing methods to extrapolate and comprehend changes in object states are limited . relying on a small set of symbolic words to represent changes has restricted expressiveness of language. |
| Approach: | They propose a dataset and benchmark to evaluate multimodal large language models . they investigate causal relations between a concrete action and the change . |
| Outcome: | The proposed method achieves near parity with GPT-4V ratings across helpfulness, accuracy, reasoning, and other key metrics. |
OSCaR: Orthogonal Subspace Correction and Rectification of Biases in Word Embeddings (2021.emnlp-main)
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| Challenge: | Existing methods to mitigate stereotypical biases by linear projection are too aggressive . existing methods remove bias, but they also erase valuable information from word embeddings . |
| Approach: | They propose a bias-mitigating method that disentangles biased associations between concepts instead of removing concepts wholesale. |
| Outcome: | The proposed method disentangles biased associations between concepts rather than eliminating concepts wholesale. |