AI Research Intern Screening Assignment: LLM Fine-tuning for Small Models - Exploratory Study

Overview

This is a screening assignment for our AI Research Intern position. The task involves conducting an exploratory research study on fine-tuning approaches for small-size Large Language Models (LLMs) from prominent open-source families like Qwen, Llama, and DeepSeek. The goal is to investigate efficient and effective fine-tuning methodologies suitable for resource-constrained environments and document your findings in a short academic-style research paper.

Background

The rapid advancement of Large Language Models has opened up exciting possibilities across various applications. However, deploying and fine-tuning these models, especially the larger ones, can be computationally expensive and resource-intensive. There is growing interest in leveraging smaller, yet capable, LLMs for specific tasks, particularly in scenarios with limited computational resources or latency requirements. Models like Qwen, Llama, and DeepSeek offer smaller variations that are promising candidates for fine-tuning and deployment in such settings.

This assignment focuses on exploring different fine-tuning approaches for these smaller LLMs and understanding their performance characteristics when adapted for specific downstream tasks. The findings will help us identify promising strategies for efficient and effective fine-tuning of small LLMs within our research team.

Task Description

Conduct an exploratory research study investigating different fine-tuning approaches applicable to small-size LLMs (specifically from the Qwen, Llama, or DeepSeek families). Your research should aim to:

• Select a relevant downstream task: Choose a specific natural language processing task suitable for evaluating LLM fine-tuning (e.g., text classification, question answering, text summarization, etc.). Clearly justify your choice of task and its relevance to evaluating fine-tuning efficacy.

• Choose one or more small LLMs: Select at least one (ideally up to two or three for comparative analysis, if time permits) small-size LLMs from the Qwen, Llama, or DeepSeek families. Justify your model selection based on factors like model size, architecture, pre-training data, and reported performance.

• Experiment with different fine-tuning approaches: Explore and implement at least two different fine-tuning techniques. This could include (but is not limited to):

  • Full Fine-tuning: Traditional fine-tuning of all model parameters.
  • Parameter-Efficient Fine-tuning (PEFT) techniques: Investigate and implement methods like LoRA (Low-Rank Adaptation), Adapter modules, Prefix-tuning, Prompt-tuning, etc. Explain the rationale behind your chosen PEFT methods.
  • Instruction Fine-tuning: If applicable and relevant to your chosen task, explore instruction fine-tuning strategies using appropriate datasets.

• Evaluate and compare the performance: Quantitatively and qualitatively evaluate the performance of the fine-tuned models on your chosen downstream task. Utilize appropriate evaluation metrics relevant to your task (e.g., accuracy, F1-score, BLEU, ROUGE, etc.). Compare the performance of different fine-tuning approaches and models.

• Analyze and document findings: Analyze your experimental results, identify key observations, and draw conclusions about the effectiveness and efficiency of different fine-tuning approaches for small LLMs. Document your research process, findings, and observations in a short research paper.

Dataset

For your fine-tuning experiments, you are expected to utilize publicly available datasets relevant to your chosen downstream task. You should:

• Identify and select a suitable dataset(s) for your chosen downstream task.
• Clearly describe the dataset(s) in your research paper, including its source, size, characteristics, and relevance to your research.
• Provide links or references to the dataset(s) for reproducibility.

Example Datasets (Illustrative, applicant should research and choose based on their task):

• Text Classification: AG News, SST-2, IMDb Reviews
• Question Answering: SQuAD, BoolQ
• Text Summarization: CNN/DailyMail, XSum

Technical Requirements

Core Research Components:

• Literature Review (Brief): Conduct a brief review of existing literature related to LLM fine-tuning, focusing on techniques applicable to small models and your chosen downstream task. (This should be integrated into your research paper - Introduction & Related Work).
• Experimental Design & Implementation: Design and implement your fine-tuning experiments systematically. This includes dataset selection, model selection, fine-tuning method implementation, hyperparameter tuning (basic level), and evaluation setup.
• Quantitative & Qualitative Analysis: Analyze the results using appropriate metrics and provide qualitative observations about the performance of different approaches.
• Academic-Style Research Paper: Document your entire research process, findings, and analysis in a short research paper using LaTeX.

Technical Specifications:

• Frameworks/Libraries: You are encouraged to utilize popular deep learning frameworks and libraries such as PyTorch, TensorFlow, and Hugging Face Transformers for model implementation, fine-tuning, and evaluation.
• LLM Models: Focus on small-size models from the Qwen, Llama, or DeepSeek families available through Hugging Face Transformers or similar repositories.
• Programming Language: Python is the expected programming language.
• Output Format: The research paper must be written in LaTeX and submitted as a PDF.
• Reproducibility: Ensure your experiments are reasonably reproducible by documenting your code, environment, and data sources.

Quality Standards

• Research Rigor: Demonstrate a systematic and rigorous approach to your research. Clearly define your research questions, methodology, and evaluation metrics.
• Clarity and Academic Writing: Write a clear, concise, and well-structured research paper in academic style. Use proper grammar, spelling, and formatting.
• Literature Awareness: Show awareness of relevant existing research in the field (through the brief literature review).
• Methodology Documentation: Clearly document your experimental methodology, including dataset details, model configurations, fine-tuning parameters, and evaluation procedures.
• Result Analysis & Interpretation: Provide insightful analysis and interpretation of your experimental results. Discuss the implications of your findings and any limitations.
• Ethical Considerations (Brief): Briefly consider and address any ethical considerations related to your chosen task and dataset (e.g., bias, fairness, privacy, if applicable).

Evaluation Criteria

• Research Methodology and Execution (40%):
  • Appropriateness of chosen downstream task and dataset justification.
  • Rationale for model and fine-tuning approach selection.
  • Rigorousness of experimental design and implementation.
  • Appropriate use of evaluation metrics.
  • Clarity and completeness of methodology documentation.
• Experimental Results and Analysis (30%):
  • Quality and clarity of presented results (tables, figures).
  • Insightful analysis and interpretation of experimental findings.
  • Identification of key observations and trends.
  • Discussion of limitations and potential future directions.
• Literature Review and Background (15%):
  • Demonstrated understanding of relevant existing research in LLM fine-tuning.
  • Contextualization of the research within the existing body of knowledge (even if brief).
  • Appropriate referencing and citation (in LaTeX).
• Academic Writing and Presentation (15%):
  • Clarity, conciseness, and organization of the research paper.
  • Quality of writing (grammar, spelling, style).
  • Proper LaTeX formatting and presentation.
  • Overall professionalism of the submission.

Submission Guidelines

Required Files:

Your submission should be a ZIP file containing:

• LaTeX Source Code: All LaTeX files (.tex, .bib, image files, etc.) required to compile your research paper.
• Compiled Research Paper (PDF): The compiled PDF version of your research paper.
• Source Code (Scripts): All Python scripts developed for data loading, model fine-tuning, evaluation, and result analysis. Ensure your code is well-commented and reasonably organized.
• README.md: A README file containing:
  • Setup instructions (environment setup, library installation).
  • Instructions to run your code and reproduce your experiments (if feasible within the screening timeframe, otherwise, detailed steps are sufficient).
  • Dataset details and download links/references.
  • Model configurations and fine-tuning parameter details.
  • A brief summary of your research findings and key conclusions.
  • Any important notes or assumptions.
• requirements.txt (or similar dependency file): A file listing all Python library dependencies.

Submission Process

Send your submission as a compressed ZIP file to info@alphanome.ai.

Important Notes

• Focus on Core Research Objectives: Prioritize conducting a sound and well-documented research study. Do not over-engineer or spend excessive time on minor details.
• Time Management: Manage your time effectively to address all aspects of the assignment within the given timeframe. Focus on demonstrating your research skills, analytical abilities, and writing proficiency.
• Original Work: This assignment is expected to be your original work. Plagiarism or any form of academic dishonesty will result in disqualification.
• Limited Computational Resources: You are expected to work within reasonable computational resource constraints. Choose model sizes and fine-tuning approaches that are feasible to experiment with using standard resources (e.g., cloud-based GPUs, readily available free tiers).
• Ethical Considerations: Be mindful of ethical considerations related to data usage and model development throughout your research.

Time Expectation

This assignment should take approximately 5-10 days to complete, focusing on core research, experimentation, and paper writing. Focus on the core functionality and research questions first, then add improvements and deeper analysis if time permits.

Questions?

If you have any questions about the assignment, please email info@alphanome.ai.