Papers with task-agnostic method
Evidentiality-guided Generation for Knowledge-Intensive NLP Tasks (2022.naacl-main)
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| Challenge: | Existing methods to augment retrieval-augmented generation models with retrievers often rely on spurious cues or generate hallucinations during inference. |
| Approach: | They propose a method to incorporate evidentiality of passages into training a retrieval-augmented generation model. |
| Outcome: | The proposed method outperforms its direct counterpart on all knowledge-intensive tasks. |
Making Pre-trained Language Models Better Few-shot Learners (2021.acl-long)
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| Challenge: | Recent studies show that the GPT-3 model can perform few-shots on language understanding tasks with a natural-language prompt and a few task demonstrations. |
| Approach: | They propose a technique for fine-tuning language models using a few examples . they propose LM-BFF, which uses prompt-based fine-uning and a pipeline for automating prompt generation . |
| Outcome: | The proposed approach outperforms standard fine-tuning procedures on a range of NLP tasks. |