# Inductive Bias Extraction and Matching for LLM Prompts

arXiv: https://arxiv.org/abs/2508.10295 · HTML: https://mlsanity.com/paper/2508.10295/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 29 references that are in the map.
- First posted on arXiv 14 Aug 2025.

## Builds on (most cited first, 9 of 9)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions](https://mlsanity.com/paper/2212.10509/) (2022)
- [A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories](https://mlsanity.com/paper/1604.01696/) (2016)
- [Large Language Models for Information Retrieval: A Survey](https://mlsanity.com/paper/2308.07107/) (2023)
- [Text Classification via Large Language Models](https://mlsanity.com/paper/2305.08377/) (2023)
- [PASTA: A Dataset for Modeling Participant States in Narratives](https://mlsanity.com/paper/2208.00329/) (2022)
- [Learning Action Conditions from Instructional Manuals for Instruction Understanding](https://mlsanity.com/paper/2205.12420/) (2022)
- [SAGA: A Participant-specific Examination of Story Alternatives and Goal Applicability for a Deeper Understanding of Complex Events](https://mlsanity.com/paper/2408.05793/) (2024)

## Abstract (arXiv)

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%.