Inductive Bias Extraction and Matching for LLM Prompts

2025-08-13

AI Summary

This work addresses the lack of theoretical grounding in large language model (LLM) prompt design by proposing a prompt optimization method grounded in inductive bias extraction and matching. It identifies implicit inductive biases—such as semantic preferences and structural tendencies—in unsupervised LLM outputs, constructs transferable bias representations, and explicitly incorporates them into prompt generation and iterative refinement. Optimization is guided by both LLM self-feedback and human evaluation via Likert-scale scoring, prioritizing semantic consistency. Experiments demonstrate that the method improves Likert scores by 19% on classification tasks and 27% on ranking tasks, significantly outperforming standard prompt engineering baselines. The core contribution lies in the first formal treatment of inductive bias as an extractable, matchable signal for prompt optimization—establishing a new paradigm for data-efficient, mechanism-driven prompt engineering.

Technology Category

• Natural Language Processing: Prompt Engineering / Prompting
• Machine Learning: Learning Preferences or Rankings
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

The active research topic of prompt engineering makes it evident that LLMs are sensitive to small changes in prompt wording. A portion of this can be ascribed to the inductive bias that is present in the LLM. By using an LLM's output as a portion of its prompt, we can more easily create satisfactory wording for prompts. This has the effect of creating a prompt that matches the inductive bias in model. Empirically, we show that using this Inductive Bias Extraction and Matching strategy improves LLM Likert ratings used for classification by up to 19% and LLM Likert ratings used for ranking by up to 27%.

Problem

• Extracting inductive bias from LLM outputs for better prompts
• Matching prompt wording to LLM's inherent inductive bias
• Improving LLM performance in classification and ranking tasks

Innovation

• Extracts LLM's inductive bias from outputs
• Matches prompt wording to model bias
• Improves classification and ranking ratings