Simulation as Reality? The Effectiveness of LLM-Generated Data in Open-ended Question Assessment

• URL: http://arxiv.org/abs/2502.06371v1
• Date: Mon, 10 Feb 2025 11:40:11 GMT
• Title: Simulation as Reality? The Effectiveness of LLM-Generated Data in Open-ended Question Assessment
• Authors: Long Zhang, Meng Zhang, Wei Lin Wang, Yu Luo,
• Abstract summary: This study investigates the potential and gap of simulative data to address the limitation of AI-based assessment tools. Our findings reveal that while simulative data demonstrates promising results in training automated assessment models, its effectiveness has notable limitations. The absence of real-world noise and biases, which are also present in over-processed real-world data, contributes to this limitation.
• Score: 7.695222586877482
• License:
• Abstract: The advancement of Artificial Intelligence (AI) has created opportunities for e-learning, particularly in automated assessment systems that reduce educators' workload and provide timely feedback to students. However, developing effective AI-based assessment tools remains challenging due to the substantial resources required for collecting and annotating real student data. This study investigates the potential and gap of simulative data to address this limitation. Through a two-phase experimental study, we examined the effectiveness and gap of Large Language Model generated synthetic data in training educational assessment systems. Our findings reveal that while simulative data demonstrates promising results in training automated assessment models, outperforming state-of-the-art GPT-4o in most question types, its effectiveness has notable limitations. Specifically, models trained on synthetic data show excellent performance in simulated environment but need progress when applied to real-world scenarios. This performance gap highlights the limitations of only using synthetic data in controlled experimental settings for AI training. The absence of real-world noise and biases, which are also present in over-processed real-world data, contributes to this limitation. We recommend that future development of automated assessment agents and other AI tools should incorporate a mixture of synthetic and real-world data, or introduce more realistic noise and biases patterns, rather than relying solely on synthetic or over-processed data.

Related papers

• Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data [0.8267034114134277]
  Performance of machine learning models depends on the nature and size of the training data sets. High-quality, diverse, relevant and representative training data is essential to build accurate and reliable machine learning models. It is hypothesised that well-designed synthetic data can improve the performance of a machine learning algorithm.
  arXiv  Detail & Related papers  (2024-11-23T16:38:02Z)
• How Hard is this Test Set? NLI Characterization by Exploiting Training Dynamics [49.9329723199239]
  We propose a method for the automated creation of a challenging test set without relying on the manual construction of artificial and unrealistic examples. We categorize the test set of popular NLI datasets into three difficulty levels by leveraging methods that exploit training dynamics. When our characterization method is applied to the training set, models trained with only a fraction of the data achieve comparable performance to those trained on the full dataset.
  arXiv  Detail & Related papers  (2024-10-04T13:39:21Z)
• Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models [89.88010750772413]
  Synthetic data has been proposed as a solution to address the issue of high-quality data scarcity in the training of large language models (LLMs) Our work delves into these specific flaws associated with question-answer (Q-A) pairs, a prevalent type of synthetic data, and presents a method based on unlearning techniques to mitigate these flaws. Our work has yielded key insights into the effective use of synthetic data, aiming to promote more robust and efficient LLM training.
  arXiv  Detail & Related papers  (2024-06-18T08:38:59Z)
• When AI Eats Itself: On the Caveats of AI Autophagy [18.641925577551557]
  The AI autophagy phenomenon suggests a future where generative AI systems may increasingly consume their own outputs without discernment. This study examines the existing literature, delving into the consequences of AI autophagy, analyzing the associated risks, and exploring strategies to mitigate its impact.
  arXiv  Detail & Related papers  (2024-05-15T13:50:23Z)
• Best Practices and Lessons Learned on Synthetic Data [83.63271573197026]
  The success of AI models relies on the availability of large, diverse, and high-quality datasets. Synthetic data has emerged as a promising solution by generating artificial data that mimics real-world patterns.
  arXiv  Detail & Related papers  (2024-04-11T06:34:17Z)
• The Frontier of Data Erasure: Machine Unlearning for Large Language Models [56.26002631481726]
  Large Language Models (LLMs) are foundational to AI advancements. LLMs pose risks by potentially memorizing and disseminating sensitive, biased, or copyrighted information. Machine unlearning emerges as a cutting-edge solution to mitigate these concerns.
  arXiv  Detail & Related papers  (2024-03-23T09:26:15Z)
• Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark [56.8042116967334]
  Synthetic data serves as an alternative in training machine learning models. ensuring that synthetic data mirrors the complex nuances of real-world data is a challenging task. This paper explores the potential of integrating data-centric AI techniques to guide the synthetic data generation process.
  arXiv  Detail & Related papers  (2023-10-25T20:32:02Z)
• Synthetic Alone: Exploring the Dark Side of Synthetic Data for Grammatical Error Correction [5.586798679167892]
  Data-centric AI approach aims to enhance the model performance without modifying the model. Data quality control method has a positive impact on models trained with real-world data. A negative impact is observed in models trained solely on synthetic data.
  arXiv  Detail & Related papers  (2023-06-26T01:40:28Z)
• Synthetic data, real errors: how (not) to publish and use synthetic data [86.65594304109567]
  We show how the generative process affects the downstream ML task. We introduce Deep Generative Ensemble (DGE) to approximate the posterior distribution over the generative process model parameters.
  arXiv  Detail & Related papers  (2023-05-16T07:30:29Z)
• Robust SAR ATR on MSTAR with Deep Learning Models trained on Full Synthetic MOCEM data [0.0]
  Simulation can overcome this issue by producing synthetic training datasets. We show that domain randomization techniques and adversarial training can be combined to overcome this issue.
  arXiv  Detail & Related papers  (2022-06-15T08:04:36Z)

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.