Can Large Language Models Make the Grade? An Empirical Study Evaluating LLMs Ability to Mark Short Answer Questions in K-12 Education

• URL: http://arxiv.org/abs/2405.02985v1
• Date: Sun, 5 May 2024 16:11:06 GMT
• Title: Can Large Language Models Make the Grade? An Empirical Study Evaluating LLMs Ability to Mark Short Answer Questions in K-12 Education
• Authors: Owen Henkel, Adam Boxer, Libby Hills, Bill Roberts,
• Abstract summary: This paper presents reports on a series of experiments with a novel dataset evaluating how well Large Language Models can mark (i.e. grade) open text responses to short answer questions. We found that GPT-4, with basic few-shot prompting performed well (Kappa, 0.70) and, importantly, very close to human-level performance (0.75) This research builds on prior findings that GPT-4 could reliably score short answer reading comprehension questions at a performance-level very close to that of expert human raters.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents reports on a series of experiments with a novel dataset evaluating how well Large Language Models (LLMs) can mark (i.e. grade) open text responses to short answer questions, Specifically, we explore how well different combinations of GPT version and prompt engineering strategies performed at marking real student answers to short answer across different domain areas (Science and History) and grade-levels (spanning ages 5-16) using a new, never-used-before dataset from Carousel, a quizzing platform. We found that GPT-4, with basic few-shot prompting performed well (Kappa, 0.70) and, importantly, very close to human-level performance (0.75). This research builds on prior findings that GPT-4 could reliably score short answer reading comprehension questions at a performance-level very close to that of expert human raters. The proximity to human-level performance, across a variety of subjects and grade levels suggests that LLMs could be a valuable tool for supporting low-stakes formative assessment tasks in K-12 education and has important implications for real-world education delivery.

Related papers

• Evaluating the capability of large language models to personalize science texts for diverse middle-school-age learners [0.0]
  GPT-4 was used to profile student learning preferences based on choices made during a training session. For the experimental group, GPT-4 was used to rewrite science texts to align with the student's predicted profile while, for students in the control group, texts were rewritten to contradict their learning preferences.
  arXiv  Detail & Related papers  (2024-08-09T17:53:35Z)
• Annotation Guidelines-Based Knowledge Augmentation: Towards Enhancing Large Language Models for Educational Text Classification [11.69740323250258]
  We propose the Guidelines-based Knowledge Augmentation (AGKA) approach to improve Large Language Models (LLMs) AGKA employs GPT 4.0 to retrieve label definition knowledge from annotation guidelines, and then applies the random under-sampler to select a few typical examples. The study results demonstrate that AGKA can enhance non-fine-tuned LLMs, particularly GPT 4.0 and Llama 3 70B.
  arXiv  Detail & Related papers  (2024-06-03T03:09:01Z)
• Benchmarking Large Language Models on CFLUE -- A Chinese Financial Language Understanding Evaluation Dataset [7.954348293179786]
  We propose CFLUE, a benchmark to assess the capability of large language models (LLMs) across various dimensions. In knowledge assessment, it consists of 38K+ multiple-choice questions with associated solution explanations. In application assessment, it features 16K+ test instances across distinct groups of NLP tasks such as text classification, machine translation, relation extraction, reading comprehension, and text generation.
  arXiv  Detail & Related papers  (2024-05-17T05:03:40Z)
• InFoBench: Evaluating Instruction Following Ability in Large Language Models [57.27152890085759]
  Decomposed Requirements Following Ratio (DRFR) is a new metric for evaluating Large Language Models' (LLMs) ability to follow instructions. We present InFoBench, a benchmark comprising 500 diverse instructions and 2,250 decomposed questions across multiple constraint categories.
  arXiv  Detail & Related papers  (2024-01-07T23:01:56Z)
• LLaMA Beyond English: An Empirical Study on Language Capability Transfer [49.298360366468934]
  We focus on how to effectively transfer the capabilities of language generation and following instructions to a non-English language. We analyze the impact of key factors such as vocabulary extension, further pretraining, and instruction tuning on transfer. We employ four widely used standardized testing benchmarks: C-Eval, MMLU, AGI-Eval, and GAOKAO-Bench.
  arXiv  Detail & Related papers  (2024-01-02T06:29:02Z)
• CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation [87.44350003888646]
  Eval-Instruct can acquire pointwise grading critiques with pseudo references and revise these critiques via multi-path prompting. CritiqueLLM is empirically shown to outperform ChatGPT and all the open-source baselines.
  arXiv  Detail & Related papers  (2023-11-30T16:52:42Z)
• Can LLMs Grade Short-Answer Reading Comprehension Questions : An Empirical Study with a Novel Dataset [0.0]
  This paper investigates the potential for the newest version of Large Language Models (LLMs) to be used in short answer questions for formative assessments. It introduces a novel dataset of short answer reading comprehension questions, drawn from a set of reading assessments conducted with over 150 students in Ghana. The paper empirically evaluates how well various configurations of generative LLMs grade student short answer responses compared to expert human raters.
  arXiv  Detail & Related papers  (2023-10-26T17:05:40Z)
• A Large Language Model Approach to Educational Survey Feedback Analysis [0.0]
  This paper assesses the potential for the large language models (LLMs) GPT-4 and GPT-3.5 to aid in deriving insight from education feedback surveys.
  arXiv  Detail & Related papers  (2023-09-29T17:57:23Z)
• L-Eval: Instituting Standardized Evaluation for Long Context Language Models [91.05820785008527]
  We propose L-Eval to institute a more standardized evaluation for long context language models (LCLMs) We build a new evaluation suite containing 20 sub-tasks, 508 long documents, and over 2,000 human-labeled query-response pairs. Results show that popular n-gram matching metrics generally can not correlate well with human judgment.
  arXiv  Detail & Related papers  (2023-07-20T17:59:41Z)
• Document-Level Machine Translation with Large Language Models [91.03359121149595]
  Large language models (LLMs) can produce coherent, cohesive, relevant, and fluent answers for various natural language processing (NLP) tasks. This paper provides an in-depth evaluation of LLMs' ability on discourse modeling.
  arXiv  Detail & Related papers  (2023-04-05T03:49:06Z)
• ElitePLM: An Empirical Study on General Language Ability Evaluation of Pretrained Language Models [78.08792285698853]
  We present a large-scale empirical study on general language ability evaluation of pretrained language models (ElitePLM) Our empirical results demonstrate that: (1) PLMs with varying training objectives and strategies are good at different ability tests; (2) fine-tuning PLMs in downstream tasks is usually sensitive to the data size and distribution; and (3) PLMs have excellent transferability between similar tasks.
  arXiv  Detail & Related papers  (2022-05-03T14:18:10Z)

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.