2 posts tagged with "Machine Learning"

Key Challenges with Monolithic LMs​

• Hard to control, debug, and improve.
• Every AI system makes mistakes.
• Modular systems (Compound AI) address these challenges.

Compound AI Systems​

• Modular programs use LMs as specialized components.
• Examples:
  • Retrieval-Augmented Generation.
  • Multi-Hop Retrieval-Augmented Generation.
  • Compositional Report Generation.
• Benefits:
  • Quality: Reliable LM composition.
  • Control: Iterative improvement via tools.
  • Transparency: Debugging and user-facing attribution.
  • Efficiency: Use smaller LMs and offload control flow.
  • Inference-time Scaling: Search for better outputs.

Anatomy of LM Programs in DSPy​

• Modules:

  • Define strategies for tasks.
  • Example: MultiHop uses Chain of Thought and retrieval.

• Program Components:

  • Signature: Task definition.
  • Adapter: Maps input/output to prompts.
  • Predictor: Applies inference strategies.
  • Metrics: Define objectives and constraints.
  • Optimizer: Refines instructions for desired behavior.

DSPy Optimization Methods​

1. Bootstrap Few-shot:

   • Generate examples using rejection sampling.

2. Extending OPRO:

   • Optimize instructions through prompting.

3. MIPRO:

   • Jointly optimize instructions and few-shot examples using Bayesian learning.

Key Benefits of DSPy​

• Simplifies programming for LMs.
• Optimized prompts for accuracy and efficiency.
• Enables modularity and scalability in AI systems.

Lessons and Research Directions​

1. Natural Language Programming:
   • Programs are more accurate, controllable, and transparent.
   • High-level optimizers bootstrap prompts and instructions.
2. Natural Language Optimization:
   • Effective grounding and credit assignment are crucial.
   • Optimizing both instructions and demonstrations enhances performance.
3. Future Directions:
   • Focus on modularity, better inference strategies, and optimized LM usage.

Summary​

• Compound AI Systems make LMs modular and reliable.
• DSPy provides tools to build, optimize, and deploy modular AI systems.
• Emphasizes modularity and systematic optimization for AI progress.

Reasoning is pivotal for advancing LLM capabilities

Introduction​

• Expectations for AI: Solving complex math problems, discovering scientific theories, achieving AGI.
• Baseline Expectation: AI should emulate human-like learning with few examples.

Key Concepts​

• What is Missing in ML?
  • Reasoning: The ability to logically derive answers from minimal examples.

Toy Problem: Last Letter Concatenation​

• Problem

  : Extract the last letters of words and concatenate them.

  • Example: "Elon Musk" → "nk".

• Traditional ML: Requires significant labeled data.

• LLMs: Achieve 100% accuracy with one demonstration using reasoning.

Importance of Intermediate Steps​

• Humans solve problems through reasoning and intermediate steps.
• Example:
  • Input: "Elon Musk"
  • Reasoning: Last letter of "Elon" = "n", of "Musk" = "k".
  • Output: "nk".

Advancements in Reasoning Approaches​

1. Chain-of-Thought (CoT) Prompting
   • Breaking problems into logical steps.
   • Examples from math word problems demonstrate enhanced problem-solving accuracy.
2. Least-to-Most Prompting
   • Decomposing problems into easier sub-questions for gradual generalization.
3. Analogical Reasoning
   • Adapting solutions from related problems.
   • Example: Finding the area of a square by recalling distance formula logic.
4. Zero-Shot and Few-Shot CoT
   • Triggering reasoning without explicit examples.
5. Self-Consistency in Decoding
   • Sampling multiple responses to improve step-by-step reasoning accuracy.

Limitations​

• Distraction by Irrelevant Context
  • Adding irrelevant details significantly lowers performance.
  • Solution: Explicitly instructing the model to ignore distractions.
• Challenges in Self-Correction
  • LLMs can fail to self-correct errors, sometimes worsening correct answers.
  • Oracle feedback is essential for effective corrections.
• Premise Order Matters
  • Performance drops with re-ordered problem premises, emphasizing logical progression.

Practical Implications​

• Intermediate reasoning steps are crucial for solving serial problems.
• Techniques like self-debugging with unit tests are promising for future improvements.

Future Directions​

1. Defining the right problem is critical for progress.
2. Solving reasoning limitations by developing models that autonomously address these issues.