Overview
This week focuses on Natural Language Processing (NLP), a crucial field in AI that deals with the interaction between computers and humans using natural language. We’ll explore fundamental concepts, modern architectures, and practical applications of NLP.
Instructor
Zilong Zheng, BIGAI
Topics Covered
- From PyTorch to Transformers
- Tokenization, Embedding, and Attention mechanisms
- Encoder-Decoder and Transformer architectures
- Introduction to LangChain and LlamaIndex (Optional)
Assignments
Full Assignment:
- Implement a task-oriented agent system with a context graph that incrementally extracts structured, interpretable, and auditable graph nodes from the agent’s tool-call results, reasoning traces, and intermediate conclusions. Build on the open-source Belief Context Graph (BCG) project.
- Use the constructed graph to identify and improve the weakest link in the agent’s reasoning chain, such as evidence-grounded verification, parallel tool-call planning, contradiction detection, or error attribution.
- Evaluate your system on the BrowseComp benchmark, and report the quantitative results (task performance and token cost) in your report.
Short Assignment: For the short assignment, you only need to submit a PDF report (written in LaTeX). The report should include the implementation approach for the full assignment.
Additional Resources
- Hugging Face Transformers Documentation
- Attention Is All You Need (Transformer paper)
- LangChain Documentation
- LlamaIndex Documentation
Notes
- This module builds upon your PyTorch knowledge and introduces NLP-specific concepts and libraries.
- Pay special attention to the attention mechanism and its implementation in Transformers.
- For the full assignment, consider experimenting with pre-trained models and fine-tuning them for your specific task.
- As always, document your code thoroughly and use version control (Git) for your project.
- Submit your assignments via Gradescope.