Unveiling Scoring Processes: Dissecting the Differences between LLMs and Human Graders in Automatic Scoring

• URL: http://arxiv.org/abs/2407.18328v1
• Date: Thu, 4 Jul 2024 22:26:20 GMT
• Title: Unveiling Scoring Processes: Dissecting the Differences between LLMs and Human Graders in Automatic Scoring
• Authors: Xuansheng Wu, Padmaja Pravin Saraf, Gyeong-Geon Lee, Ehsan Latif, Ninghao Liu, Xiaoming Zhai,
• Abstract summary: Large language models (LLMs) have demonstrated strong potential in performing automatic scoring for constructed response assessments. While constructed responses graded by humans are usually based on given grading rubrics, the methods by which LLMs assign scores remain largely unclear. This paper uncovers the grading rubrics that LLMs used to score students' written responses to science tasks and their alignment with human scores.
• Score: 21.7782670140939
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have demonstrated strong potential in performing automatic scoring for constructed response assessments. While constructed responses graded by humans are usually based on given grading rubrics, the methods by which LLMs assign scores remain largely unclear. It is also uncertain how closely AI's scoring process mirrors that of humans, or if it adheres to the same grading criteria. To address this gap, this paper uncovers the grading rubrics that LLMs used to score students' written responses to science tasks and their alignment with human scores. We also examine whether enhancing the alignments can improve scoring accuracy. Specifically, we prompt LLMs to generate analytic rubrics that they use to assign scores and study the alignment gap with human grading rubrics. Based on a series of experiments with various configurations of LLM settings, we reveal a notable alignment gap between human and LLM graders. While LLMs can adapt quickly to scoring tasks, they often resort to shortcuts, bypassing deeper logical reasoning expected in human grading. We found that incorporating high-quality analytical rubrics designed to reflect human grading logic can mitigate this gap and enhance LLMs' scoring accuracy. These results caution against the simplistic application of LLMs in science education and highlight the importance of aligning LLM outputs with human expectations to ensure efficient and accurate automatic scoring.

Related papers

• Can LLMs Replace Human Evaluators? An Empirical Study of LLM-as-a-Judge in Software Engineering [18.766132076075365]
  Large language models (LLMs) have been deployed to tackle various software engineering (SE) tasks like code generation. Pass@k metric requires extensive unit tests and configured environments, and is not suitable for evaluating LLM-generated text. Conventional metrics like BLEU, which measure only lexical rather than semantic similarity, have also come under scrutiny.
  arXiv  Detail & Related papers  (2025-02-10T06:49:29Z)
• Re-evaluating Automatic LLM System Ranking for Alignment with Human Preference [63.03859517284341]
  An automatic evaluation framework aims to rank LLMs based on their alignment with human preferences. An automatic LLM bencher consists of four components: the input set, the evaluation model, the evaluation type and the aggregation method.
  arXiv  Detail & Related papers  (2024-12-31T17:46:51Z)
• Scoring with Large Language Models: A Study on Measuring Empathy of Responses in Dialogues [3.2162648244439684]
  We develop a framework for investigating how effective Large Language Models are at measuring and scoring empathy of responses in dialogues. Our strategy is to approximate the performance of state-of-the-art and fine-tuned LLMs with explicit and explainable features. Our results show that when only using embeddings, it is possible to achieve performance close to that of generic LLMs.
  arXiv  Detail & Related papers  (2024-12-28T20:37:57Z)
• Evaluating Human Alignment and Model Faithfulness of LLM Rationale [66.75309523854476]
  We study how well large language models (LLMs) explain their generations through rationales. We show that prompting-based methods are less "faithful" than attribution-based explanations.
  arXiv  Detail & Related papers  (2024-06-28T20:06:30Z)
• DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation [75.81096662788254]
  Large Language Models (LLMs) are scalable and economical evaluators. The question of how reliable these evaluators are has emerged as a crucial research question. We propose Decompose and Aggregate, which breaks down the evaluation process into different stages based on pedagogical practices.
  arXiv  Detail & Related papers  (2024-05-24T08:12:30Z)
• Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences [11.23629471911503]
  EvalGen provides automated assistance to users in generating evaluation criteria and implementing assertions. A qualitative study finds overall support for EvalGen but underscores the subjectivity and iterative process of alignment. We identify a phenomenon we dub emphcriteria drift: users need criteria to grade outputs, but grading outputs helps users define criteria.
  arXiv  Detail & Related papers  (2024-04-18T15:45:27Z)
• PiCO: Peer Review in LLMs based on the Consistency Optimization [19.130941716491716]
  We use peer-review mechanisms to measure large language models (LLMs) automatically. We formalize it as a constrained optimization problem, intending to maximize the consistency of each LLM's capabilities and scores. We propose three metrics called PEN, CIN, and LIS to evaluate the gap in aligning human rankings.
  arXiv  Detail & Related papers  (2024-02-02T18:49:26Z)
• Human-Instruction-Free LLM Self-Alignment with Limited Samples [64.69906311787055]
  We propose an algorithm that can self-align large language models (LLMs) iteratively without active human involvement. Unlike existing works, our algorithm relies on neither human-crafted instructions nor labeled rewards, significantly reducing human involvement. We show that our method can unlock the LLMs' self-generalization ability to perform alignment with near-zero human supervision.
  arXiv  Detail & Related papers  (2024-01-06T14:00:12Z)
• CLOMO: Counterfactual Logical Modification with Large Language Models [109.60793869938534]
  We introduce a novel task, Counterfactual Logical Modification (CLOMO), and a high-quality human-annotated benchmark. In this task, LLMs must adeptly alter a given argumentative text to uphold a predetermined logical relationship. We propose an innovative evaluation metric, the Self-Evaluation Score (SES), to directly evaluate the natural language output of LLMs.
  arXiv  Detail & Related papers  (2023-11-29T08:29:54Z)
• Evaluating Large Language Models at Evaluating Instruction Following [54.49567482594617]
  We introduce a challenging meta-evaluation benchmark, LLMBar, designed to test the ability of an LLM evaluator in discerning instruction-following outputs. We discover that different evaluators exhibit distinct performance on LLMBar and even the highest-scoring ones have substantial room for improvement.
  arXiv  Detail & Related papers  (2023-10-11T16:38:11Z)

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.