Summary of Itd: Large Language Models Can Teach Themselves Induction Through Deduction, by Wangtao Sun et al.
ItD: Large Language Models Can Teach Themselves Induction through Deduction
by Wangtao Sun, Haotian Xu, Xuanqing Yu, Pei Chen, Shizhu He, Jun Zhao, Kang Liu
First submitted to arxiv on: 9 Mar 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 Induction through Deduction (ItD) framework enables Large Language Models (LLMs) to teach themselves induction through deduction. It consists of two main components: a Deductive Data Generation module and a Naive Bayesian Induction module, which optimize fine-tuning and decoding of LLMs. The approach achieves significant performance improvements on two induction benchmarks, outperforming previous state-of-the-art methods by 36% and 10%, respectively. The effectiveness of ItD is verified across different LLMs and deductors through an ablation study. This paper showcases the potential of LLMs to learn induction through deduction, demonstrating a relative performance improvement of 36% and 10%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Induction through Deduction (ItD) helps Large Language Models (LLMs) learn induction by themselves. It uses two parts: one makes data for learning and another optimizes the model’s fine-tuning. This approach does better than previous methods on two tests, showing that LLMs can get better at induction by learning from deduction. |
Keywords
» Artificial intelligence » Fine tuning
