SEFL: Harnessing Large Language Model Agents to Improve Educational Feedback Systems

• URL: http://arxiv.org/abs/2502.12927v1
• Date: Tue, 18 Feb 2025 15:09:29 GMT
• Title: SEFL: Harnessing Large Language Model Agents to Improve Educational Feedback Systems
• Authors: Mike Zhang, Amalie Pernille Dilling, Léon Gondelman, Niels Erik Ruan Lyngdorf, Euan D. Lindsay, Johannes Bjerva,
• Abstract summary: Synthetic Educational Feedback Loops (SEFL) is a novel framework designed to deliver immediate, on-demand feedback at scale. Two large language models (LLMs) operate in teacher--student roles to simulate assignment completion and formative feedback. We show that SEFL-tuned models outperform their non-tuned counterparts in feedback quality, clarity, and timeliness.
• Score: 5.191286314473505
• License:
• Abstract: Providing high-quality feedback is crucial for student success but is constrained by time, cost, and limited data availability. We introduce Synthetic Educational Feedback Loops (SEFL), a novel framework designed to deliver immediate, on-demand feedback at scale without relying on extensive, real-world student data. In SEFL, two large language models (LLMs) operate in teacher--student roles to simulate assignment completion and formative feedback, generating abundant synthetic pairs of student work and corresponding critiques. We then fine-tune smaller, more computationally efficient LLMs on these synthetic pairs, enabling them to replicate key features of high-quality, goal-oriented feedback. Unlike personalized tutoring approaches that offer multi-turn, individualized instruction, SEFL specifically focuses on replicating the teacher-->student feedback loop for diverse assignments. Through both LLM-as-a-judge and human evaluations, we demonstrate that SEFL-tuned models outperform their non-tuned counterparts in feedback quality, clarity, and timeliness. These findings reveal SEFL's potential to transform feedback processes for higher education and beyond, offering an ethical and scalable alternative to conventional manual feedback cycles.

Related papers

• A Systematic Examination of Preference Learning through the Lens of Instruction-Following [83.71180850955679]
  We use a novel synthetic data generation pipeline to generate 48,000 instruction unique-following prompts. With our synthetic prompts, we use two preference dataset curation methods - rejection sampling (RS) and Monte Carlo Tree Search (MCTS) Experiments reveal that shared prefixes in preference pairs, as generated by MCTS, provide marginal but consistent improvements. High-contrast preference pairs generally outperform low-contrast pairs; however, combining both often yields the best performance.
  arXiv  Detail & Related papers  (2024-12-18T15:38:39Z)
• Automated Feedback in Math Education: A Comparative Analysis of LLMs for Open-Ended Responses [0.0]
  This study aims to explore the potential of Large Language Models (LLMs) in facilitating automated feedback in math education. We employ Mistral, a version of Llama catered to math, and fine-tune this model for evaluating student responses by leveraging a dataset of student responses and teacher-written feedback for middle-school math problems. We evaluate the model's performance in scoring accuracy and the quality of feedback by utilizing judgments from 2 teachers.
  arXiv  Detail & Related papers  (2024-10-29T16:57:45Z)
• Closing the Loop: Learning to Generate Writing Feedback via Language Model Simulated Student Revisions [6.216542656489173]
  We propose PROF that PROduces Feedback via learning from LM simulated student revisions. We empirically test the efficacy of PROF and observe that our approach surpasses a variety of baseline methods in effectiveness of improving students' writing.
  arXiv  Detail & Related papers  (2024-10-10T15:52:48Z)
• Beyond Thumbs Up/Down: Untangling Challenges of Fine-Grained Feedback for Text-to-Image Generation [67.88747330066049]
  Fine-grained feedback captures nuanced distinctions in image quality and prompt-alignment. We show that demonstrating its superiority to coarse-grained feedback is not automatic. We identify key challenges in eliciting and utilizing fine-grained feedback.
  arXiv  Detail & Related papers  (2024-06-24T17:19:34Z)
• UniFL: Improve Latent Diffusion Model via Unified Feedback Learning [61.66652875042845]
  We present UniFL, a unified framework that leverages feedback learning to enhance diffusion models comprehensively. UniFL consists of three key components: perceptual feedback learning, which enhances visual quality; decoupled feedback learning, which improves aesthetic appeal; and adversarial feedback learning, which accelerates inference. In-depth experiments and extensive user studies validate the superior performance of our method in enhancing generation quality and inference acceleration.
  arXiv  Detail & Related papers  (2024-04-08T15:14:20Z)
• Improving the Validity of Automatically Generated Feedback via Reinforcement Learning [46.667783153759636]
  We propose a framework for feedback generation that optimize both correctness and alignment using reinforcement learning (RL) Specifically, we use GPT-4's annotations to create preferences over feedback pairs in an augmented dataset for training via direct preference optimization (DPO)
  arXiv  Detail & Related papers  (2024-03-02T20:25:50Z)
• DRESS: Instructing Large Vision-Language Models to Align and Interact with Humans via Natural Language Feedback [61.28463542324576]
  We present DRESS, a large vision language model (LVLM) that innovatively exploits Natural Language feedback (NLF) from Large Language Models. We propose a novel categorization of the NLF into two key types: critique and refinement. Our experimental results demonstrate that DRESS can generate more helpful (9.76%), honest (11.52%), and harmless (21.03%) responses.
  arXiv  Detail & Related papers  (2023-11-16T18:37:29Z)
• Constructive Large Language Models Alignment with Diverse Feedback [76.9578950893839]
  We introduce Constructive and Diverse Feedback (CDF) as a novel method to enhance large language models alignment. We exploit critique feedback for easy problems, refinement feedback for medium problems, and preference feedback for hard problems. By training our model with this diversified feedback, we achieve enhanced alignment performance while using less training data.
  arXiv  Detail & Related papers  (2023-10-10T09:20:14Z)
• PREFER: Prompt Ensemble Learning via Feedback-Reflect-Refine [24.888093229577965]
  We propose a simple, universal, and automatic method named PREFER to address the stated limitations. Our PREFER achieves state-of-the-art performance in multiple types of tasks by a significant margin.
  arXiv  Detail & Related papers  (2023-08-23T09:46:37Z)
• Aligning Large Language Models through Synthetic Feedback [43.84431341195111]
  We propose a novel alignment learning framework with synthetic feedback not dependent on extensive human annotations. In human evaluation, our model is preferred to Alpaca and Dolly-v2, 55.0% and 58.5% of the time, respectively.
  arXiv  Detail & Related papers  (2023-05-23T06:41:16Z)
• Training Language Models with Language Feedback at Scale [50.70091340506957]
  We introduce learning from Language Feedback (ILF), a new approach that utilizes more informative language feedback. ILF consists of three steps that are applied iteratively: first, conditioning the language model on the input, an initial LM output, and feedback to generate refinements. We show theoretically that ILF can be viewed as Bayesian Inference, similar to Reinforcement Learning from human feedback.
  arXiv  Detail & Related papers  (2023-03-28T17:04:15Z)

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.