{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/aligning-with-whom-large-language-models-have","title":"Sociodemographic Prompting is Not Yet an Effective Approach for Simulating Subjective Judgments with LLMs","arxiv_id":"2311.09730","date":"2023-11-16","proceeding":null,"authors":["Huaman Sun","Jiaxin Pei","MinJe Choi","David Jurgens"],"abstract":"Human judgments are inherently subjective and are actively affected by personal traits such as gender and ethnicity. While Large Language Models (LLMs) are widely used to simulate human responses across diverse contexts, their ability to account for demographic differences in subjective tasks remains uncertain. In this study, leveraging the POPQUORN dataset, we evaluate nine popular LLMs on their ability to understand demographic differences in two subjective judgment tasks: politeness and offensiveness. We find that in zero-shot settings, most models' predictions for both tasks align more closely with labels from White participants than those from Asian or Black participants, while only a minor gender bias favoring women appears in the politeness task. Furthermore, sociodemographic prompting does not consistently improve and, in some cases, worsens LLMs' ability to perceive language from specific sub-populations. These findings highlight potential demographic biases in LLMs when performing subjective judgment tasks and underscore the limitations of sociodemographic prompting as a strategy to achieve pluralistic alignment. Code and data are available at: https://github.com/Jiaxin-Pei/LLM-as-Subjective-Judge.","url_abs":"https://arxiv.org/abs/2311.09730v2","url_pdf":"https://arxiv.org/pdf/2311.09730v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"aligning-with-whom-large-language-models-have","repo_url":"https://github.com/jiaxin-pei/llm-as-subjective-judge","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"aligning-with-whom-large-language-models-have","repo_url":"https://github.com/jiaxin-pei/llm-group-bias","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2311.09730","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.09730"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/jiaxin-pei/llm-as-subjective-judge","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/jiaxin-pei/llm-group-bias","reach":{"status":"ok"}}],"summary":{"ran_draft_wrong":1,"ran_honours":1,"ran":1},"by_repo_kind":{"official":{"samples":3,"ran":3,"repositories":2}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"ddc71db3edf9f508","entry":"add_message","repo":"jiaxin-pei/llm-as-subjective-judge","repo_kind":"official","path":"LLM_infer/inference_hf.py","file_url":"https://github.com/jiaxin-pei/llm-as-subjective-judge/blob/HEAD/LLM_infer/inference_hf.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"ddc71db3edf9f508"}},{"code_sha256_prefix":"b85e5c124ae0f07f","entry":"func_1","repo":"jiaxin-pei/llm-as-subjective-judge","repo_kind":"official","path":"LLM_infer/inference_hf.py","file_url":"https://github.com/jiaxin-pei/llm-as-subjective-judge/blob/HEAD/LLM_infer/inference_hf.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"b85e5c124ae0f07f"}},{"code_sha256_prefix":"e998ffa2acd18f26","entry":"func_1","repo":"jiaxin-pei/llm-group-bias","repo_kind":"official","path":"archived/preliminary/random_experiment/adjustment.py","file_url":"https://github.com/jiaxin-pei/llm-group-bias/blob/HEAD/archived/preliminary/random_experiment/adjustment.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"e998ffa2acd18f26"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}