Causal Synthetic Augmentation for Few-Shot Logical Deduction in Large Language Models Versus Standard Back-Translation

Abstract

Abstract: This paper proposes a framework for quantitatively evaluating interactive LLMs such as ChatGPT using publicly available data sets. We carry out an extensive technical evaluation of ChatGPT using 23 data sets covering 8 different common NLP application tasks. We evaluate the multitask, multilingual and multi-modal aspects of ChatGPT based on these data sets and a newly designed multimodal dataset. We find that ChatGPT outperforms LLMs with zero-shot learning on most tasks and even outperforms fine-tuned models on some tasks. We find that it is better at understanding non-Latin script languages

Research Question

How does causal synthetic augmentation affect the few-shot reasoning accuracy of large language models on logical deduction benchmarks compared to standard back-translation methods?

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Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

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