Best Practices for Gemma 2

[Llarian]

Dear Community,

As a researcher at the University of Zurich, I want to apply Gemma 2 27B on Vertex AI for an AI in Education project focused on automated essay scoring. I'm seeking resources and best practices for using this model effectively, particularly in areas like prompt engineering, data preparation, and fine-tuning. I have prior experience with large language models like GPT-4, GPT-4o, and Llama 3.1 on MS Azure. Within GCP, I've experimented with Gemini-1.0 and Gemini-1.5, but these models are larger than I need for this project. Could the community point me towards relevant resources or share their experiences with Gemma 2 27B on Vertex AI, especially in the context of applications in education?

[ruthseki]

Hi @Llarian,

Gemma 2 is relatively new, publicly available. Specific resources directly addressing its use in educational essay scoring are limited. However, we can leverage your existing experience and general best practices to guide your approach.

Here's a breakdown of resources and strategies, focusing on your specific needs:

1. Data Preparation:

• Dataset Acquisition: You'll need a large dataset of essays with corresponding human-assigned scores. Consider sources like:
• Existing Educational Datasets: Search for publicly available datasets on platforms like Kaggle, Hugging Face, or educational research repositories. Look for datasets with diverse writing styles, topics, and proficiency levels.
• Internal University Data: If ethically permissible and with appropriate anonymization, leverage essays from your university's existing archives. This provides a tailored dataset.
• Data Augmentation: If your dataset is smaller than ideal, consider techniques like back-translation or paraphrasing to increase its size. Be cautious, as this can introduce noise.
• Data Cleaning and Preprocessing: This is crucial. Address:
• Noise Removal: Eliminate irrelevant characters, HTML tags, etc.
• Standardization: Ensure consistent formatting (e.g., punctuation, capitalization).
• Score Calibration: Ensure your scores are consistently scaled and reliable. Consider methods to improve inter-rater reliability if using multiple human graders.
• Data Splitting: Divide your dataset into training, validation, and test sets (e.g., 80%, 10%, 10%).

2. Prompt Engineering:

• Instruction Tuning: Because you're using a large model, instruction tuning is likely to be more effective than few-shot prompting. Craft clear and concise instructions that explicitly state the task: "Score this essay on a scale of 1-5 based on clarity, grammar, coherence, and argumentation." Experiment with different phrasing to see what works best.
• Few-shot Learning (as a complement): Include a few examples of essays with their scores in your prompt to further guide the model. The examples should be representative of the data distribution.
• Output Formatting: Specify the desired format for the model's output. For example, you might want the score as a numerical value, along with a brief justification of the score.

3. Fine-tuning (Optional but Recommended):

• Vertex AI's Fine-tuning Capabilities: Utilize Vertex AI's tools for fine-tuning Gemma 2 27B. This is crucial for adapting the model to your specific essay scoring task. You'll likely need significant computational resources for this.
• Hyperparameter Tuning: Experiment with different learning rates, batch sizes, and optimization algorithms to find the optimal configuration for your data.
• Regularization: Employ regularization techniques (e.g., dropout, weight decay) to prevent overfitting.
• Evaluation Metrics: Track metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Pearson correlation, and Spearman rank correlation between the model's scores and human scores.

4. Resources and Tools:

• Vertex AI Documentation: Google Cloud's official documentation on Vertex AI and its large language model capabilities is your primary resource. Pay close attention to sections on model fine-tuning and evaluation. Consider checking this link as well.
• Google AI Blog: Look for blog posts and research papers from Google AI on Gemma 2 and related models.
• Research Papers on Automated Essay Scoring: Explore academic literature on automated essay scoring. This will provide insights into successful approaches and common challenges.
• Hugging Face: While not directly related to Gemma 2 on Vertex AI, Hugging Face offers many resources and pre-trained models for NLP tasks, providing valuable contextual information.

5. Ethical Considerations:

• Bias Mitigation: Be aware of potential biases in your dataset and model outputs. Strive for fairness and inclusivity in your scoring system.
• Transparency and Explainability: Develop mechanisms to explain the model's scoring decisions. This is crucial for building trust and addressing concerns about the fairness and accuracy of the automated system.

Remember that this is an iterative process. Start with a smaller subset of your data for initial experimentation, gradually scaling up as you refine your approach. Thorough evaluation and iterative improvement are essential for achieving high-quality results in automated essay scoring. 

I hope the above information is helpful.

[Llarian]

Thank you for this extremely helpful information. I feel like I am on the right path. Could you elaborate a bit on the aspects of transparency and explainability? In my field, researchers are usually expected to provide evidence for any interpretation of a model score. In this case, this model score would be Gemma 2‘s output. For instance, if Gemma 2 provides a score that is supposed to grade a text’s spelling, we are supposed to provide evidence that this score indeed reflects the correctness of the text’s spelling and not, for instance, its overall quality. Although I am familiar with some tools of interpretable machine learning, such as SHAP and LIME values, I wonder if evaluations on benchmarks for essay scoring and similar problems would be a more suitable approach to provide such evidence. Are there any tools on GCP that could be useful for such an endeavor?

[ruthseki · Accepted solution]

Hello @Llarian,

Simply providing a numerical score from Gemma 2 isn't sufficient; you need to demonstrate that the score accurately reflects the intended aspect of essay quality (e.g., spelling, grammar, argumentation). While SHAP and LIME are valuable, they might not be the most effective approach for this specific problem, and relying solely on them could be insufficient.

Here are the approaches to enhance transparency and explainability, focusing on the context of automated essay scoring and GCP's capabilities:

1. Beyond SHAP and LIME for Essay Scoring:

SHAP and LIME are helpful for understanding feature importance at the individual instance level. However, for essay scoring, you need a more holistic approach because:

• Holistic Understanding: Essay quality isn't determined by isolated words or phrases but by the overall structure, argumentation, and coherence. SHAP/LIME might highlight individual words, but that doesn't explain the why behind the overall score.
• Lack of Granularity: They may not provide the granular feedback needed to pinpoint specific areas for improvement, which is often a requirement in educational feedback.

2. More Suitable Approaches:

• Benchmark Datasets and Evaluation Metrics: This is your strongest approach. Use established benchmark datasets for essay scoring (search for them on Kaggle, papers with code, etc.). Compare Gemma 2's performance against established baselines and other state-of-the-art models using metrics relevant to your specific scoring criteria:
• Correlation with Human Scores: Pearson and Spearman correlation coefficients quantify the agreement between the model's scores and those of human graders.
• Error Analysis: Analyze cases where the model significantly deviates from human judgment. This helps identify systematic biases or weaknesses in the model.
• Specific Metric Analysis (e.g., for Spelling): If evaluating spelling specifically, incorporate metrics that directly assess spelling accuracy (e.g., word error rate). This provides concrete evidence focused on your specific claims.
• Attention Visualization (If Possible): If Gemma 2's architecture allows for it, visualize its attention mechanisms. Attention weights reveal which parts of the essay the model focused on most when generating the score. This can provide insights into its reasoning process, although interpretation requires careful consideration.
• Probing Classifiers: Train separate classifiers to predict specific aspects of writing quality (e.g., grammar, coherence, argumentation) using features extracted from the essay. Compare the predictions of these classifiers with Gemma 2's scores to see how well the model aligns with these individual aspects.
• Case Studies: Present detailed analyses of a selected subset of essays, showing the model's score, the human scores, and a rationale for the model's decision based on the attention visualization (if available) or textual analysis.

3. GCP Tools for Enhanced Explainability:

While GCP doesn't offer a single "explainability tool" for LLM outputs, its ecosystem supports the methods above:

• Vertex AI's Model Monitoring: While primarily for detecting model drift, it can provide data on the model's performance over time, aiding in identifying potential problems.
• BigQuery and Data Studio: Use BigQuery to store and analyze your essay data, human scores, and model predictions. Data Studio can then be used to create insightful visualizations and reports to illustrate your findings.
• Custom Code: You'll likely need to write custom code (Python is ideal) to implement attention visualization, error analysis, correlation calculations, and the integration with probing classifiers.

In summary, for academic rigor, relying solely on SHAP/LIME is insufficient. Focus on rigorous evaluation using benchmark datasets, relevant metrics, and detailed case studies to provide strong evidence supporting your claims about Gemma 2's performance in essay scoring. GCP's data processing and visualization tools provide the infrastructure for managing and presenting your findings effectively. Remember to clearly articulate how your methodology addresses concerns about potential confounding factors influencing the model's assessment of spelling or other specific aspects of writing.