String Seed of Thought: Prompting LLMs for Distribution-Faithful and Diverse Generation

Overview

The paper presents 'String Seed of Thought' (SSoT), a new prompting technique for large language models (LLMs) aimed at improving their ability to generate responses that are both diverse and faithful to a target distribution. This is particularly important for applications that require non-deterministic outputs.

Key Insights

• SSoT Method: Introduces a method where LLMs first generate a random string to create entropy, then manipulate this string to produce a final answer.
  • Evidence: Demonstrated improvement in Probabilistic Instruction Following (PIF) tasks.
  • Verifiable: Yes, through experiments on NoveltyBench.
• Performance: SSoT significantly enhances LLMs' ability to generate diverse responses, approaching the performance of a pseudo-random number generator.
  • Evidence: Experimental results on NoveltyBench.
  • Verifiable: Yes.

BFSI Relevance

• Why Relevant: Enhancing LLMs' ability to generate diverse and distribution-faithful responses can improve customer interaction simulations and risk modeling.
• Primary Sector: Financial Services
• Subsectors: Asset Management, Risk Management
• Actionable Implications:
  • Implement SSoT in customer service chatbots to improve interaction quality.
  • Use in risk modeling to simulate a wider range of scenarios.