Inductive Bias Extraction and Matching for LLM Prompts
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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%.
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References · 9
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
- 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 923
- 2016A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Storiescited by 417
- 2023Large Language Models for Information Retrieval: A Surveycited by 331
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- 2022PASTA: A Dataset for Modeling Participant States in Narrativescited by 5
- 2022Learning Action Conditions from Instructional Manuals for Instruction Understandingcited by 4
- 2024SAGA: A Participant-specific Examination of Story Alternatives and Goal Applicability for a Deeper Understanding of Complex Eventscited by 2
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