Incorporating Dialect Understanding Into LLM Using RAG and Prompt Engineering Techniques for Causal Commonsense Reasoning

AbstractThe choice of plausible alternatives (COPA) task requires selecting the most plausible outcome from two choices based on understanding the causal relationships presented in a given text.This paper outlines several approaches and model adaptation strategies to the VarDial 2024 DIALECT-COPA shared task, focusing on causal commonsense reasoning in South-Slavic dialects. We utilize and evaluate the GPT-4 model in combination with various prompts engineering and the Retrieval-Augmented Generation (RAG) technique. Initially, we test and compare the performance of GPT-4 with simple and advanced prompts on the COPA task across three dialects: Cerkno, Chakavian and Torlak. Next, we enhance prompts using the RAG technique specifically for the Chakavian and Cerkno dialect. This involves creating an extended Chakavian-English and Cerkno-Slovene lexical dictionary and integrating it into the prompts. Our findings indicate that the most complex approach, which combines an advanced prompt with an injected dictionary, yields the highest performance on the DIALECT-COPA task.