Summary of Cem: a Data-efficient Method For Large Language Models to Continue Evolving From Mistakes, by Haokun Zhao and Haixia Han and Jie Shi and Chengyu Du and Jiaqing Liang and Yanghua Xiao
CEM: A Data-Efficient Method for Large Language Models to Continue Evolving From Mistakes
by Haokun Zhao, Haixia Han, Jie Shi, Chengyu Du, Jiaqing Liang, Yanghua Xiao
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 This paper tackles the challenge of keeping Large Language Models (LLMs) current and addressing their shortcomings through Continual Learning (CL). The authors propose the Continue Evolving from Mistakes (CEM) method, a data-efficient approach that iteratively evaluates and supplements LLMs’ performance by incorporating mistake-relevant knowledge. To optimize data usage and mitigate forgetting, they introduce a novel training paradigm combining continual instruction tuning (CIT) and continual pre-training (CPT). The authors demonstrate the effectiveness of CEM on multiple models, achieving gains of up to 29.63% on both in-domain and out-of-domain QA tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Continual Learning is important because it helps Large Language Models stay current with new information. This process involves two main steps: continual instruction tuning (CIT) and continual pre-training (CPT). The problem is that collecting enough data for CPT and filling knowledge gaps can be difficult. To solve this, the authors created a new method called Continue Evolving from Mistakes (CEM). CEM helps LLMs learn from their mistakes and get better over time. It’s like how humans learn by correcting their own mistakes. |
Keywords
» Artificial intelligence » Continual learning » Instruction tuning
