Summary of Diagnosing and Remedying Knowledge Deficiencies in Llms Via Label-free Curricular Meaningful Learning, by Kai Xiong et al.
Diagnosing and Remedying Knowledge Deficiencies in LLMs via Label-free Curricular Meaningful Learning
by Kai Xiong, Xiao Ding, Li Du, Jiahao Ying, Ting Liu, Bing Qin, Yixin Cao
First submitted to arxiv on: 21 Aug 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 proposed label-free curricular meaningful learning framework (LaMer) tackles the challenge of diagnosing and remediating Large Language Models’ (LLMs) knowledge deficiencies in a label-free setting. LaMer employs relative entropy to diagnose and quantify knowledge deficiencies, then applies curricular meaningful learning to adaptively synthesize augmentation data and design a deficiency remedy strategy. The framework improves various LLMs across seven out-of-distribution reasoning and language understanding benchmarks with only 40% training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LaMer is a new way to help Large Language Models learn better by fixing their mistakes without needing labeled data. It works by finding what the models don’t know, then giving them more information to fill in those gaps. This makes the models better at answering questions and understanding language. LaMer does this all on its own, without needing human teachers or labeled examples. |
Keywords
» Artificial intelligence » Language understanding
