LLM Hallucinations and Evaluation Framework Workshop

Introduction

• Host: Diana Chan Morgan from deeplearning.ai
• Guests: Special guests from Galileo
• Session Info:
  • Recorded and available for replay
  • Use chat link for questions
  • Developer Evangelists Setu and Jonathan available in chat

Event and Workshop Overview

• Focus on:
  • Research-backed metrics for evaluating input/output quality
  • Hallucinations in LLM applications
  • Evaluation and experimentation framework
  • Prompt engineering with RAG
  • Fine-tuning with personal data
  • Demo for implementation

Speakers Introduction

• Vikram Chathy: Co-founder and CEO at Galileo
  • Background: Product management at Google AI, Google Pay
• Endrio Son: Co-founder and CTO at Galileo
  • Background: Engineering leader at Uber AI, Michelangelo, Siri at Apple

Workshop Agenda

1. Need for LLM Experimentation and Evaluation Frameworks

   • Challenges with LLMs: Inputs and Outputs complexity
   • Importance of iterative experimentation and evaluation

2. Different Evaluation Methods and Usage

   • Output evaluation requires domain-specific metrics
   • Existing limitations in current methods

3. Emerging Hallucination Detection Metrics

   • Development of new metrics tailored for LLMs
   • Significance of understanding and mitigating hallucinations

4. Demo Walkthrough

   • Application of discussed methods and metrics
   • Implement mitigation strategies

LLMs Experimentation Details

• Input Side:
  • Complexity in model parameters, prompt templates, RAG context, etc.
• Output Side:
  • Diverse use cases require different evaluation methods
• Framework Necessity:
  • Need for concrete metrics to guide experimentation and evaluation

Galileo’s G LLM Studio

• Components:
  1. Prompt: Accelerate prompt engineering
  2. Fine Tune: High-quality data fine-tuning
  3. Monitoring: Real-time observability
• Griel Metric Store:
  • Research-backed metrics to reduce hallucinations

Hallucination Detection Techniques

1. Engram Matching: Measures output-reference overlap

   • Limitations: Requires ground truth, adaptability issues

2. Self-checking via LLM: LLMs assess own outputs

   • Limitations: Blackbox approach, lacks explainability

3. Statistical Properties: Token confidence analysis

   • Limitations: Granular, limited LLM API support

Chain Pole Method

• Approach: Combines features of existing methods
• Phases:
  1. Chaining: Detailed Chain of Thought prompting
  2. Polling: Ensembling technique to detect hallucinations
• Benefits:
  • High efficacy, low latency, cheap computation

Demonstration Highlights

• Prompt Engineering with RAG: Steps and metrics used
• Fine-Tuning with Own Data: Importance of data quality

Observability in Production

• Key Metrics:
  • Cost, latency, API failure rates
  • LLM correctness, context adherence

Conclusion

• Takeaway:
  • Metrics are crucial for guiding LLM experimentation
  • Emphasis on continuous improvement and real-time monitoring

Q&A Highlights

• Addressing hallucinations in real-time outputs
• Ensuring reliability with non-deterministic LLM behavior

Closing Remarks

• Survey for feedback
• Encouragement for continued learning and exploration in AI

Note: This summary captures key points from the workshop for effective study and review of LLM hallucination evaluation and mitigation strategies.