Papers with task-oriented synthetic passage generation
Task Oriented In-Domain Data Augmentation (2024.emnlp-main)
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| Challenge: | Existing methods for large language models suffer from two major issues: in-domain data are scarce compared with general domain-agnostic data. |
| Approach: | They propose a task-oriented in-domain data augmentation framework that uses in- domain data selection and task-orientated synthetic passage generation to adapt LLMs to two domains: advertisement and math. |
| Outcome: | The proposed framework improves LLM performance by 8% in the advertisement domain and 7.5% in the math domain. |