Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.10295 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911105398341632 |
|---|---|
| author | Angel, Christian M. Ferraro, Francis |
| author_facet | Angel, Christian M. Ferraro, Francis |
| contents | 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%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10295 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Inductive Bias Extraction and Matching for LLM Prompts Angel, Christian M. Ferraro, Francis Computation and Language 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%. |
| title | Inductive Bias Extraction and Matching for LLM Prompts |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2508.10295 |