Papers with quality models
CombLM: Adapting Black-Box Language Models through Small Fine-Tuned Models (2023.emnlp-main)
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| Challenge: | Methods for adapting language models to new tasks and domains have traditionally assumed white-box access to the model and work by modifying its parameters. |
| Approach: | They propose a method for adapting large language models to new domains and tasks . they fine-tune a small white-box LM and combine it with a large black-box model at the probability level through a network, learned on a smaller validation set. |
| Outcome: | The proposed method improves performance in all cases, while using a domain expert 23x smaller. |
Combining Distant and Direct Supervision for Neural Relation Extraction (N19-1)
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| Challenge: | Existing methods to train relation extraction with distant supervision use noisy labels and implicitly assumes that all the KB facts are mentioned in the text. |
| Approach: | They propose to combine distant supervision data with additional directly-supervised data to train relation extraction models by using sigmoidal attention weights with max pooling. |
| Outcome: | The proposed method achieves state-of-the-art on the widely used FB-NYT dataset. |
Multi-View Cross-Lingual Structured Prediction with Minimum Supervision (2021.acl-long)
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| Challenge: | Existing work on cross-lingual transfer learning focuses on transferring knowledge from high-resource languages to low-resourced ones. |
| Approach: | They propose a multi-view framework that integrates multiple source models into an aggregated source view and transfers it to a target view based on a task-specific model. |
| Outcome: | The proposed framework improves on three structured prediction tasks on 16 datasets. |