Summary of Instructioncp: a Fast Approach to Transfer Large Language Models Into Target Language, by Kuang-ming Chen et al.
InstructionCP: A fast approach to transfer Large Language Models into target language
by Kuang-Ming Chen, Hung-yi Lee
First submitted to arxiv on: 30 May 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 The paper proposes a novel approach to continual pre-training of large language models for multilingual conversational abilities. The authors introduce Instruction Continual Pre-training (InsCP), which integrates instruction tags into the pre-training process to maintain conversational proficiency while acquiring new languages. This approach is demonstrated to retain conversational and Reinforcement Learning from Human Feedback (RLHF) abilities, as evaluated through language alignment, reliability, and knowledge benchmarks. InsCP requires only 0.1 billion tokens of high-quality instruction-following data, reducing resource consumption. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps large language models talk in many languages by using a special training method called Instruction Continual Pre-training (InsCP). This makes the models better at conversing with people and retaining their ability to learn from humans. The authors tested InsCP and found it works well, making sure the model stays good at chatting and learning. This new way of training is more efficient because it only needs a small amount of high-quality data. |
Keywords
» Artificial intelligence » Alignment » Reinforcement learning from human feedback » Rlhf
