jjzha/Qwen2.5-0.5B-Instruct-SEFL

Model Card for Model ID

This is a Qwen/Qwen2.5-0.5B-Instruct model fine-tuned on the jjzha/sefl dataset using the SEFL approach (Synthetic Educational Feedback Loops).

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Model Details

Model Description

• Developed by: Mike Zhang
• Funded by [optional]: Villum Fonden (VIL57392)
• Model type: Autoregressive language model
• Language(s) (NLP): English
• License: cc-by-4.0
• Finetuned from model [optional]: Qwen/Qwen2.5-0.5B-Instruct

Quick Summary (SEFL Approach)

SEFL (\textbf{S}ynthetic \textbf{E}ducational \textbf{F}eedback \textbf{L}oops) is a framework designed to generate on-demand, concise, and targeted feedback for educational settings. Instead of relying on real-world student data—which often raises privacy and consent issues—SEFL simulates a teacher–student feedback loop using Large Language Models (LLMs). In particular:

1. Synthetic Data Generation
   Two LLM "agents" (a Teacher-Agent and a Student-Agent) produce assignment and answer pairs. The Student-Agent introduces deliberate errors, and the Teacher-Agent provides specific, formative feedback on each error.

2. Fine-tuning on Synthetic Data
   Smaller or mid-sized models (like Qwen2.5-14B-Instruct) are then fine-tuned on the teacher–student interaction data. This allows them to provide high-quality, contextually relevant, and concise feedback on new educational tasks.

3. Efficiency and Scalability
   Because the data is fully synthetic, fine-tuning can be done at scale without the usual bottlenecks of data acquisition and anonymization.

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Model Sources [optional]

• Repository: https://github.com/jjzha/sefl
• Paper [optional]: SEFL: Harnessing Large Language Model Agents to Improve Educational Feedback Systems (preprint)

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Uses

This model is intended to provide high-quality, concise feedback on educational assignments. By combining instruction tuning with a specialized SEFL dataset, it is designed to address common pitfalls in automated feedback systems (e.g., vagueness, excessive verbosity, lack of specificity).

Direct Use

• Formative Feedback: Instructors or students can prompt the model with an assignment and a student response, and receive structured comments pinpointing strengths, weaknesses, and actionable improvement steps.
• Assignment Testing: Course creators might use the model to generate feedback for sample student responses during test-design phases.

Downstream Use [optional]

• Integration into LMS: (e.g., Moodle, Canvas) The model’s concise feedback approach can be embedded within an LMS for large-scale, automated or semi-automated feedback generation.
• Pedagogical Research: Educational researchers can experiment with the model's feedback style to gauge student outcomes and assess the impact of immediate feedback loops.

Out-of-Scope Use

• Personalized Tutoring/Chat: SEFL specifically focuses on single-turn or short feedback loops for tasks, rather than ongoing multi-turn or deeply personalized tutoring.
• Sensitive or High-Stakes Assessments: This model should not be the sole determinant of success in high-stakes exams or certifications, as it does not guarantee error-free or unbiased feedback.

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Bias, Risks, and Limitations

Known Limitations

• Synthetic Data Alignment: The dataset is entirely synthetic. While this avoids privacy concerns, it may not capture the full diversity of real-world classroom submissions.
• Domain-Specific Depth: If the assignment is too specialized or requires deep domain expertise, the model may provide incomplete or overly general feedback.
• Verbosity vs. Brevity: LLMs can default to verbose explanations. While SEFL aims for concise feedback, some prompts or queries might still elicit lengthy responses.

Recommendations

• Human Oversight: Educators should review automated feedback for correctness, especially for specialized or high-stakes tasks.
• Transparency: Inform students that feedback is AI-generated and may not fully reflect instructor judgment.
• Refinement via Real Data: Over time, augmenting synthetic data with real anonymized examples (if ethically collected) could improve domain coverage.

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How to Get Started with the Model

You can use the code below to get started:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "jjzha/Qwen2.5-0.5B-Instruct-SEFL"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = """<Insert assignment and student answer here>"""

inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(**inputs, max_length=512)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)