Language Model Query Language and Retrieval Augmented Generation

I am preparing for a talk at KI Navigator on the 21st of November about the Language Model Query Language and Retrieval Augmented Generation (RAG). These are the slides for the initial version of the talk View Presentation Slides

Background

Large Language Models (LLMs) have become increasingly powerful, but leveraging them effectively for specific tasks remains challenging. This talk explores how LMQL and RAG can enhance LLM capabilities and performance.

LMQL: Structured Querying for LLMs

LMQL allows for structured querying of language models, enabling more controlled and targeted interactions. Key features include:

• Wikipedia searches integration
• Multi-step reasoning capabilities
• Ability to define custom constraints and logic flows

Example LMQL query:

@lmql.query
async def norse_origins():
    '''lmql
    "Q: From which countries did the Norse originate?\n"
    "Action: Let us search Wikipedia for the term '[TERM]\n" where STOPS_AT(TERM, "'")
    wiki_result = await wikipedia(TERM)
    "Result: {wiki_result}\n"
    "Final Answer:[ANSWER]"
    '''

RAG Implementation and Evaluation

The talk demonstrated a RAG implementation, comparing generated answers with ground truth using three key metrics:

1. Context Recall
2. Factual Correctness
3. Faithfulness

Evaluation Results

The evaluation revealed significant challenges in RAG performance:

• Context Recall: 0.0667
• Factual Correctness: 0.1900
• Faithfulness: 0.0909

These surprisingly low scores highlight the need for improved retrieval and generation techniques.

Example Comparisons

The presentation included several examples comparing expected answers to generated ones:

1. Question about USA Supreme Court ruling on abortion
2. Query about the right to know the truth in human rights contexts
3. Inquiry about Ramsar site designation criteria

These examples illustrated discrepancies between expected and generated answers, emphasizing areas for improvement in RAG systems.

Key Takeaways

1. LMQL offers powerful capabilities for structured LLM interactions
2. Current RAG implementations face significant challenges in accuracy and relevance
3. There’s a substantial need for improved retrieval and generation techniques in RAG systems
4. Careful evaluation and comparison with ground truth are crucial for assessing LLM-based systems

Future Directions

The low performance metrics suggest several areas for future research and development:

1. Enhancing retrieval algorithms to improve context relevance
2. Developing more sophisticated generation techniques to increase factual correctness
3. Exploring ways to improve the faithfulness of generated responses to source material
4. Investigating the integration of LMQL techniques with RAG systems for potential performance boosts