Perspective-Driven Research

Introduction

Single-perspective research often leads to biased or incomplete coverage. Perspective-driven research addresses this by systematically exploring a topic from multiple angles—such as historical, economic, ethical, or technical viewpoints. This approach simulates the curiosity-driven exploration of a thorough human researcher.

System Architecture

A complete perspective-driven research system consists of three main stages:

1. Perspective Generation: Identifying the most relevant lenses through which to view the topic.
2. Guided Questioning: Formulating specific questions for each perspective.
3. Multi-Perspective Retrieval: Gathering and synthesizing information for each viewpoint.

1. Generating Perspectives

First, we define a module to brainstorm diverse perspectives.

class PerspectiveGenerator(dspy.Module):
    def __init__(self):
        super().__init__()
        self.generate_perspectives = dspy.ChainOfThought("topic -> perspectives, rationale")

    def forward(self, topic):
        return self.generate_perspectives(topic=topic)

2. Perspective-Guided Questioning

Once perspectives are defined, the system generates targeted questions to drive the search.

class PerspectiveQuestionGenerator(dspy.Module):
    def __init__(self):
        super().__init__()
        self.generate_questions = dspy.ChainOfThought("topic, perspective -> focused_questions")

Advanced Features

Dynamic Perspective Expansion

A sophisticated system can dynamically identify gaps in its current understanding and spawn new perspectives on the fly.

self.identify_gaps = dspy.ChainOfThought(
    "topic, current_perspectives -> missing_perspectives"
)

Cross-Perspective Synthesis

Finally, the system must integrate findings, resolving conflicts and highlighting connections between different viewpoints.

Practical Example

Applying this to a topic like "The Impact of Social Media on Mental Health" might yield perspectives such as:

• Psychological: Effects on self-esteem and anxiety.
• Sociological: Changes in community dynamics and communication.
• Technological: Algorithm design and addictive patterns.

Each leads to distinct search queries and a richer final summary.