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Main Authors: Park, Bumjin, Lee, Jinsil, Choi, Jaesik
Format: Preprint
Published: 2025
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Online Access:https://arxiv.org/abs/2506.11068
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author Park, Bumjin
Lee, Jinsil
Choi, Jaesik
author_facet Park, Bumjin
Lee, Jinsil
Choi, Jaesik
contents Large language models (LLMs) are increasingly engaging in moral and ethical reasoning, where criteria for judgment are often unclear, even for humans. While LLM alignment studies cover many areas, one important yet underexplored area is how LLMs make judgments about obligations. This work reveals a strong tendency in LLMs to judge non-obligatory contexts as obligations when prompts are augmented with modal expressions such as must or ought to. We introduce this phenomenon as Deontological Keyword Bias (DKB). We find that LLMs judge over 90\% of commonsense scenarios as obligations when modal expressions are present. This tendency is consist across various LLM families, question types, and answer formats. To mitigate DKB, we propose a judgment strategy that integrates few-shot examples with reasoning prompts. This study sheds light on how modal expressions, as a form of linguistic framing, influence the normative decisions of LLMs and underscores the importance of addressing such biases to ensure judgment alignment.
format Preprint
id arxiv_https___arxiv_org_abs_2506_11068
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Deontological Keyword Bias: The Impact of Modal Expressions on Normative Judgments of Language Models
Park, Bumjin
Lee, Jinsil
Choi, Jaesik
Computation and Language
Large language models (LLMs) are increasingly engaging in moral and ethical reasoning, where criteria for judgment are often unclear, even for humans. While LLM alignment studies cover many areas, one important yet underexplored area is how LLMs make judgments about obligations. This work reveals a strong tendency in LLMs to judge non-obligatory contexts as obligations when prompts are augmented with modal expressions such as must or ought to. We introduce this phenomenon as Deontological Keyword Bias (DKB). We find that LLMs judge over 90\% of commonsense scenarios as obligations when modal expressions are present. This tendency is consist across various LLM families, question types, and answer formats. To mitigate DKB, we propose a judgment strategy that integrates few-shot examples with reasoning prompts. This study sheds light on how modal expressions, as a form of linguistic framing, influence the normative decisions of LLMs and underscores the importance of addressing such biases to ensure judgment alignment.
title Deontological Keyword Bias: The Impact of Modal Expressions on Normative Judgments of Language Models
topic Computation and Language
url https://arxiv.org/abs/2506.11068