Large Language Models (GPT) Struggle to Answer Multiple-Choice Questions about Code

• URL: http://arxiv.org/abs/2303.08033v1
• Date: Thu, 9 Mar 2023 16:52:12 GMT
• Title: Large Language Models (GPT) Struggle to Answer Multiple-Choice Questions about Code
• Authors: Jaromir Savelka, Arav Agarwal, Christopher Bogart, Majd Sakr
• Abstract summary: We analyzed effectiveness of three generative pre-trained transformer (GPT) models in answering multiple-choice question (MCQ) assessments. These findings can be leveraged by educators to adapt their instructional practices and assessments in programming courses.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We analyzed effectiveness of three generative pre-trained transformer (GPT) models in answering multiple-choice question (MCQ) assessments, often involving short snippets of code, from introductory and intermediate programming courses at the postsecondary level. This emerging technology stirs countless discussions of its potential uses (e.g., exercise generation, code explanation) as well as misuses in programming education (e.g., cheating). However, the capabilities of GPT models and their limitations to reason about and/or analyze code in educational settings have been under-explored. We evaluated several OpenAI's GPT models on formative and summative MCQ assessments from three Python courses (530 questions). We found that MCQs containing code snippets are not answered as successfully as those that only contain natural language. While questions requiring to fill-in a blank in the code or completing a natural language statement about the snippet are handled rather successfully, MCQs that require analysis and/or reasoning about the code (e.g., what is true/false about the snippet, or what is its output) appear to be the most challenging. These findings can be leveraged by educators to adapt their instructional practices and assessments in programming courses, so that GPT becomes a valuable assistant for a learner as opposed to a source of confusion and/or potential hindrance in the learning process.

Related papers

• Genetic Auto-prompt Learning for Pre-trained Code Intelligence Language Models [54.58108387797138]
  We investigate the effectiveness of prompt learning in code intelligence tasks. Existing automatic prompt design methods are very limited to code intelligence tasks. We propose Genetic Auto Prompt (GenAP) which utilizes an elaborate genetic algorithm to automatically design prompts.
  arXiv  Detail & Related papers  (2024-03-20T13:37:00Z)
• Code Prompting Elicits Conditional Reasoning Abilities in Text+Code LLMs [65.2379940117181]
  We introduce code prompting, a chain of prompts that transforms a natural language problem into code. We find that code prompting exhibits a high-performance boost for multiple LLMs. Our analysis of GPT 3.5 reveals that the code formatting of the input problem is essential for performance improvement.
  arXiv  Detail & Related papers  (2024-01-18T15:32:24Z)
• Code Generation Based Grading: Evaluating an Auto-grading Mechanism for "Explain-in-Plain-English" Questions [0.0]
  "Code Generation Based Grading" (CGBG) achieves moderate agreement with human graders. CGBG achieves moderate agreement with human graders with respect to low-level and line-by-line descriptions of code.
  arXiv  Detail & Related papers  (2023-11-25T02:45:00Z)
• Unmasking the giant: A comprehensive evaluation of ChatGPT's proficiency in coding algorithms and data structures [0.6990493129893112]
  We evaluate ChatGPT's ability to generate correct solutions to the problems fed to it, its code quality, and nature of run-time errors thrown by its code. We look into patterns in the test cases passed in order to gain some insights into how wrong ChatGPT code is in these kinds of situations.
  arXiv  Detail & Related papers  (2023-07-10T08:20:34Z)
• Thrilled by Your Progress! Large Language Models (GPT-4) No Longer Struggle to Pass Assessments in Higher Education Programming Courses [0.0]
  GPT models evolved from completely failing the typical programming class' assessments to confidently passing the courses with no human involvement. This study provides evidence that programming instructors need to prepare for a world in which there is an easy-to-use technology that can be utilized by learners to collect passing scores.
  arXiv  Detail & Related papers  (2023-06-15T22:12:34Z)
• Can Generative Pre-trained Transformers (GPT) Pass Assessments in Higher Education Programming Courses? [6.2122699483618]
  We evaluated the capability of generative pre-trained transformers (GPT) to pass assessments in Python programming courses at the postsecondary level. We studied if and how successfully GPT models leverage feedback provided by an auto-grader. It is clear that a straightforward application of these easily accessible models could enable a learner to obtain a non-trivial portion of the overall available score.
  arXiv  Detail & Related papers  (2023-03-16T13:58:45Z)
• Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering [124.16250115608604]
  We present Science Question Answering (SQA), a new benchmark that consists of 21k multimodal multiple choice questions with a diverse set of science topics and annotations of their answers with corresponding lectures and explanations. We show that SQA improves the question answering performance by 1.20% in few-shot GPT-3 and 3.99% in fine-tuned UnifiedQA. Our analysis further shows that language models, similar to humans, benefit from explanations to learn from fewer data and achieve the same performance with just 40% of the data.
  arXiv  Detail & Related papers  (2022-09-20T07:04:24Z)
• Automatic Generation of Programming Exercises and Code Explanations with Large Language Models [4.947560475228859]
  OpenAI Codex is a recent large language model from the GPT-3 family for translating code into natural language. We explore the natural language generation capabilities of Codex in two different phases of the life of a programming exercise. We find the majority of this automatically generated content both novel and sensible, and in many cases ready to use as is.
  arXiv  Detail & Related papers  (2022-06-03T11:00:43Z)
• ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback [54.142719510638614]
  In this paper, we frame the problem of providing feedback as few-shot classification. A meta-learner adapts to give feedback to student code on a new programming question from just a few examples by instructors. Our approach was successfully deployed to deliver feedback to 16,000 student exam-solutions in a programming course offered by a tier 1 university.
  arXiv  Detail & Related papers  (2021-07-23T22:41:28Z)
• Measuring Coding Challenge Competence With APPS [54.22600767666257]
  We introduce APPS, a benchmark for code generation. Our benchmark includes 10,000 problems, which range from having simple one-line solutions to being substantial algorithmic challenges. Recent models such as GPT-Neo can pass approximately 15% of the test cases of introductory problems.
  arXiv  Detail & Related papers  (2021-05-20T17:58:42Z)
• Multi-hop Question Generation with Graph Convolutional Network [58.31752179830959]
  Multi-hop Question Generation (QG) aims to generate answer-related questions by aggregating and reasoning over multiple scattered evidence from different paragraphs. We propose Multi-Hop volution Fusion Network for Question Generation (MulQG), which does context encoding in multiple hops. Our proposed model is able to generate fluent questions with high completeness and outperforms the strongest baseline by 20.8% in the multi-hop evaluation.
  arXiv  Detail & Related papers  (2020-10-19T06:15:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.