Do Language Models Think Consistently? A Study of Value Preferences Across Varying Response Lengths

ACL ARR 2025 May Submission5317 Authors

Abstract:

Evaluations of LLMs’ ethical risks and value inclinations often rely on short-form surveys and psychometric tests, yet real-world use involves long-form, open-ended responses—leaving value-related risks and preferences in practical settings largely underexplored.
In this work, we ask: Do value preferences inferred from short-form tests align with those expressed in long-form outputs? To address this question, we compare value preferences elicited from short-form reactions and long-form responses, varying the number of arguments in the latter to capture users' differing verbosity preferences. Analyzing five LLMs (llama3-8b, gemma2-9b, mistral-7b, qwen2-7b, and olmo-7b), we find (1) a weak correlation between value preferences inferred from short-form and long-form responses across varying argument counts, and (2) similarly weak correlation between preferences derived from any two distinct long-form generation settings. (3} Alignment yields only modest gains in the consistency of value expression. Further, we examine how long-form generation attributes relate to value preferences, finding that argument specificity negatively correlates with preference strength, while representation across scenarios shows a positive correlation. Our findings underscore the need for more robust methods to ensure consistent value expression across diverse applications.

Paper Type: Long

Research Area: Computational Social Science and Cultural Analytics

Research Area Keywords: Value Alignment, Ethical Dilemma, Value Preferences, Consistency

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 5317