Papers by Zhiying Jiang
Operator Selection and Ordering in a Pipeline Approach to Efficiency Optimizations for Transformers (2023.findings-acl)
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| Challenge: | Natural language processing tasks rely on complex neural models . transformer-based models are typically slow to execute, making it a non-trivial challenge to apply them in real-world applications. |
| Approach: | They propose to consider an efficiency method as an operator applied on a model . they find that the commutativity and cumulativeness of efficiency operators are plausible . |
| Outcome: | The proposed method is commutative and cumulative, and the results are estimated by combining methods. |
“Low-Resource” Text Classification: A Parameter-Free Classification Method with Compressors (2023.findings-acl)
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| Challenge: | Text classification is one of the most fundamental tasks in natural language processing (NLP), but deep neural networks are data-hungry and expensive to train. |
| Approach: | They propose a non-parametric alternative to DNNs that uses a compressor like gzip and a k-nearest-neighbor classifier to achieve competitive results. |
| Outcome: | The proposed method outperforms BERT on all five OOD datasets and outperformed other methods on the few-shot setting. |
What the DAAM: Interpreting Stable Diffusion Using Cross Attention (2023.acl-long)
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Raphael Tang, Linqing Liu, Akshat Pandey, Zhiying Jiang, Gefei Yang, Karun Kumar, Pontus Stenetorp, Jimmy Lin, Ferhan Ture
| Challenge: | a new text-image attribution analysis model for text-to-image generation is understudied due to ethical constraints . corporators have restricted the general public from using the models and their weights . |
| Approach: | They perform a text-image attribution analysis on Stable Diffusion, a recently open-sourced model. |
| Outcome: | The proposed method achieves a competitive 58.8-64.8 mIoU on noun segmentation and fair to good mean opinion scores on all parts of speech rated by humans . it also achieves good attribution quality on all part of speech, rated in humans - and the first to interpret large diffusion models from a visuolinguistic perspective. |
Inserting Information Bottlenecks for Attribution in Transformers (2020.findings-emnlp)
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| Challenge: | Pretrained transformers are a popular approach for understanding features important for prediction. |
| Approach: | They apply information bottlenecks to analyze attribution of features for prediction on a black-box model. |
| Outcome: | The proposed method outperforms two competing methods in degradation tests on four datasets. |
Document Ranking with a Pretrained Sequence-to-Sequence Model (2020.findings-emnlp)
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| Challenge: | Experimental results on the MS MARCO passage ranking task show that our ranking approach is superior to strong encoder-only models. |
| Approach: | They propose to use a pretrained sequence-to-sequence model to generate relevance labels as "target tokens" they also show how the underlying logits of these target tokens can be interpreted as relevance probabilities for ranking. |
| Outcome: | The proposed model outperforms existing models in a data-poor setting and significantly outperformed an encoder-only model on the MS MARCO passage ranking task. |
PaperRobot: Incremental Draft Generation of Scientific Ideas (P19-1)
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| Challenge: | a paper robot can read existing papers and create new nodes or links in the knowledge graphs. |
| Approach: | They propose to automate the creation of new ideas by predicting links from the background KGs. |
| Outcome: | The proposed paper automates three tasks: read existing papers, create new ideas, predict links . the paper generated abstracts, conclusion and future work sections, and new titles are chosen over human-written ones up to 30%, 24% and 12% of the time. |