Related papers: Auditing an Automatic Grading Model with deep Reinforcement Learning

Auditing an Automatic Grading Model with deep Reinforcement Learning

URL: http://arxiv.org/abs/2405.07087v1
Date: Sat, 11 May 2024 20:07:09 GMT
Title: Auditing an Automatic Grading Model with deep Reinforcement Learning
Authors: Aubrey Condor, Zachary Pardos,
Abstract summary: We explore the use of deep reinforcement learning to audit an automatic short answer grading (ASAG) model. We show that a high level of agreement to human ratings does not give sufficient evidence that an ASAG model is infallible.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: We explore the use of deep reinforcement learning to audit an automatic short answer grading (ASAG) model. Automatic grading may decrease the time burden of rating open-ended items for educators, but a lack of robust evaluation methods for these models can result in uncertainty of their quality. Current state-of-the-art ASAG models are configured to match human ratings from a training set, and researchers typically assess their quality with accuracy metrics that signify agreement between model and human scores. In this paper, we show that a high level of agreement to human ratings does not give sufficient evidence that an ASAG model is infallible. We train a reinforcement learning agent to revise student responses with the objective of achieving a high rating from an automatic grading model in the least number of revisions. By analyzing the agent's revised responses that achieve a high grade from the ASAG model but would not be considered a high scoring responses according to a scoring rubric, we discover ways in which the automated grader can be exploited, exposing shortcomings in the grading model.

Related papers

Auto-Evaluation with Few Labels through Post-hoc Regression [4.813376208491175]
Prediction Powered Inference (PPI) framework provides a way of leveraging statistical power of automatic evaluation and a small pool of labelled data. We present two new PPI-based techniques that leverage robust regressors to produce even lower variance estimators in the few-label regime.
arXiv Detail & Related papers (2024-11-19T17:17:46Z)
Self-Judge: Selective Instruction Following with Alignment Self-Evaluation [27.69410513313001]
We study the study of selective instruction following, whereby the system declines to execute instructions if the anticipated response quality is low. We introduce Self-J, a novel self-training framework for developing judge models without needing human-annotated quality scores.
arXiv Detail & Related papers (2024-09-02T04:14:13Z)
QualEval: Qualitative Evaluation for Model Improvement [82.73561470966658]
We propose QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement. QualEval uses a powerful LLM reasoner and our novel flexible linear programming solver to generate human-readable insights. We demonstrate that leveraging its insights, for example, improves the absolute performance of the Llama 2 model by up to 15% points relative.
arXiv Detail & Related papers (2023-11-06T00:21:44Z)
Evaluating the Generation Capabilities of Large Chinese Language Models [27.598864484231477]
This paper unveils CG-Eval, the first-ever comprehensive and automated evaluation framework. It assesses the generative capabilities of large Chinese language models across a spectrum of academic disciplines. Gscore automates the quality measurement of a model's text generation against reference standards.
arXiv Detail & Related papers (2023-08-09T09:22:56Z)
From Static Benchmarks to Adaptive Testing: Psychometrics in AI Evaluation [60.14902811624433]
We discuss a paradigm shift from static evaluation methods to adaptive testing. This involves estimating the characteristics and value of each test item in the benchmark and dynamically adjusting items in real-time. We analyze the current approaches, advantages, and underlying reasons for adopting psychometrics in AI evaluation.
arXiv Detail & Related papers (2023-06-18T09:54:33Z)
Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations [97.91630330328815]
We conduct a crowdsourcing study, where participants interact with deception detection models that have been trained to distinguish between genuine and fake hotel reviews. We observe that for a linear bag-of-words model, participants with access to the feature coefficients during training are able to cause a larger reduction in model confidence in the testing phase when compared to the no-explanation control.
arXiv Detail & Related papers (2021-12-17T18:29:56Z)
Manual Evaluation Matters: Reviewing Test Protocols of Distantly Supervised Relation Extraction [61.48964753725744]
We build manually-annotated test sets for two DS-RE datasets, NYT10 and Wiki20, and thoroughly evaluate several competitive models. Results show that the manual evaluation can indicate very different conclusions from automatic ones.
arXiv Detail & Related papers (2021-05-20T06:55:40Z)
On the model-based stochastic value gradient for continuous reinforcement learning [50.085645237597056]
We show that simple model-based agents can outperform state-of-the-art model-free agents in terms of both sample-efficiency and final reward. Our findings suggest that model-based policy evaluation deserves closer attention.
arXiv Detail & Related papers (2020-08-28T17:58:29Z)
Evaluation Toolkit For Robustness Testing Of Automatic Essay Scoring Systems [64.4896118325552]
We evaluate the current state-of-the-art AES models using a model adversarial evaluation scheme and associated metrics. We find that AES models are highly overstable. Even heavy modifications(as much as 25%) with content unrelated to the topic of the questions do not decrease the score produced by the models.
arXiv Detail & Related papers (2020-07-14T03:49:43Z)

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