Unintended Harms of Value-Aligned LLMs: Psychological and Empirical Insights

• URL: http://arxiv.org/abs/2506.06404v1
• Date: Fri, 06 Jun 2025 07:03:12 GMT
• Title: Unintended Harms of Value-Aligned LLMs: Psychological and Empirical Insights
• Authors: Sooyung Choi, Jaehyeok Lee, Xiaoyuan Yi, Jing Yao, Xing Xie, JinYeong Bak,
• Abstract summary: The application scope of Large Language Models (LLMs) continues to expand, leading to increasing interest in personalized LLMs that align with human values.<n> aligning these models with individual values raises significant safety concerns, as certain values may correlate with harmful information.<n>This study offers insights into the "black box" of value alignment and proposes in-context alignment methods to enhance the safety of value-aligned LLMs.
• Score: 30.69754256601697
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The application scope of Large Language Models (LLMs) continues to expand, leading to increasing interest in personalized LLMs that align with human values. However, aligning these models with individual values raises significant safety concerns, as certain values may correlate with harmful information. In this paper, we identify specific safety risks associated with value-aligned LLMs and investigate the psychological principles behind these challenges. Our findings reveal two key insights. (1) Value-aligned LLMs are more prone to harmful behavior compared to non-fine-tuned models and exhibit slightly higher risks in traditional safety evaluations than other fine-tuned models. (2) These safety issues arise because value-aligned LLMs genuinely generate text according to the aligned values, which can amplify harmful outcomes. Using a dataset with detailed safety categories, we find significant correlations between value alignment and safety risks, supported by psychological hypotheses. This study offers insights into the "black box" of value alignment and proposes in-context alignment methods to enhance the safety of value-aligned LLMs.

Related papers

• $\texttt{SAGE}$: A Generic Framework for LLM Safety Evaluation [9.935219917903858]
  This paper introduces the $texttSAGE$ (Safety AI Generic Evaluation) framework.<n>$texttSAGE$ is an automated modular framework designed for customized and dynamic harm evaluations.<n>Our experiments with multi-turn conversational evaluations revealed a concerning finding that harm steadily increases with conversation length.
  arXiv  Detail & Related papers  (2025-04-28T11:01:08Z)
• Fundamental Safety-Capability Trade-offs in Fine-tuning Large Language Models [92.38300626647342]
  Fine-tuning Large Language Models (LLMs) on some task-specific datasets has been a primary use of LLMs.<n>This paper presents a theoretical framework for understanding the interplay between safety and capability in two primary safety-aware LLM fine-tuning strategies.
  arXiv  Detail & Related papers  (2025-03-24T20:41:57Z)
• Value Compass Benchmarks: A Platform for Fundamental and Validated Evaluation of LLMs Values [76.70893269183684]
  Large Language Models (LLMs) achieve remarkable breakthroughs.<n> aligning their values with humans has become imperative for their responsible development.<n>There still lack evaluations of LLMs values that fulfill three desirable goals.
  arXiv  Detail & Related papers  (2025-01-13T05:53:56Z)
• SafetyAnalyst: Interpretable, Transparent, and Steerable Safety Moderation for AI Behavior [56.10557932893919]
  We present SafetyAnalyst, a novel AI safety moderation framework.<n>Given an AI behavior, SafetyAnalyst uses chain-of-thought reasoning to analyze its potential consequences.<n>It aggregates effects into a harmfulness score using 28 fully interpretable weight parameters.
  arXiv  Detail & Related papers  (2024-10-22T03:38:37Z)
• Unveiling the Misuse Potential of Base Large Language Models via In-Context Learning [61.2224355547598]
  Open-sourcing of large language models (LLMs) accelerates application development, innovation, and scientific progress. Our investigation exposes a critical oversight in this belief. By deploying carefully designed demonstrations, our research demonstrates that base LLMs could effectively interpret and execute malicious instructions.
  arXiv  Detail & Related papers  (2024-04-16T13:22:54Z)
• AEGIS: Online Adaptive AI Content Safety Moderation with Ensemble of LLM Experts [0.0]
  As Large Language Models (LLMs) and generative AI become more widespread, the content safety risks associated with their use also increase. We find a notable deficiency in high-quality content safety datasets and benchmarks that comprehensively cover a wide range of critical safety areas. To address this, we define a broad content safety risk taxonomy, comprising 13 critical risk and 9 sparse risk categories.
  arXiv  Detail & Related papers  (2024-04-09T03:54:28Z)
• ALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety through Red Teaming [64.86326523181553]
  ALERT is a large-scale benchmark to assess safety based on a novel fine-grained risk taxonomy. It aims to identify vulnerabilities, inform improvements, and enhance the overall safety of the language models.
  arXiv  Detail & Related papers  (2024-04-06T15:01:47Z)
• Fake Alignment: Are LLMs Really Aligned Well? [91.26543768665778]
  This study investigates the substantial discrepancy in performance between multiple-choice questions and open-ended questions. Inspired by research on jailbreak attack patterns, we argue this is caused by mismatched generalization.
  arXiv  Detail & Related papers  (2023-11-10T08:01:23Z)
• Denevil: Towards Deciphering and Navigating the Ethical Values of Large Language Models via Instruction Learning [36.66806788879868]
  Large Language Models (LLMs) have made unprecedented breakthroughs, yet their integration into everyday life might raise societal risks due to generated unethical content. This work delves into ethical values utilizing Moral Foundation Theory.
  arXiv  Detail & Related papers  (2023-10-17T07:42:40Z)
• Heterogeneous Value Alignment Evaluation for Large Language Models [91.96728871418]
  Large Language Models (LLMs) have made it crucial to align their values with those of humans. We propose a Heterogeneous Value Alignment Evaluation (HVAE) system to assess the success of aligning LLMs with heterogeneous values.
  arXiv  Detail & Related papers  (2023-05-26T02:34:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.