Leveraging Quantized Large Language Models for Accurate Automatic Grading and Feedback Generation

Alapfogalmak

Quantized LLaMA-2 models can be effectively fine-tuned to achieve high accuracy in automatically assigning grades to short answers and essays, as well as generating feedback that closely aligns with expert evaluations.

Kivonat

This study explores the use of quantized large language models (LLMs), specifically the LLaMA-2 model, for the tasks of automatic grading and feedback generation. The researchers conducted experiments on both proprietary and open-source datasets to evaluate the performance of the quantized LLaMA-2 models.

For the automatic grading task, the quantized LLaMA-2 13B model with QLoRA fine-tuning outperformed other baseline models, achieving an RMSE of 0.036 and MAE of 0.028 on the proprietary dataset, and an RMSE of 0.257 on the open-source SAF dataset. The results demonstrate the effectiveness of the quantized LLaMA-2 models in accurately predicting grade scores.

For the feedback generation task, the researchers found that supplying the predicted grade scores as additional input to the fine-tuned LLaMA-2 models led to significant improvements in the quality of the generated feedback, as measured by BLEU and ROUGE scores. The quantized LLaMA-2 13B model with grade score input achieved the best performance, with BLEU, ROUGE-1, and ROUGE-2 scores of 0.707, 0.775, and 0.737, respectively, on the proprietary dataset.

The findings from this study provide valuable insights into the potential of using quantization techniques to fine-tune LLMs for various downstream tasks, such as automatic grading and feedback generation, while maintaining high accuracy and quality at reduced computational costs and latency.

Statisztikák

The LLaMA-2-13B model with QLoRA fine-tuning achieved an RMSE of 0.036 and MAE of 0.028 on the proprietary dataset.
The quantized LLaMA-2-7B model with QLoRA fine-tuning achieved an RMSE of 0.257 on the open-source SAF dataset.
The LLaMA-2-13B model with grade score input achieved BLEU, ROUGE-1, and ROUGE-2 scores of 0.707, 0.775, and 0.737, respectively, on the proprietary dataset for feedback generation.

Idézetek

"The findings from this study provide important insights into the impacts of the emerging capabilities of using quantization approaches to fine-tune LLMs for various downstream tasks, such as automatic short answer scoring and feedback generation at comparatively lower costs and latency."
"The incorporation of predicted grade scores as additional input further enhanced the model's performance."

Főbb Kivonatok

Investigating Automatic Scoring and Feedback using Large Language Models

by Gloria Ashiy... : arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00602.pdf

Mélyebb kérdések

How can the proposed approach be extended to handle longer-form essays and open-ended questions, which may require more complex reasoning and understanding?

To extend the proposed approach to handle longer-form essays and open-ended questions, several adjustments and enhancements can be made. Firstly, the model architecture can be modified to accommodate longer input sequences and more complex linguistic structures. This may involve increasing the maximum sequence length allowed during training and inference to capture the entirety of longer essays. Additionally, incorporating hierarchical modeling techniques can help the model better understand the context and structure of longer texts.
Furthermore, the fine-tuning process can be optimized to focus on higher-level semantic understanding and reasoning capabilities. This can be achieved by providing the model with more diverse and challenging training data that includes a wide range of essay topics and writing styles. By exposing the model to a variety of writing samples, it can learn to generalize better and generate more coherent and insightful feedback for longer essays.
Moreover, leveraging ensemble learning techniques by combining multiple LLMs fine-tuned for specific aspects of essay evaluation, such as content relevance, coherence, and argumentation strength, can enhance the overall performance of the system. By aggregating the outputs of these specialized models, a more comprehensive and nuanced evaluation of longer-form essays can be achieved.

What are the potential limitations or biases that may arise when using quantized LLMs for automated grading and feedback generation, and how can these be addressed?

When using quantized LLMs for automated grading and feedback generation, several limitations and biases may arise. One potential limitation is the loss of precision and information due to quantization, which can impact the model's ability to capture subtle nuances in student responses. This may lead to inaccuracies in grading and feedback provision, especially for complex or ambiguous answers.
Biases can also be introduced during the fine-tuning process, where the model may inadvertently learn from biased training data, leading to unfair evaluations or feedback. Additionally, quantization techniques may introduce biases related to the compression of numerical values, affecting the model's decision-making process.
To address these limitations and biases, it is essential to carefully select and preprocess training data to ensure diversity and representativeness. By incorporating data augmentation techniques and bias mitigation strategies, such as adversarial training or debiasing algorithms, the model can learn to make more objective and unbiased evaluations.
Regular monitoring and evaluation of the model's performance, including bias audits and fairness assessments, can help identify and rectify any biases that may arise during the automated grading process. Transparency in the model's decision-making process and feedback generation can also help mitigate biases by allowing for human oversight and intervention when necessary.

Could the techniques developed in this study be applied to other educational domains beyond short answers and essays, such as programming assignments or mathematical problem-solving?

Yes, the techniques developed in this study can be applied to other educational domains beyond short answers and essays, such as programming assignments or mathematical problem-solving. By fine-tuning large language models (LLMs) for specific tasks and domains, automated grading and feedback generation systems can be tailored to evaluate a wide range of student responses in various educational contexts.
For programming assignments, LLMs can be trained on code snippets and programming concepts to assess students' coding proficiency, logic, and problem-solving skills. By providing annotated datasets of correct and incorrect code implementations, the model can learn to identify errors, suggest improvements, and provide detailed feedback to students.
Similarly, in mathematical problem-solving, LLMs can be fine-tuned on mathematical expressions, equations, and problem-solving strategies to evaluate students' mathematical reasoning and accuracy. The model can analyze the steps taken by students to solve problems, identify misconceptions, and offer personalized feedback to enhance learning outcomes.
Overall, the techniques developed in this study can be adapted and extended to various educational domains by customizing the training data, evaluation criteria, and feedback generation processes to suit the specific requirements of each domain. This flexibility and scalability make automated grading and feedback systems using LLMs versatile tools for enhancing learning and assessment across diverse educational disciplines.