Augmenting In-Context-Learning in LLMs via Automatic Data Labeling and Refinement

• URL: http://arxiv.org/abs/2410.10348v1
• Date: Mon, 14 Oct 2024 10:06:58 GMT
• Title: Augmenting In-Context-Learning in LLMs via Automatic Data Labeling and Refinement
• Authors: Joseph Shtok, Amit Alfassy, Foad Abo Dahood, Eliyahu Schwartz, Sivan Doveh, Assaf Arbelle,
• Abstract summary: We propose Automatic Data Labeling and Refinement (ADLR) to automatically generate and filter demonstrations. We demonstrate the advantage of ADLR in code-based table QA and mathematical reasoning, achieving up to a 5.5% gain.
• Score: 7.108002571622824
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It has been shown that Large Language Models' (LLMs) performance can be improved for many tasks using Chain of Thought (CoT) or In-Context Learning (ICL), which involve demonstrating the steps needed to solve a task using a few examples. However, while datasets with input-output pairs are relatively easy to produce, providing demonstrations which include intermediate steps requires cumbersome manual work. These steps may be executable programs, as in agentic flows, or step-by-step reasoning as in CoT. In this work, we propose Automatic Data Labeling and Refinement (ADLR), a method to automatically generate and filter demonstrations which include the above intermediate steps, starting from a small seed of manually crafted examples. We demonstrate the advantage of ADLR in code-based table QA and mathematical reasoning, achieving up to a 5.5% gain. The code implementing our method is provided in the Supplementary material and will be made available.

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