[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-293698-en":3,"doc-seo-293698-105":31,"detail-sidebar-cat-0-en-105":99},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30,"preview_page_count":4},293698,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Are the Values of LLMs Structurally Aligned with Humans? - A Causal Perspective","As large language models (LLMs) increasingly enter critical applications, aligning their behavior with human values poses major challenges. Existing RLHF-style methods cover limited, coarse-grained value sets and obscure the correlations among values, making value steering explanations unclear and often accompanied by side effects. The work proposes a latent causal value graph and uses it to enable lightweight role-based prompting and sparse autoencoder (SAE) steering, improving control and reducing unexpected outcomes. Experiments on Gemma-2B-IT and Llama3-8B-IT support effectiveness and controllability.","Are the Values of LLMs Structurally Aligned with Humans?  \nA Causal Perspective  \nYipeng Kang1 , Junqi Wang1 , Yexin Li1 , Mengmeng Wang1 , Wenming Tu1 , Quansen Wang1,3 , Hengli Li1,3 , Tingjun Wu4 ,  \nXue Feng1 , Fangwei Zhong2,1 , Zilong Zheng1,B  \n1 State Key Laboratory of General Artificial Intellligence, BIGAI  \n2Beijing Normal University, 3Peking University, 4Tsinghua University {kangyipeng, zlzheng}@bigai.ai  \nAbstract  \nAs large language models (LLMs) become increasingly integrated into critical applications, aligning their behavior with human values presents significant challenges. Current methods, such as Reinforcement Learning from Human Feedback (RLHF), typically focus on a limited set of coarse-grained values and are resource-intensive. Moreover, the correlations between these values remain implicit, leading to unclear explanations for value-steering outcomes. Our work argues that a latent causal value graph underlies the value dimensions of LLMs and that, despite alignment training, this structure remains significantly different from human value systems. We leverage these causal value graphs to guide two lightweight value-steering methods: role-based prompting and sparse autoencoder (SAE) steering, effectively mitigating unexpected side effects. Furthermore, SAE provides a more finegrained approach to value steering. Experiments on Gemma-2B-IT and Llama3-8B-IT demonstrate the effectiveness and controllability of our methods.  \n1 Introduction  \nThe rapid advancement and widespread deployment of large language models (LLMs) have revolutionized a range of fields, from natural language processing to decision-making systems (Huang et al., 2024b) . These models, powered by vast amounts of data and sophisticated algorithms, have demonstrated remarkable abilities in various domains. However, as LLMs are increasingly deployed in critical applications, ensuring their alignment with human values and societal norms has become a pressing concern. Misalignment between LLM behaviors and ethical standards can lead to unintended, or even harmful consequences. As a result, value alignment, which aims to ensure that the actions and outputs of these models are consistent with human values has emerged as a pivotal  \nValue-oriented Questions  \n\n| Uncertainty Avoidance:\u003Cbr>Should I accept the current situation unless the problems are truly severe and unrecoverable?\u003Cbr>|\n| --- |\n| Breadth of Interest:\u003Cbr>Should I find political discussions interesting? |\n| Social Cynicism: \u003Cbr>\u003Cbr>\u003Cbr>Are young people impulsive and unreliable? |\n\nLLM Internal Values Causality  \nThought and Answers  \n…  \n…  \nFigure 1: Steering multiple causally related value dimensions in LLMs. When we use prompts or sparse autoencoders to steer certain dimensions of a large model, other values will correspondingly change.  \nchallenge to the research community.  \nCurrent approaches to value alignment typically focus on a few core values, such as the 3H: helpfulness, harmlessness, and honesty, using algorithms like Reinforcement Learning from Human Feedback (RLHF) (Ouyang et al., 2022) and constitutional learning (Bai et al., 2022) . While this paradigm has proven effective in guiding models toward certain desirable behaviors, human values encompass a much broader spectrum, often spanning hundreds of distinct dimensions with intricate and interconnected substructures (Schwartz and Boehnke, 2004) . When LLMs are deployed, these value systems often remain implicit, with their underlying structures and causal relationships poorly understood. This lack of clarity leads to unpredictable effects on alternative dimensions when steering specific values. Another issue with these alignment processes is their resource-intensiveness, requiring considerable computational power, human feedback data, and time for fine-tuning. As a result, it is impractical to steer LLMs toward each of the numerous human value dimensions in realtime. To effectively align with a broader range of  \n23147 ","cbCain0rrOAhuvOF","https://ap.wps.com/l/cbCain0rrOAhuvOF","pdf",1719833,10,1,15,"English","en",105,"# Abstract\n# 1 Introduction\n## Value-oriented Questions\n## LLM Internal Values Causality","[{\"question\":\"What limitations do current value-alignment methods face?\",\"answer\":\"They typically focus on a small set of coarse-grained values, are resource-intensive, and the correlations between values are implicit, which makes steering outcomes difficult to explain and can produce unexpected side effects.\"},{\"question\":\"What is the paper’s main causal idea for value alignment?\",\"answer\":\"It argues that a latent causal value graph underlies LLM value dimensions, and that alignment training does not fully make this structure match human value systems.\"},{\"question\":\"How do the proposed methods guide value steering, and what do they improve?\",\"answer\":\"The paper uses the causal value graphs to guide two lightweight approaches: role-based prompting and sparse autoencoder (SAE) steering. These mitigate unexpected side effects, and SAE offers a more fine-grained way to steer values.\"}]","Are the Values of LLMs Structurally Aligned with Humans? - A Causal Perspective | PDF",1789667715,38,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":93,"head_meta":95,"extra_data":97,"updated_unix":98},"are-the-values-of-llms-structurally-aligned-with-humans-a-causal-perspective","",{"@graph":37,"@context":92},[38,55,75],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/are-the-values-of-llms-structurally-aligned-with-humans-a-causal-perspective/293698/",4,{"url":53,"name":13,"@type":56,"image":57,"author":62,"headline":13,"publisher":64,"fileFormat":67,"inLanguage":24,"description":14,"dateModified":68,"datePublished":69,"encodingFormat":67,"isAccessibleForFree":70,"interactionStatistic":71},"DigitalDocument",{"url":58,"@type":59,"width":60,"height":61},"https://docshare.wps.com/thumbnails/are-the-values-of-llms-structurally-aligned-with-humans-a-causal-perspective/293698.png","ImageObject",300,407,{"name":9,"@type":63},"Person",{"url":42,"name":65,"@type":66},"DocShare","Organization","application/pdf","2026-10-19","2026-09-17",true,{"@type":72,"interactionType":73,"userInteractionCount":20},"InteractionCounter",{"@type":74},"ViewAction",{"@type":76,"mainEntity":77},"FAQPage",[78,84,88],{"name":79,"@type":80,"acceptedAnswer":81},"What limitations do current value-alignment methods face?","Question",{"text":82,"@type":83},"They typically focus on a small set of coarse-grained values, are resource-intensive, and the correlations between values are implicit, which makes steering outcomes difficult to explain and can produce unexpected side effects.","Answer",{"name":85,"@type":80,"acceptedAnswer":86},"What is the paper’s main causal idea for value alignment?",{"text":87,"@type":83},"It argues that a latent causal value graph underlies LLM value dimensions, and that alignment training does not fully make this structure match human value systems.",{"name":89,"@type":80,"acceptedAnswer":90},"How do the proposed methods guide value steering, and what do they improve?",{"text":91,"@type":83},"The paper uses the causal value graphs to guide two lightweight approaches: role-based prompting and sparse autoencoder (SAE) steering. 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