{"work":{"id":"b8eecc06-3e78-4eba-9e1e-7c3bbebeaa4d","openalex_id":null,"doi":null,"arxiv_id":"2411.10109","raw_key":null,"title":"LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals","authors":null,"authors_text":null,"year":2024,"venue":"cs.AI","abstract":"Machine learning can predict human behavior well when substantial structured data and well-defined outcomes are available, but these models are typically limited to specific outcomes and cannot readily be applied to new domains. We test whether large language models (LLMs) can support a more general-purpose approach by building person-specific simulations (i.e., \"generative agents\") grounded in self-report data. Using data from a diverse national sample of 1,052 Americans, we build agents from (i) two-hour, semi-structured interviews (elicited using the American Voices Project interview schedule), (ii) structured surveys (the General Social Survey and Big Five personality inventory), or (iii) both sources combined. On held-out General Social Survey items, agent accuracy reached 83% (interview only), 82% (surveys only), and 86% (combined) of participants' two-week test-retest consistency, compared with agents prompted only with individuals' demographics (74%). Agents predicted personality traits and behaviors in experiments with similar accuracy, and reduced disparities in accuracy across racial and ideological groups relative to demographics-only baselines. Together, these results show that LLMs agents grounded in rich qualitative or quantitative self-report data can support general-purpose simulation of individuals across outcomes, without requiring task-specific training data.","external_url":"https://arxiv.org/abs/2411.10109","cited_by_count":null,"metadata_source":"pith","metadata_fetched_at":"2026-05-25T06:15:23.765419+00:00","pith_arxiv_id":"2411.10109","created_at":"2026-05-10T02:53:29.878361+00:00","updated_at":"2026-05-25T06:15:23.765419+00:00","title_quality_ok":true,"display_title":"LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals","render_title":"LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals"},"hub":{"state":{"work_id":"b8eecc06-3e78-4eba-9e1e-7c3bbebeaa4d","tier":"hub","tier_reason":"10+ Pith inbound or 1,000+ external citations","pith_inbound_count":43,"external_cited_by_count":null,"distinct_field_count":8,"first_pith_cited_at":"2025-02-12T15:27:07+00:00","last_pith_cited_at":"2026-05-21T07:32:16+00:00","author_build_status":"not_needed","summary_status":"needed","contexts_status":"needed","graph_status":"needed","ask_index_status":"not_needed","reader_status":"not_needed","recognition_status":"not_needed","updated_at":"2026-05-28T12:28:33.667435+00:00","tier_text":"hub"},"tier":"hub","role_counts":[{"context_role":"background","n":7},{"context_role":"method","n":1},{"context_role":"other","n":1}],"polarity_counts":[{"context_polarity":"background","n":7},{"context_polarity":"unclear","n":1},{"context_polarity":"use_method","n":1}],"runs":{},"summary":{},"graph":{},"authors":[]}}