Summary of Constraint Back-translation Improves Complex Instruction Following Of Large Language Models, by Yunjia Qi et al.
Constraint Back-translation Improves Complex Instruction Following of Large Language Models
by Yunjia Qi, Hao Peng, Xiaozhi Wang, Bin Xu, Lei Hou, Juanzi Li
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel technique called constraint back-translation is proposed to improve large language models’ ability to follow complex instructions. This approach leverages existing datasets by adding constraints that are already met by the responses to the instructions. The authors use Llama3-70B-Instruct to generate a high-quality dataset, CRAB, and demonstrate that post-training on CRAB improves multiple backbone LLMs’ performance on instruction-following benchmarks. Additionally, constraint back-translation serves as a useful auxiliary training objective in post-training. This work has implications for the quality of generated data and the development of advanced language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help large language models understand complex instructions is discovered! Currently, these models struggle to follow instructions with many rules. Researchers found that existing datasets already have some of these rules hidden inside them. They used a special technique called constraint back-translation to make the models better at following instructions. This worked really well and made other models better too! The results are important for improving the quality of data generated by language models. |
Keywords
» Artificial intelligence » Translation
