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Venn Diagram Prompting : Accelerating Comprehension with Scaffolding Effect

Sakshi Mahendru, Tejul Pandit

TL;DR

VD prompting introduces a set-theoretic, Venn diagram-inspired prompting method that enables a single LLM call to synthesize information from multiple long-context documents. By organizing data into overlapping and unique components and citing sources, it reduces position bias and improves traceability. Across four diverse datasets, VD prompting matches or surpasses carefully crafted instruction prompts in both RAGAS metrics and LLM-as-a-judge evaluations, showing robust performance on long-context QA. This approach has practical implications for legal, medical, and financial domains, where reliable, source-supported reasoning over lengthy documents is critical.

Abstract

We introduce Venn Diagram (VD) Prompting, an innovative prompting technique which allows Large Language Models (LLMs) to combine and synthesize information across complex, diverse and long-context documents in knowledge-intensive question-answering tasks. Generating answers from multiple documents involves numerous steps to extract relevant and unique information and amalgamate it into a cohesive response. To improve the quality of the final answer, multiple LLM calls or pretrained models are used to perform different tasks such as summarization, reorganization and customization. The approach covered in the paper focuses on replacing the multi-step strategy via a single LLM call using VD prompting. Our proposed technique also aims to eliminate the inherent position bias in the LLMs, enhancing consistency in answers by removing sensitivity to the sequence of input information. It overcomes the challenge of inconsistency traditionally associated with varying input sequences. We also explore the practical applications of the VD prompt based on our examination of the prompt's outcomes. In the experiments performed on four public benchmark question-answering datasets, VD prompting continually matches or surpasses the performance of a meticulously crafted instruction prompt which adheres to optimal guidelines and practices.

Venn Diagram Prompting : Accelerating Comprehension with Scaffolding Effect

TL;DR

VD prompting introduces a set-theoretic, Venn diagram-inspired prompting method that enables a single LLM call to synthesize information from multiple long-context documents. By organizing data into overlapping and unique components and citing sources, it reduces position bias and improves traceability. Across four diverse datasets, VD prompting matches or surpasses carefully crafted instruction prompts in both RAGAS metrics and LLM-as-a-judge evaluations, showing robust performance on long-context QA. This approach has practical implications for legal, medical, and financial domains, where reliable, source-supported reasoning over lengthy documents is critical.

Abstract

We introduce Venn Diagram (VD) Prompting, an innovative prompting technique which allows Large Language Models (LLMs) to combine and synthesize information across complex, diverse and long-context documents in knowledge-intensive question-answering tasks. Generating answers from multiple documents involves numerous steps to extract relevant and unique information and amalgamate it into a cohesive response. To improve the quality of the final answer, multiple LLM calls or pretrained models are used to perform different tasks such as summarization, reorganization and customization. The approach covered in the paper focuses on replacing the multi-step strategy via a single LLM call using VD prompting. Our proposed technique also aims to eliminate the inherent position bias in the LLMs, enhancing consistency in answers by removing sensitivity to the sequence of input information. It overcomes the challenge of inconsistency traditionally associated with varying input sequences. We also explore the practical applications of the VD prompt based on our examination of the prompt's outcomes. In the experiments performed on four public benchmark question-answering datasets, VD prompting continually matches or surpasses the performance of a meticulously crafted instruction prompt which adheres to optimal guidelines and practices.
Paper Structure (19 sections, 1 equation, 8 figures, 3 tables)

This paper contains 19 sections, 1 equation, 8 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Prior Findings
  4. Challenges
  5. Approach
  6. Experimental Setup
  7. Dataset Description
  8. Implementation Tools and Setup
  9. LLM Model
  10. Prompts
  11. Evaluation Criteria
  12. RAGAS
  13. LLM-as-a-Judge Model
  14. Evaluation Results
  15. Results from RAGAS
  16. ...and 4 more sections

Figures (8)

  • Figure 1: A unified RAG frameworkli2022survey with basic workflow and VD prompt proposed workflow which modifies the generation component. The new workflow presents an innovative approach that condenses the multiple steps of the original workflow via Venn diagram prompting.
  • Figure 2: Comparison between standard and VD prompt for the same query and comparison with Ground Truth Response
  • Figure 3: (a) response generated using standard prompt, (b) response generated using VD prompt
  • Figure 4: Description of each step taken by VD prompt (a) identification of source(s) provided, (b) find overlapping areas w.r.t. query, (c) find unique areas w.r.t. query, (d) explanation for overlapping areas (e) explanation for unique areas
  • Figure 5: Representation of VD prompt example using Venn Diagram
  • ...and 3 more figures