Summary of Batch-icl: Effective, Efficient, and Order-agnostic In-context Learning, by Kaiyi Zhang et al.
Batch-ICL: Effective, Efficient, and Order-Agnostic In-Context Learning
by Kaiyi Zhang, Ang Lv, Yuhan Chen, Hansen Ha, Tao Xu, Rui Yan
First submitted to arxiv on: 12 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 presents a novel approach to in-context learning (ICL) by treating it as a meta-optimization process. The authors demonstrate that large language models (LLMs) are sensitive to the order of ICL examples, which leads to the development of Batch-ICL, an efficient and order-agnostic inference algorithm for ICL. Unlike standard N-shot learning approaches, Batch-ICL employs separate 1-shot forward computations and aggregates meta-gradients to generate final predictions. The authors show that Batch-ICL consistently outperforms most permutations of ICL examples and reduces computational resources required. Additionally, a novel variant of Batch-ICL featuring multiple “epochs” of meta-optimization is proposed, further enhancing ICL performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how we can make language models work better with fewer training examples. It shows that these models are sensitive to the order in which they see these examples. The authors create a new way to use these models called Batch-ICL, which ignores the order of the examples and does a better job than usual ways of doing it. They also show that this method is faster and uses less computer power. Additionally, they propose a new version of this method that works even better. |
Keywords
* Artificial intelligence * 1 shot * Inference * N shot * Optimization
