Papers with OPT-30B
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. |
Prompt to be Consistent is Better than Self-Consistent? Few-Shot and Zero-Shot Fact Verification with Pre-trained Language Models (2023.findings-acl)
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| Challenge: | Existing methods for few-shot and zero-shot fact verification require a large set of training data. |
| Approach: | They propose a method to prompt pre-trained language models to be consistent to improve the factuality assessment capability of PLMs. |
| Outcome: | The proposed method outperforms state-of-the-art few-shot fact verification models with a small number of unlabeled instances on zero-shot verification. |