Do Language Models Think Consistently? A Study of Value Preferences Across Varying Response Lengths (2026.findings-acl)
Copied to clipboard
| Challenge: | In short-form surveys and psychometric tests, value-related risks and preferences are often underexplored in practical settings. |
| Approach: | They compare short-form responses to long-form outputs to determine whether value preferences align with those expressed in long-term outputs. |
| Outcome: | The proposed method yields only modest gains in the consistency of value expression. |
Similar Papers
Are Large Language Models Consistent over Value-laden Questions? (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) appear to bias survey answers toward certain values . however, some argue that LLMs are inconsistent to simulate particular values - a recent study . |
| Approach: | They define value consistency as similarity of answers across paraphrases, related questions and multilingual translations of a question to English, Chinese, German, and Japanese. |
| Outcome: | The proposed model is consistent across paraphrases, use-cases, translations, and within a topic. |
Think Again! The Effect of Test-Time Compute on Preferences, Opinions, and Beliefs of Large Language Models (2025.acl-industry)
Copied to clipboard
| Challenge: | Large Language Models exhibit subjective preferences, opinions, and beliefs, which may shape their behavior, influence advice and recommendations, and potentially reinforce certain viewpoints. |
| Approach: | They developed a benchmark to assess LLMs’ subjective inclinations across societal, cultural, ethical, and personal domains. |
| Outcome: | The proposed benchmark assesses LLMs’ subjective inclinations across societal, cultural, ethical, and personal domains. |
Dissecting Human and LLM Preferences (2024.acl-long)
Copied to clipboard
| Challenge: | a recent study shows that human and Large Language Model preferences are important for model fine-tuning and evaluation. |
| Approach: | They dissect the preferences of human and 32 different Large Language Models to understand their quantitative composition. |
| Outcome: | The proposed model is compared with 32 different large language models using real-world user-model conversations. |
Your Mileage May Vary: How Empathy and Demographics Shape Human Preferences in LLM Responses (2025.findings-emnlp)
Copied to clipboard
| Challenge: | large language models (LLMs) increasingly assist subjective decision-making . prior work uses aggregate human judgments, but demographic variation and its linguistic drivers remain underexplored. |
| Approach: | They analyze how demographic background and empathy level correlate with LLM-generated dilemma responses . they also identify markers that predict group-level differences . |
| Outcome: | The authors show that demographic background and empathy level correlate with LLM preferences . their findings highlight the need for demographically informed LLM evaluations. |
Inertia in Moral and Value Judgments of Large Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Large Language Models behave non-deterministically, and prompting is a common method for steering their outputs. |
| Approach: | They use role-play at scale to study the value orientation and inertia of Large Language Models. |
| Outcome: | The proposed model keeps values skewed in one direction across persona settings. |
Revisiting LLM Value Probing Strategies: Are They Robust and Expressive? (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing value probing methods that capture in-context information and predict models’ real-world actions are limited and lack systematic comparisons. |
| Approach: | They compare three widely used value probing methods: token likelihood, sequence perplexity, and text generation. |
| Outcome: | The proposed methods exhibit large variances under non-semantic perturbations in prompts and option formats, with sequence perplexity being the most robust overall. |
LLM Tropes: Revealing Fine-Grained Values and Opinions in Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to evaluate latent values and opinions in large language models suffer from three notable shortcomings. |
| Approach: | They propose to analyze 156k LLM responses to 62 propositions of the Political Compass Test (PCT) generated by 6 LLMs using 420 prompt variations. |
| Outcome: | The proposed analysis of 156k LLM responses to the Political Compass Test (PCT) generated by 6 LLMs shows that tropes are recurrent and consistent across prompts. |
Do LLMs Align Human Values Regarding Social Biases? Judging and Explaining Social Biases with LLMs (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models can lead to undesired consequences when misaligned with human values . previous studies have shown misalignment of LLMs with human value using expert-designed or agent-based emulated bias scenarios . |
| Approach: | They investigate whether large language models (LLMs) are misaligned with human values . they find no significant differences in understanding of HVSB between LLMs . |
| Outcome: | The results show that large language models do not have lower misalignment rates and attack success rates . the study also shows that smaller language models have the ability to explain HVSB . |
Unintended Harms of Value-Aligned LLMs: Psychological and Empirical Insights (2025.acl-long)
Copied to clipboard
| Challenge: | Value-aligned LLMs are more prone to harmful behavior than fine-tuned models . value-aligned models generate text according to the aligned values, which can amplify harmful outcomes. |
| Approach: | They propose to use in-context alignment methods to enhance the safety of value-aligned LLMs. |
| Outcome: | The proposed methods improve value alignment and safety, the authors say . value-aligned models are more prone to harmful behavior than fine-tuned models . |
Probing the Plasticity and Correlation of LLM Value Systems: LLM Value Rankings are Not Stable (2026.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have similar value rankings but little is known about how susceptible they are to external influence and how different values are correlated with each other. |
| Approach: | They propose to use 6 different value transformation prompting methods to examine the plasticity of LLM value systems by comparing them with 8 LLMs. |
| Outcome: | The proposed methods are effective on 8 LLMs and 3 families. |