Falcon Research Overview

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Overview

Evaluating the research capabilities of Large Language Models (LLMs) requires sophisticated assessment of complex information synthesis, logical reasoning, and factual grounding. The Falcon: Deep Research Benchmark & Evaluation Framework addresses this challenge by providing an automated, structured evaluation system for LLM responses to sophisticated research prompts.

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Design Philosophy

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Core Principles

• Modularity: System components are separated for enhanced maintainability and extensibility
• Automation: Minimizes manual effort through scriptable and repeatable workflows
• Transparency: Explicit evaluation criteria and saved raw scores enable detailed analysis
• Objectivity: Aggregates multiple judge assessments to reduce individual bias
• Standardization: Consistent prompts and formats ensure comparable evaluations
• Configuration: Settings managed via environment variables for flexibility
• Pragmatism: Utilizes available APIs and libraries for efficient implementation

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Framework Components

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Source Code Access

The complete Falcon source code is available at:

• GitHub Repository: https://github.com/chima-org/falcon
• Detailed Design Documentation: report.md

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Research Prompts & Responses

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Prompt Selection

We curated 10 proprietary complex research questions across various domains:

• Academic research questions
• Industry-level analysis tasks
• Open-source intelligence gathering
• Recent information and news analysis

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Example Prompts

1. Creator Content Analysis: “Model the full conversion funnel generated by this week’s highest-engagement creator content on TikTok and Instagram specifically for whiteclaws and hard seltzers. Correlate engagement spikes to attributable sales lifts online and in-store, identify the creator tiers, content formats, and posting cadences that deliver the greatest incremental ROI.”

2. Consumer Behavior Forecast: “Using data gathered from recent news, forecast consumer behaviors with regards to Dunkin’s coffee and coffee in general for the upcoming month.”

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Evaluation Framework

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Subjective Criteria

1. Logical Correctness

   • Assesses internal consistency
   • Evaluates reasoning clarity

2. Thoroughness

   • Measures completeness
   • Evaluates depth relative to requirements

3. Hallucination Rate

   • Higher scores indicate lower hallucination rates
   • Validated through human-in-the-loop feedback

4. Factual Accuracy

   • Verifies claims using internet search
   • Iterative scoring process

5. Source Quality

   • Evaluates credibility of sources
   • Assesses information relevance

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Objective Criteria

1. Response Time

   • Measures generation completion time

2. Token Counts

   • Measures response length
   • Not directly impacting overall rating

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LLM Judges

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Selected Models

Three cutting-edge LLMs serve as judges:

• Claude 3.7 Sonnet
• GPT-4.1
• Gemini 2.5 Pro

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Search Integration

• Exa AI Web Search API for Claude 3.7 Sonnet
• Built-in search capabilities for GPT-4.1 and Gemini

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Scoring Methodology

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Calculation Approach

• Combines quantitative raw scores
• Qualitative bucketing into 1-5 ratings
• Hybrid model using Z-scores and absolute scores
• Category-specific computation methods
• Weighted averages from multiple judges

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Key Findings

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Model Performance

• No single model dominates across all criteria
• OpenAI and Gemini lead in comprehensive research
• xAI Grok 3 excels in speed-to-depth ratio
• Trade-offs between quality and response time

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Cost Considerations

Subscription-based pricing varies significantly:

• OpenAI: $20-200/month
• Anthropic: $100-200/month
• Gemini: $20/month
• Perplexity: Free-$20/month
• xAI: $30/month
• Manus AI: $2-10 per task

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Future Improvements

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Current Limitations

• Difficulty with ambiguous prompts
• Challenges with private/limited data
• Limited search capabilities

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Planned Enhancements

1. Expanded Coverage

   • Additional prompts and responses
   • More diverse evaluation aspects

2. Enhanced Evaluation

   • Integration of newer LLM judges
   • Advanced web search tools

3. Browser Use Integration

   • Automated prompt processing
   • Citation verification
   • Web page validation

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Conclusion

The Falcon framework provides a robust, automated evaluation system for assessing LLM research capabilities. Its multi-judge approach and comprehensive criteria enable objective assessment of both open-source and proprietary models, supporting enterprise-level research tasks and advanced analysis workflows.