Prompting recommendations

Author

Affiliation

Andrew Ellis

Virtuelle Akademie, Berner Fachhochschule

Recommendations for paper analysis

If you are using LLMs to analyze scientific papers, here are some recommendations for how to do so effectively.

LLM-Assisted Scientific Paper Analysis: A Comprehensive Guide

Pre-Reading Phase

1. Setup and Preparation

• Have the paper open in a format where you can easily copy text
• Keep a structured note-taking system ready
• Consider using multiple LLM providers (e.g., Claude, GPT-4) for cross-validation
• Prepare a basic template document for organizing findings

2. Initial Paper Assessment

1. Title and Abstract Analysis

   Prompt Template:
   "Please analyze this title and abstract focusing on:
   1. The main research question
   2. The key methodological approach
   3. The primary claimed findings
   4. Any potential red flags or limitations that should guide my reading
   Please maintain a critical perspective and highlight any assumptions or claims that require careful verification."

2. Background Context

   Prompt Template:
   "What are the key papers and concepts I should be familiar with to understand this research? 
   Please format the response as:
   1. Essential prerequisite concepts:
   2. Key related papers:
   3. Potential controversies in this field:
   Note: Please indicate if you're uncertain about any of these recommendations."

Deep Reading Phase

3. Methodological Analysis

1. Initial Method Review

   Prompt Template:
   "Please analyze this methods section, focusing on:
   1. Key methodological choices and their justification
   2. Potential alternative approaches not discussed
   3. Assumptions made by the authors
   4. Possible limitations or confounds
   Please maintain a skeptical perspective and highlight any methodological choices that require extra scrutiny."

2. Statistical Analysis Verification

   Prompt Template:
   "For these statistical methods:
   1. Are they appropriate for the research questions?
   2. What assumptions do these methods make?
   3. What alternative analyses might have been more appropriate?
   4. What potential confounds might affect these analyses?
   Please be specific about any concerns or limitations."

4. Results Interpretation

1. Data Presentation Analysis

   Prompt Template:
   "Please analyze these results focusing on:
   1. Whether the data supports the stated conclusions
   2. Alternative interpretations not discussed
   3. Potential confounding variables
   4. Missing analyses that would strengthen the findings
   Be explicitly critical and highlight any gaps between data and conclusions."

2. Verification Queries

   • Always ask the same question in multiple ways
   • Use contrary framings to test for sycophancy
   • Cross-reference critical points with other LLMs

5. Discussion Analysis

1. Claims Verification

   Prompt Template:
   "For each major claim in the discussion:
   1. Is it fully supported by the presented data?
   2. What alternative explanations exist?
   3. What additional evidence would strengthen this claim?
   4. What limitations might affect this interpretation?
   Please be specific about any gaps between evidence and claims."

Critical Analysis Phase

6. Structured Critique

1. Strength Assessment

   Prompt Template:
   "Please analyze the paper's strengths:
   1. Most robust findings
   2. Strongest methodological elements
   3. Most convincing arguments
   4. Most significant contributions
   Provide specific evidence for each point."

2. Weakness Assessment

   Prompt Template:
   "Please analyze the paper's weaknesses:
   1. Methodological limitations
   2. Unsupported assumptions
   3. Alternative interpretations
   4. Missing controls or analyses
   Be specific and explain why each is important."

7. Cross-Validation Strategies

A. Multiple Model Approach

1. Ask the same questions to different LLMs
2. Compare responses for consistency
3. Investigate discrepancies
4. Document variations in interpretation

B. Reverse Questioning

1. Ask for critiques of both positive and negative interpretations
2. Challenge both supportive and critical responses
3. Compare how the model handles opposing viewpoints
4. Document any tendency to agree with the questioner

Documentation and Synthesis

8. Creating a Structured Analysis

1. Main Findings Summary
   • Document key claims and evidence
   • Note areas of uncertainty
   • List verified vs. questionable conclusions
   • Include cross-validation results
2. Limitations Documentation
   • Record methodological concerns
   • Note potential biases
   • Document areas needing verification
   • List missing analyses
3. Future Directions
   • Identify logical next steps
   • Note gaps in current evidence
   • Suggest methodological improvements
   • List potential applications

Best Practices and Safeguards

9. Quality Control Measures

A. Bias Prevention

• Rotate question framing
• Use multiple prompting strategies
• Cross-validate critical findings
• Document model uncertainties

B. Documentation Requirements

• Record all significant prompts
• Note areas of model disagreement
• Track changes in model responses
• Document verification steps

C. Red Flags to Watch For

1. Model significantly changes stance when questioned
2. Responses align too readily with suggestions
3. Inconsistent technical explanations
4. Overly confident answers about uncertain topics

10. Common Pitfalls to Avoid

1. Over-reliance Traps
   • Accepting model interpretations without verification
   • Using single prompts for complex issues
   • Failing to cross-validate critical points
   • Neglecting human expertise
2. Bias Introduction
   • Leading questions that suggest preferred answers
   • Failing to challenge confirmatory responses
   • Accepting responses that align with expectations
   • Neglecting alternative viewpoints

Implementation Tips

11. Practical Workflow Integration

1. Setup Phase
   • Prepare standard prompts
   • Create documentation templates
   • Set up cross-validation system
   • Establish verification protocols
2. Execution Phase
   • Follow structured analysis process
   • Document all significant interactions
   • Cross-validate critical findings
   • Maintain skeptical perspective
3. Review Phase
   • Verify key conclusions
   • Check for bias patterns
   • Review documentation completeness
   • Validate critical interpretations

Conclusion

Success in using LLMs for paper analysis requires:

1. Structured approach to manage biases
2. Robust verification processes
3. Clear documentation practices
4. Regular validation of findings

Remember: LLMs are tools to assist analysis, not replace critical thinking. Always maintain skepticism and verify important conclusions through multiple methods.

Prompting strategies

Basic prompting strategies

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Intermediate prompting strategies

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Explore these prompting guides

Learn prompting: An comprehensive guide to prompt engineering.

Learn prompting

Prompting guide: A more technical guide to prompting

Prompting guide: DAIR.AI (Democratizing Artificial Intelligence Research, Education, and Technologies). The guide is licensed under an MIT license.

Further reading

Schulhoff et al. (2024) provides a comprehensive overview of the current state of prompting research. Zamfirescu-Pereira et al. (2023) discusses the capabilities of non-experts at designing prompts for LLMs.

References

Schulhoff, Sander, Michael Ilie, Nishant Balepur, Konstantine Kahadze, Amanda Liu, Chenglei Si, Yinheng Li, et al. 2024. “The Prompt Report: A Systematic Survey of Prompting Techniques.” June 6, 2024. http://arxiv.org/abs/2406.06608.

Zamfirescu-Pereira, J. D., Richmond Y. Wong, Bjoern Hartmann, and Qian Yang. 2023. “Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts.” In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 1–21. Hamburg Germany: ACM. https://doi.org/10.1145/3544548.3581388.