ScienceQA experimental results.

<div><p>This paper presents a novel approach to enhancing educational question-answering (Q&A) systems by combining Retrieval-Augmented Generation (RAG) with Large Language Model (LLM) Code Interpreters. Traditional educational Q&A systems face challenges in areas such as knowled...

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Bibliographic Details
Main Author:	Jin Lu (428513) (author)
Other Authors:	Ji Li (207201) (author)
Published:	2025
Subjects:	Medicine Sociology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified solve mathematical problems scienceqa &# 8212 promising path toward large language model identify several challenges handle complex queries hallucination &# 8221 future research aimed complex computational tasks common &# 8220 among tested models 5 consistently showed 15 percentage points 10 &# 8722 execute python code enhancing educational question code execution errors average accuracy improvement code interpreters offers code interpreters achieved external knowledge sources traditional educational q personalized educational q optimizing educational q systems face challenges step logical reasoning approach combining retrieval domains requiring multi code interpreters educational q reasoning accuracy combining retrieval knowledge retrieval step reasoning novel approach step computations combining text specific knowledge knowledge updates scientific reasoning modal reasoning xlink "> work shows thus mitigating synthesizing cross strongest performance significantly improving significantly improve relevant information precise calculations
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ScienceQA experimental results.

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