Course Goal: To equip learners with the expertise to design, implement, and evaluate advanced prompt engineering techniques for a wide range of NLP tasks, enabling them to effectively leverage the power of large language models (LLMs) for real-world applications.
Prerequisites:
- Successful completion of "Modern AI Development: From Transformers to Generative Models" or equivalent experience.
- Strong understanding of Transformer architectures and their applications.
- Experience with fine-tuning and using pre-trained models from the Hugging Face ecosystem.
- Proficiency in Python and PyTorch.
- Familiarity with common NLP tasks (classification, question answering, text generation, etc.).
Course Duration: 8 weeks (flexible), with each module designed for approximately one week of study.
Tools:
- Python (>= 3.8)
- PyTorch (latest stable version)
- Hugging Face Transformers library
- Hugging Face Datasets library
- Hugging Face Accelerate library (for distributed training)
- Jupyter Notebooks/Google Colab
- Standard Python libraries (NumPy, Pandas, etc.)
- Potentially other libraries for specific tasks (e.g., for evaluating code generation)
Curriculum Draft:
Module 1: Foundations of Prompt Engineering and Recap (Week 1)
- Topic 1.1: The Rise of Prompt Engineering:
- Recap of the limitations of traditional fine-tuning approaches.
- The paradigm shift towards prompt-based learning.
- The role of prompt engineering in unlocking the potential of LLMs.
- Introduction to the concept of "in-context learning."
- Topic 1.2: Prompt Design Fundamentals:
- Basic prompting strategies (zero-shot, few-shot).
- Understanding the impact of prompt phrasing on model outputs.
- Key considerations: clarity, specificity, and task definition.
- Introduction to different prompt components (instructions, examples, context).
- Topic 1.3: Evaluation Metrics for Prompting:
- Recap of common NLP evaluation metrics (accuracy, precision, recall, F1-score, BLEU, ROUGE).
- Metrics specific to evaluating the effectiveness of prompts.
- Human evaluation and its role in assessing prompt quality.
- Topic 1.4: Surveying the Landscape (Based on the Paper):
- Introduction to the Vatsal and Dubey (2024) paper.
- Overview of the paper's taxonomy of prompting methods and NLP tasks.
- Understanding the paper's methodology and contribution.
- Hands-on Exercises:
- Experimenting with basic zero-shot and few-shot prompts on different NLP tasks using Hugging Face models.
- Analyzing the impact of different prompt variations on model outputs.
- Implementing evaluation metrics to compare prompt performance.
Module 2: Reasoning and Chain-of-Thought Prompting (Week 2)
- Topic 2.1: Chain-of-Thought (CoT) Prompting:
- Deep dive into CoT prompting (Wei et al., 2022).
- Understanding how CoT enhances reasoning abilities in LLMs.
- Implementing CoT for complex reasoning tasks.
- Topic 2.2: Self-Consistency and its Variants:
- Exploring Self-Consistency (Wang et al., 2022) as an enhancement to CoT.
- Understanding the concept of sampling diverse reasoning paths.
- Implementing and evaluating Self-Consistency.
- Introduction to Contrastive CoT (Chia et al., 2023)
- Topic 2.3: Advanced CoT Techniques:
- Automatic CoT (Auto-CoT - Zhang et al., 2022).
- Complex CoT (Fu et al., 2022)
- Least-to-Most Prompting (Zhou et al., 2022).
- Other variations from the paper (e.g., Fed-SP/DP-SC/CoT).
- Topic 2.4: Applying CoT to Different Reasoning Tasks:
- Mathematical Problem Solving
- Commonsense Reasoning
- Multi-Hop Reasoning
- Hands-on Exercises:
- Implementing different CoT variations (Self-Consistency, Auto-CoT, etc.) in PyTorch.
- Applying CoT to datasets from the paper's selected tasks.
- Comparing the performance of different CoT methods.
Module 3: Enhancing CoT with Structure and External Knowledge (Week 3)
- Topic 3.1: Structured Chain-of-Thought (SCoT):
- Introducing SCoT (Li et al., 2023b) for improved code generation.
- Understanding the role of program structures in guiding reasoning.
- Topic 3.2: Incorporating Symbolic Reasoning:
- Chain-of-Symbol (CoS - Hu et al., 2023) for spatial reasoning.
- Exploring the use of symbols to represent relationships.
- Topic 3.3: Leveraging External Knowledge:
- Chain-of-Knowledge (CoK - Li et al., 2023c) for addressing hallucination.
- Verify-and-Edit (VE - Zhao et al., 2023a) for factual consistency.
- Topic 3.4: Program-Aided Language Models (PAL):
- Using programs to enhance reasoning (PAL - Gao et al., 2023, PoT - Chen et al. 2022a).
- Delegating computation to external tools.
- Hands-on Exercises:
- Implementing SCoT for a code generation task.
- Exploring CoS for a spatial reasoning task (if a suitable dataset is available).
- Experimenting with CoK or VE to improve the factual accuracy of a model's outputs.
Module 4: Prompting for Specific Tasks: Question Answering and Truthfulness (Week 4)
- Topic 4.1: Contextual Question Answering:
- Techniques for prompting LLMs to answer questions based on a given context.
- Implicit Retrieval Augmented Generation (Implicit RAG - Vatsal & Singh, 2024).
- Exploring other methods mentioned in the paper for contextual QA.
- Topic 4.2: Context-Free Question Answering:
- Prompting strategies for leveraging the LLM's internal knowledge base.
- Techniques like Thread-of-Thought (ThoT - Zhou et al., 2023).
- Topic 4.3: Ensuring Truthfulness:
- Addressing the issue of hallucination in LLMs.
- System 2 Attention (S2A - Weston & Sukhbaatar, 2023) for handling irrelevant context.
- Instructed Prompting (Shi et al., 2023)
- Chain-of-Verification (CoVe - Dhuliawala et al., 2023).
- Topic 4.4: Table-Based Tasks:
- Prompting for Table-Based QA and Truthfulness.
- Chain-of-Table (Wang et al., 2024).
- Dater (Ye et al., 2023)
- Binder (Cheng et al., 2022)
- Hands-on Exercises:
- Implementing and evaluating different prompting strategies for QA tasks using datasets mentioned in the paper.
- Applying techniques like S2A and CoVe to improve truthfulness.
- Experimenting with Chain-of-Table for table-based reasoning.
Module 5: Prompting for Specialized Domains and Tasks (Week 5)
- Topic 5.1: Code Generation:
- Deep dive into prompting for code generation.
- Structured Chain-of-Thought (SCoT).
- Exploring other relevant methods from the paper.
- Topic 5.2: Dialogue Systems:
- Prompting techniques for building conversational agents.
- Multi-Turn Conversation Response (MTCR) prompting if applicable.
- Topic 5.3: Summarization:
- Chain-of-Event (CoE - Bao et al., 2024).
- Other summarization-specific prompting strategies.
- Topic 5.4: Other Specialized Tasks (Based on Student Interest):
- Emotion/Sentiment Understanding (e.g., THOR - Fei et al., 2023).
- Named Entity Recognition.
- Machine Translation.
- Hands-on Exercises:
- Building a code generation system using SCoT or other relevant methods.
- Developing a simple dialogue system using appropriate prompting strategies.
- Applying CoE for a summarization task.
Module 6: Advanced Prompting Techniques (Week 6)
- Topic 6.1: Tree-of-Thoughts (ToT):
- In-depth exploration of ToT (Yao et al., 2024) for complex problem-solving.
- Implementing ToT with search algorithms.
- Topic 6.2: Metacognitive Prompting (MP):
- Understanding MP (Wang & Zhao, 2023) and its basis in cognitive psychology.
- Applying MP to enhance self-reflection and reasoning in LLMs.
- Topic 6.3: Logical Reasoning with Prompting:
- Exploring techniques like Logical Thoughts (LoT - Zhao et al., 2023b).
- Maieutic Prompting (Jung et al., 2022).
- Topic 6.4: Ensemble Refinement (ER):
- Implementing ER (Singhal et al., 2023) for improved response generation.
- Hands-on Exercises:
- Implementing ToT for a challenging reasoning task.
- Applying MP to a task requiring critical evaluation and justification.
- Experimenting with ER to enhance the quality of generated text.
Module 7: Prompt Engineering in the Broader Context (Week 7)
- Topic 7.1: Prompt Engineering vs. Fine-tuning:
- A detailed comparison of the trade-offs between prompt engineering and fine-tuning.
- Situations where each approach is most suitable.
- Hybrid approaches that combine prompting and fine-tuning.
- Topic 7.2: The Role of Prompt Engineering in Model Deployment:
- Considerations for deploying LLMs with prompt-based interfaces.
- Techniques for optimizing prompt performance in real-world settings.
- Prompt versioning and management.
- Topic 7.3: Ethical Considerations in Prompt Engineering:
- Addressing potential biases that can be introduced through prompts.
- Responsible use of LLMs and mitigating the risks of misuse.
- The importance of transparency and explainability in prompt-based systems.
- Topic 7.4: The Future of Prompt Engineering:
- Emerging trends and research directions in prompt engineering.
- Potential advancements and future challenges.
- The evolving relationship between humans and AI in the age of LLMs.
Module 8: Final Project (Week 8)
- Project Definition:
- Students will define and develop a substantial project that demonstrates their mastery of advanced prompt engineering techniques.
- Projects should focus on a specific NLP task and involve the implementation and evaluation of multiple prompting strategies.
- Encourage using datasets and tasks discussed in the paper or exploring new ones.
- Project Development:
- Students will work on their projects, applying the concepts and techniques learned throughout the course.
- Regular check-ins and feedback sessions with the instructor.
- Project Presentation:
- Students will present their projects, showcasing their methodology, results, and insights.
- Peer review and discussion of project findings.
Assessment:
- Weekly hands-on exercises (40%)
- Mid-term quiz covering foundational concepts and techniques from the paper (20%)
- Final project (40%)
Key Pedagogical Considerations:
- Paper-Centric Approach: The curriculum is tightly aligned with the Vatsal and Dubey (2024) paper, providing a comprehensive overview of the field.
- Hands-on Learning: Each module emphasizes practical implementation and experimentation with different prompting techniques.
- Progressive Complexity: The course starts with foundational concepts and gradually introduces more advanced techniques.
- Real-World Relevance: The curriculum focuses on applying prompt engineering to solve practical NLP tasks.
- Critical Thinking: Students are encouraged to analyze, compare, and evaluate different prompting strategies, fostering a deeper understanding of the field.
- Community and Discussion: Encourage students to actively discuss the paper, share their findings, and collaborate on projects.