{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:REB3XW2WC5PQN44I4MA3WFYJD6","short_pith_number":"pith:REB3XW2W","schema_version":"1.0","canonical_sha256":"8903bbdb56175f06f388e301bb17091f93124d3abd0165bdc371bd3de727494f","source":{"kind":"arxiv","id":"2304.00612","version":1},"attestation_state":"computed","paper":{"title":"Eight Things to Know about Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Samuel R. Bowman","submitted_at":"2023-04-02T20:03:27Z","abstract_excerpt":"The widespread public deployment of large language models (LLMs) in recent months has prompted a wave of new attention and engagement from advocates, policymakers, and scholars from many fields. This attention is a timely response to the many urgent questions that this technology raises, but it can sometimes miss important considerations. This paper surveys the evidence for eight potentially surprising such points:\n  1. LLMs predictably get more capable with increasing investment, even without targeted innovation.\n  2. Many important LLM behaviors emerge unpredictably as a byproduct of increas"},"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":"2304.00612","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-04-02T20:03:27Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"75e71882eaae4f495b5377a7008464e6a087759e69adba96e0ddbfd64d18b322","abstract_canon_sha256":"68f10f102e5af9702bbc068e18fff4bc510e18745df56a073e115370936acdd2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:57:19.855271Z","signature_b64":"gH4S1Yw1nSmA/b8/b7hXx4ZrDHgytYE7J6GXnGT7iy02OwkM30Re1WrN2Oh/n/YYPTgfVB1/Z747QWEDXj37Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8903bbdb56175f06f388e301bb17091f93124d3abd0165bdc371bd3de727494f","last_reissued_at":"2026-07-05T05:57:19.854759Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:57:19.854759Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Eight Things to Know about Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Samuel R. Bowman","submitted_at":"2023-04-02T20:03:27Z","abstract_excerpt":"The widespread public deployment of large language models (LLMs) in recent months has prompted a wave of new attention and engagement from advocates, policymakers, and scholars from many fields. This attention is a timely response to the many urgent questions that this technology raises, but it can sometimes miss important considerations. This paper surveys the evidence for eight potentially surprising such points:\n  1. LLMs predictably get more capable with increasing investment, even without targeted innovation.\n  2. Many important LLM behaviors emerge unpredictably as a byproduct of increas"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.00612","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/2304.00612/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":"2304.00612","created_at":"2026-07-05T05:57:19.854823+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.00612v1","created_at":"2026-07-05T05:57:19.854823+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.00612","created_at":"2026-07-05T05:57:19.854823+00:00"},{"alias_kind":"pith_short_12","alias_value":"REB3XW2WC5PQ","created_at":"2026-07-05T05:57:19.854823+00:00"},{"alias_kind":"pith_short_16","alias_value":"REB3XW2WC5PQN44I","created_at":"2026-07-05T05:57:19.854823+00:00"},{"alias_kind":"pith_short_8","alias_value":"REB3XW2W","created_at":"2026-07-05T05:57:19.854823+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2308.05374","citing_title":"Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2305.17926","citing_title":"Large Language Models are not Fair Evaluators","ref_index":58,"is_internal_anchor":false},{"citing_arxiv_id":"2604.03356","citing_title":"Evaluating Artificial Intelligence Through a Christian Understanding of Human Flourishing","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/REB3XW2WC5PQN44I4MA3WFYJD6","json":"https://pith.science/pith/REB3XW2WC5PQN44I4MA3WFYJD6.json","graph_json":"https://pith.science/api/pith-number/REB3XW2WC5PQN44I4MA3WFYJD6/graph.json","events_json":"https://pith.science/api/pith-number/REB3XW2WC5PQN44I4MA3WFYJD6/events.json","paper":"https://pith.science/paper/REB3XW2W"},"agent_actions":{"view_html":"https://pith.science/pith/REB3XW2WC5PQN44I4MA3WFYJD6","download_json":"https://pith.science/pith/REB3XW2WC5PQN44I4MA3WFYJD6.json","view_paper":"https://pith.science/paper/REB3XW2W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.00612&json=true","fetch_graph":"https://pith.science/api/pith-number/REB3XW2WC5PQN44I4MA3WFYJD6/graph.json","fetch_events":"https://pith.science/api/pith-number/REB3XW2WC5PQN44I4MA3WFYJD6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/REB3XW2WC5PQN44I4MA3WFYJD6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/REB3XW2WC5PQN44I4MA3WFYJD6/action/storage_attestation","attest_author":"https://pith.science/pith/REB3XW2WC5PQN44I4MA3WFYJD6/action/author_attestation","sign_citation":"https://pith.science/pith/REB3XW2WC5PQN44I4MA3WFYJD6/action/citation_signature","submit_replication":"https://pith.science/pith/REB3XW2WC5PQN44I4MA3WFYJD6/action/replication_record"}},"created_at":"2026-07-05T05:57:19.854823+00:00","updated_at":"2026-07-05T05:57:19.854823+00:00"}