Summary of Experimental Design For Active Transductive Inference in Large Language Models, by Subhojyoti Mukherjee et al.
Experimental Design for Active Transductive Inference in Large Language Models
by Subhojyoti Mukherjee, Anusha Lalitha, Aniket Deshmukh, Ge Liu, Yifei Ma, Branislav Kveton
First submitted to arxiv on: 12 Apr 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 introduces Active In-context Prompt Design (AIPD), a method to adaptively design prompts for Large Language Models (LLMs) during inference. By leveraging active learning, AIPD selects few-shot examples from a training set that optimize performance on a test set. The approach uses two algorithms, GO and SAL, which differ in their selection methods. The paper demonstrates the effectiveness of AIPD across various tasks using small, medium-sized, and large LLMs, outperforming other methods for choosing few-shot examples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers developed a new way to fine-tune language models at the moment they’re being used. They call it Active In-context Prompt Design (AIPD). It works by picking the most useful examples from a training set and using them to make the model better. Two different methods were tested: GO and SAL. The results showed that these methods worked better than others for selecting which examples to use. |
Keywords
» Artificial intelligence » Active learning » Few shot » Inference » Prompt
