Summary of In-context Transfer Learning: Demonstration Synthesis by Transferring Similar Tasks, By Dingzirui Wang et al.
In-Context Transfer Learning: Demonstration Synthesis by Transferring Similar Tasks
by Dingzirui Wang, Xuanliang Zhang, Qiguang Chen, Longxu Dou, Xiao Xu, Rongyu Cao, Yingwei Ma, Qingfu Zhu, Wanxiang Che, Binhua Li, Fei Huang, Yongbin Li
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes a novel approach called In-Context Transfer Learning (ICTL) to help large language models (LLMs) adapt to various tasks by synthesizing demonstrations. The authors recognize that current methods for generating demonstrations from scratch are limited by the capabilities and knowledge of LLMs, so they draw inspiration from transfer learning. ICTL consists of two steps: source sampling and target transfer. In the first step, an optimization objective is defined to minimize transfer error and sample source demonstrations similar to the target task. Then, a language model is employed to transfer the sampled source demonstrations to the target task, matching its definition and format. The authors evaluate their method on Super-NI and find that it outperforms synthesis from scratch by 2.0% on average. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps large language models learn new tasks better. It’s like teaching a student by showing them how someone else does the same task, rather than trying to teach them everything from scratch. The researchers came up with an idea called In-Context Transfer Learning (ICTL) that uses this approach to help the model learn. They took two steps: first, they found examples of similar tasks and sampled the best ones to use as a guide. Then, they used a special kind of computer program to adapt those examples to the new task. The results showed that ICTL worked better than trying to generate everything from scratch. |
Keywords
» Artificial intelligence » Language model » Optimization » Transfer learning
