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DSP Framework Core Knowledge

Document Type: Knowledge Base
Created: 2025-10-20
Last Updated: 2025-10-20
Confidence Level: High
Source: Original DSP paper (arXiv:2212.14024) and framework analysis

Overview

The Demonstrate-Search-Predict (DSP) framework is a sophisticated approach for composing Language Models (LM) and Retrieval Models (RM) that goes beyond simple retrieve-then-read pipelines.

Core Architecture Principles

Three Fundamental Operations

1. Demonstrate

  • Purpose: Bootstrap pipeline-aware demonstrations that guide language models
  • Function: Create examples for smaller pipeline steps, not just final answers
  • Implementation: Part of program's explicit execution logic in original DSP
  • Purpose: Find relevant information to ground LM reasoning and predictions
  • Function: Strategic placement within program flow, often after Predict calls
  • Implementation: Enables multi-step knowledge retrieval during reasoning

3. Predict

  • Purpose: Generate grounded predictions and text transformations
  • Function: Used for intermediate steps (decomposing questions, formulating queries) and final output
  • Implementation: Context and retrieved information based processing

Compositional Strategy

DSP's core concept is systematic breakdown of knowledge-intensive problems into smaller, manageable transformations that LM and RM can handle reliably.

Key Characteristics: - Natural language texts passed between LM and RM in multiple steps - Developer acts as pipeline architect - High-level programs enable sophisticated multi-hop reasoning - Explicit control over pipeline design and composition

Performance Context

Documented Improvements: - 37-120% relative gains over vanilla GPT-3.5 in open-domain QA - 8-39% improvements over standard retrieve-then-read in multi-hop reasoning - 80-290% gains vs contemporaneous self-ask pipeline in conversational QA

Problem Solved

DSP addressed structural limitations of the then-dominant "retrieve-then-read" pipeline (December 2022): - Simple model was too simplistic for powerful LM-RM combinations - Need for complex, effective multi-stage pipelines - Required sophisticated composition beyond basic retrieval

Implementation Implications for AirsDSP

Core Requirements

  1. Compositional Architecture: Support for combining three operations in complex patterns
  2. Natural Language Interfaces: Text-based communication between components
  3. Pipeline Awareness: Context-sensitive processing understanding full pipeline
  4. Frozen Model Support: Work with existing models without fine-tuning

Architectural Considerations

  • Systematic problem decomposition mechanisms
  • Explicit pipeline control and design
  • Modular composition using standard control flow
  • Performance-focused implementation in Rust

Research Foundation Quality

Credibility: High - Based on Stanford NLP research team foundational paper Technical Depth: Comprehensive - Covers all three operations and interactions Implementation Relevance: High - Direct application to AirsDSP architecture Performance Evidence: Strong - Documented significant improvements across tasks