Papers with synthetic data approach
Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai (2024.acl-srw)
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| Challenge: | Xue et al., 2024) have demonstrated that large language models can perform at human level across multitudes of tasks and domains. |
| Approach: | They propose a seed-free framework for generating synthetic instruction-tuning data that incorporates fluency, diversity, and cultural context. |
| Outcome: | The proposed framework achieves competitive performance using only 5,000 instructions compared to state-of-the-art Thai LLMs trained on hundreds of thousands of instructions. |
Synthesizing Text-to-SQL Data from Weak and Strong LLMs (2024.acl-long)
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| Challenge: | a capability gap exists between open-source and closed-source large language models (LLMs) . the adoption of closed-sourced LLMs introduces concerns pertaining to openness, privacy, and substantial costs. |
| Approach: | They propose a synthetic data approach that combines strong and weak models for error information . they demonstrate the effectiveness of SENSE, a specialized text-to-SQL model . |
| Outcome: | The proposed method enhances the domain generalization of text-to-SQL models and explores the potential of error data supervision through preference learning. |