Summary of Self-amplify: Improving Small Language Models with Self Post Hoc Explanations, by Milan Bhan and Jean-noel Vittaut and Nicolas Chesneau and Marie-jeanne Lesot
Self-AMPLIFY: Improving Small Language Models with Self Post Hoc Explanations
by Milan Bhan, Jean-Noel Vittaut, Nicolas Chesneau, Marie-Jeanne Lesot
First submitted to arxiv on: 19 Feb 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 The paper proposes Self-AMPLIFY, a novel approach for automatically generating natural language rationales from post-hoc explanation methods applied to Small Language Models (SLMs). This method aims to improve the performance of SLMs by leveraging In-Context Learning (ICL) with rationales. The 3-step Self-AMPLIFY process targets samples, generates rationales, and builds a final prompt for ICL. The proposed approach is evaluated on four SLMs and five datasets requiring strong reasoning abilities, achieving good results against competitors and leading to significant accuracy improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers have found that giving Large Language Models (LLMs) explanations can make them better at understanding language. However, creating these explanations requires a lot of human work or special machines. This paper proposes an automated way to generate explanations using smaller language models and existing explanation methods. They test their approach on several small language models and datasets and find that it works well. This is important because it could help us make language models better without needing as much human effort. |
Keywords
* Artificial intelligence * Prompt
