{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:2RVJJQE4M3WTOPDJHWO4ZMQFOT","short_pith_number":"pith:2RVJJQE4","schema_version":"1.0","canonical_sha256":"d46a94c09c66ed373c693d9dccb20574cda220db0ca494d53607c622f7f4c405","source":{"kind":"arxiv","id":"2308.15399","version":2},"attestation_state":"computed","paper":{"title":"Rethinking Machine Ethics -- Can LLMs Perform Moral Reasoning through the Lens of Moral Theories?","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Helen Meng, Irwin King, Jingyan Zhou, Junan Li, Minda Hu, Xiaoying Zhang, Xixin Wu","submitted_at":"2023-08-29T15:57:32Z","abstract_excerpt":"Making moral judgments is an essential step toward developing ethical AI systems. Prevalent approaches are mostly implemented in a bottom-up manner, which uses a large set of annotated data to train models based on crowd-sourced opinions about morality. These approaches have been criticized for overgeneralizing the moral stances of a limited group of annotators and lacking explainability. This work proposes a flexible top-down framework to steer (Large) Language Models (LMs) to perform moral reasoning with well-established moral theories from interdisciplinary research. The theory-guided top-d"},"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":"2308.15399","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2023-08-29T15:57:32Z","cross_cats_sorted":[],"title_canon_sha256":"8301820ee65d5e0b3f3e2bb6ffcdf156ccda758382467d3fb1b81718b1a12862","abstract_canon_sha256":"ac30690c03580370cf8e20a13a5d9c93b9a5413cc45f4c837cad8aebb12c8fb1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:38:34.179864Z","signature_b64":"ke/5whIrNb2X5E6pMnDYlnFM+c0LBBbCDYPWeDflyqgaTivCgRPAIbxY/mcd2CHic3gnZcXcrp+QrPVQuT7eAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d46a94c09c66ed373c693d9dccb20574cda220db0ca494d53607c622f7f4c405","last_reissued_at":"2026-07-05T08:38:34.179405Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:38:34.179405Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Rethinking Machine Ethics -- Can LLMs Perform Moral Reasoning through the Lens of Moral Theories?","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Helen Meng, Irwin King, Jingyan Zhou, Junan Li, Minda Hu, Xiaoying Zhang, Xixin Wu","submitted_at":"2023-08-29T15:57:32Z","abstract_excerpt":"Making moral judgments is an essential step toward developing ethical AI systems. Prevalent approaches are mostly implemented in a bottom-up manner, which uses a large set of annotated data to train models based on crowd-sourced opinions about morality. These approaches have been criticized for overgeneralizing the moral stances of a limited group of annotators and lacking explainability. This work proposes a flexible top-down framework to steer (Large) Language Models (LMs) to perform moral reasoning with well-established moral theories from interdisciplinary research. The theory-guided top-d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.15399","kind":"arxiv","version":2},"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/2308.15399/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":"2308.15399","created_at":"2026-07-05T08:38:34.179459+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.15399v2","created_at":"2026-07-05T08:38:34.179459+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.15399","created_at":"2026-07-05T08:38:34.179459+00:00"},{"alias_kind":"pith_short_12","alias_value":"2RVJJQE4M3WT","created_at":"2026-07-05T08:38:34.179459+00:00"},{"alias_kind":"pith_short_16","alias_value":"2RVJJQE4M3WTOPDJ","created_at":"2026-07-05T08:38:34.179459+00:00"},{"alias_kind":"pith_short_8","alias_value":"2RVJJQE4","created_at":"2026-07-05T08:38:34.179459+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2401.05561","citing_title":"TrustLLM: Trustworthiness in Large Language Models","ref_index":293,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2RVJJQE4M3WTOPDJHWO4ZMQFOT","json":"https://pith.science/pith/2RVJJQE4M3WTOPDJHWO4ZMQFOT.json","graph_json":"https://pith.science/api/pith-number/2RVJJQE4M3WTOPDJHWO4ZMQFOT/graph.json","events_json":"https://pith.science/api/pith-number/2RVJJQE4M3WTOPDJHWO4ZMQFOT/events.json","paper":"https://pith.science/paper/2RVJJQE4"},"agent_actions":{"view_html":"https://pith.science/pith/2RVJJQE4M3WTOPDJHWO4ZMQFOT","download_json":"https://pith.science/pith/2RVJJQE4M3WTOPDJHWO4ZMQFOT.json","view_paper":"https://pith.science/paper/2RVJJQE4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.15399&json=true","fetch_graph":"https://pith.science/api/pith-number/2RVJJQE4M3WTOPDJHWO4ZMQFOT/graph.json","fetch_events":"https://pith.science/api/pith-number/2RVJJQE4M3WTOPDJHWO4ZMQFOT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2RVJJQE4M3WTOPDJHWO4ZMQFOT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2RVJJQE4M3WTOPDJHWO4ZMQFOT/action/storage_attestation","attest_author":"https://pith.science/pith/2RVJJQE4M3WTOPDJHWO4ZMQFOT/action/author_attestation","sign_citation":"https://pith.science/pith/2RVJJQE4M3WTOPDJHWO4ZMQFOT/action/citation_signature","submit_replication":"https://pith.science/pith/2RVJJQE4M3WTOPDJHWO4ZMQFOT/action/replication_record"}},"created_at":"2026-07-05T08:38:34.179459+00:00","updated_at":"2026-07-05T08:38:34.179459+00:00"}