{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:DN6CUOOMRXPZBDAPGAJHZ7NKIW","short_pith_number":"pith:DN6CUOOM","schema_version":"1.0","canonical_sha256":"1b7c2a39cc8ddf908c0f30127cfdaa45b09d3745879f6d3376183f19c2c53c72","source":{"kind":"arxiv","id":"2411.05040","version":1},"attestation_state":"computed","paper":{"title":"Bottom-Up and Top-Down Analysis of Values, Agendas, and Observations in Corpora and LLMs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Christopher Miller, Drisana Mosaphir, Jeffrey Rye, Jeremy Gottlieb, Matthew McLure, Micah Goldwater, Noam Benkler, Robert P. Goldman, Ruta Wheelock, Scott E. Friedman, Sonja M. Schmer-Galunder","submitted_at":"2024-11-06T18:51:04Z","abstract_excerpt":"Large language models (LLMs) generate diverse, situated, persuasive texts from a plurality of potential perspectives, influenced heavily by their prompts and training data. As part of LLM adoption, we seek to characterize - and ideally, manage - the socio-cultural values that they express, for reasons of safety, accuracy, inclusion, and cultural fidelity. We present a validated approach to automatically (1) extracting heterogeneous latent value propositions from texts, (2) assessing resonance and conflict of values with texts, and (3) combining these operations to characterize the pluralistic "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2411.05040","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-06T18:51:04Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"40896f6d553dea0a2c61be257d730a504d83dcec27ad1bae06cf0edc4e955002","abstract_canon_sha256":"d4d506d60c0fd8d123e9985978636707d174c6672ac31ad38ee81381fd43823d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:32:51.690880Z","signature_b64":"JfM9ocJ4yUMe/+nAN42T8TTic3176meWCT5dJ1y1viGNGEB5/VeFL3AchPJAetmBGC8bNijSoQ3coJw4XJmiBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1b7c2a39cc8ddf908c0f30127cfdaa45b09d3745879f6d3376183f19c2c53c72","last_reissued_at":"2026-07-05T09:32:51.690457Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:32:51.690457Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bottom-Up and Top-Down Analysis of Values, Agendas, and Observations in Corpora and LLMs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Christopher Miller, Drisana Mosaphir, Jeffrey Rye, Jeremy Gottlieb, Matthew McLure, Micah Goldwater, Noam Benkler, Robert P. Goldman, Ruta Wheelock, Scott E. Friedman, Sonja M. Schmer-Galunder","submitted_at":"2024-11-06T18:51:04Z","abstract_excerpt":"Large language models (LLMs) generate diverse, situated, persuasive texts from a plurality of potential perspectives, influenced heavily by their prompts and training data. As part of LLM adoption, we seek to characterize - and ideally, manage - the socio-cultural values that they express, for reasons of safety, accuracy, inclusion, and cultural fidelity. We present a validated approach to automatically (1) extracting heterogeneous latent value propositions from texts, (2) assessing resonance and conflict of values with texts, and (3) combining these operations to characterize the pluralistic "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.05040","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2411.05040/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2411.05040","created_at":"2026-07-05T09:32:51.690521+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.05040v1","created_at":"2026-07-05T09:32:51.690521+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.05040","created_at":"2026-07-05T09:32:51.690521+00:00"},{"alias_kind":"pith_short_12","alias_value":"DN6CUOOMRXPZ","created_at":"2026-07-05T09:32:51.690521+00:00"},{"alias_kind":"pith_short_16","alias_value":"DN6CUOOMRXPZBDAP","created_at":"2026-07-05T09:32:51.690521+00:00"},{"alias_kind":"pith_short_8","alias_value":"DN6CUOOM","created_at":"2026-07-05T09:32:51.690521+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DN6CUOOMRXPZBDAPGAJHZ7NKIW","json":"https://pith.science/pith/DN6CUOOMRXPZBDAPGAJHZ7NKIW.json","graph_json":"https://pith.science/api/pith-number/DN6CUOOMRXPZBDAPGAJHZ7NKIW/graph.json","events_json":"https://pith.science/api/pith-number/DN6CUOOMRXPZBDAPGAJHZ7NKIW/events.json","paper":"https://pith.science/paper/DN6CUOOM"},"agent_actions":{"view_html":"https://pith.science/pith/DN6CUOOMRXPZBDAPGAJHZ7NKIW","download_json":"https://pith.science/pith/DN6CUOOMRXPZBDAPGAJHZ7NKIW.json","view_paper":"https://pith.science/paper/DN6CUOOM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.05040&json=true","fetch_graph":"https://pith.science/api/pith-number/DN6CUOOMRXPZBDAPGAJHZ7NKIW/graph.json","fetch_events":"https://pith.science/api/pith-number/DN6CUOOMRXPZBDAPGAJHZ7NKIW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DN6CUOOMRXPZBDAPGAJHZ7NKIW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DN6CUOOMRXPZBDAPGAJHZ7NKIW/action/storage_attestation","attest_author":"https://pith.science/pith/DN6CUOOMRXPZBDAPGAJHZ7NKIW/action/author_attestation","sign_citation":"https://pith.science/pith/DN6CUOOMRXPZBDAPGAJHZ7NKIW/action/citation_signature","submit_replication":"https://pith.science/pith/DN6CUOOMRXPZBDAPGAJHZ7NKIW/action/replication_record"}},"created_at":"2026-07-05T09:32:51.690521+00:00","updated_at":"2026-07-05T09:32:51.690521+00:00"}