Papers with pre-training procedure
DILBERT: Customized Pre-Training for Domain Adaptation with Category Shift, with an Application to Aspect Extraction (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for pre-training can be sub-optimal in some cases . for example, aspect extraction tasks require domain and category invariant representations . |
| Approach: | They propose a domain-invariant learning scheme for BERT to fine-tune pre-trained language models on a source domain and then apply it to a different target domain. |
| Outcome: | The proposed scheme improves performance over state-of-the-art models while using fraction of the unlabeled data. |
Open Domain Question Answering over Tables via Dense Retrieval (2021.naacl-main)
Copied to clipboard
| Challenge: | Recent advances in open-domain QA focus on retrieving textual passages . a retriever designed to handle tabular context can improve retrieval quality . |
| Approach: | They propose a tabular-based retrieval model that improves retrieval quality over a BERT-based retriever. |
| Outcome: | The proposed retriever improves retrieval quality with mined hard negatives over a BERT-based retriever. |
CLIP Models are Few-Shot Learners: Empirical Studies on VQA and Visual Entailment (2022.acl-long)
Copied to clipboard
| Challenge: | Previously, CLIP was only regarded as a powerful visual encoder. |
| Approach: | They propose a parameter-efficient fine-tuning strategy to boost CLIP's few-shot performance on a visual entailment task without introducing any additional pre-training procedure. |
| Outcome: | The proposed strategy achieves competitive zero/few-shot results on visual question answering and visual entailment tasks without introducing any additional pre-training procedure. |
Birdie: Advancing State Space Language Modeling with Dynamic Mixtures of Training Objectives (2024.emnlp-main)
Copied to clipboard
| Challenge: | Efficient state space models struggle with tasks requiring in-context retrieval, such as text copying and associative recall, limiting their usefulness in practical settings. |
| Approach: | They propose a training procedure that improves the performance of SSMs on retrieval-intensive tasks such as phone book lookup, long paragraph question-answering, and infilling tasks. |
| Outcome: | The proposed training procedure improves performance on retrieval-intensive tasks that challenge current SSMs, such as phone book lookup, long paragraph question-answering, and infilling tasks. |