Papers by Pratyush Maini
Model-tuning Via Prompts Makes NLP Models Adversarially Robust (2023.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained models are typically adapted to downstream tasks by appending a randomly initialized multilayer perceptron to their topmost representation layer and fine-tuning the entire model on a downstream task. |
| Approach: | They propose to append a multilayer perceptron to a CLS token and fine-tune the entire model on a downstream task. |
| Outcome: | The proposed model-tuning via prompts outperforms adversarial training-based state-of-art defenses by 3.5% and improves against adversarials by 8% over standard methods. |
Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling (2024.acl-long)
Copied to clipboard
| Challenge: | Large language model pre-training is infeasible due to the large compute costs and duration associated with pre- training and the impending scarcity of high-quality data on the web. |
| Approach: | They propose to use an off-the-shelf instruction-tuned model prompted to paraphrase documents on the web in specific styles such as “like Wikipedia” or in “question-answer format” to jointly pre-train LLMs on real and synthetic rephrases. |
| Outcome: | The proposed model speeds up pre-training by 3x on the C4 dataset, and improves perplexity by 50% on average across different subsets of the Pile. |
Why and when should you pool? Analyzing Pooling in Recurrent Architectures (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Various pooling techniques have been shown to improve performance of RNNs on text classification tasks. |
| Approach: | They propose a pooling-based variant that captures interactions among predictive tokens in a sentence. |
| Outcome: | The proposed pooling architecture outperforms non-pooling models on sequence classification tasks. |