{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:LCMZUO4PARAZQYTX6FC3MC4TLZ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"2aeace680063e68ff1354777e2214d91cee99f4b0e749a86d7c77bf176fe5603","cross_cats_sorted":["cs.AI","cs.CL","cs.CR","cs.SI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2023-09-25T20:23:51Z","title_canon_sha256":"64901b3353e2682e1445ace96b5752f873a343e9c1245f3d3f1d5236a1eb0840"},"schema_version":"1.0","source":{"id":"2309.14517","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.14517","created_at":"2026-07-05T07:34:35Z"},{"alias_kind":"arxiv_version","alias_value":"2309.14517v2","created_at":"2026-07-05T07:34:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.14517","created_at":"2026-07-05T07:34:35Z"},{"alias_kind":"pith_short_12","alias_value":"LCMZUO4PARAZ","created_at":"2026-07-05T07:34:35Z"},{"alias_kind":"pith_short_16","alias_value":"LCMZUO4PARAZQYTX","created_at":"2026-07-05T07:34:35Z"},{"alias_kind":"pith_short_8","alias_value":"LCMZUO4P","created_at":"2026-07-05T07:34:35Z"}],"graph_snapshots":[{"event_id":"sha256:0aac64c53da5460b29d34bc5661782cf3a88e2eb09da82eb15de6f5b6d8a9b6e","target":"graph","created_at":"2026-07-05T07:34:35Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2309.14517/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) have exploded in popularity due to their ability to perform a wide array of natural language tasks. Text-based content moderation is one LLM use case that has received recent enthusiasm, however, there is little research investigating how LLMs perform in content moderation settings. In this work, we evaluate a suite of commodity LLMs on two common content moderation tasks: rule-based community moderation and toxic content detection. For rule-based community moderation, we instantiate 95 subcommunity specific LLMs by prompting GPT-3.5 with rules from 95 Reddit subco","authors_text":"Deepak Kumar, Yousef AbuHashem, Zakir Durumeric","cross_cats":["cs.AI","cs.CL","cs.CR","cs.SI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2023-09-25T20:23:51Z","title":"Watch Your Language: Investigating Content Moderation with Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.14517","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f409f471a2de04c03632367b767ca257eccf94c831de72ac512fa2af8a432716","target":"record","created_at":"2026-07-05T07:34:35Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"2aeace680063e68ff1354777e2214d91cee99f4b0e749a86d7c77bf176fe5603","cross_cats_sorted":["cs.AI","cs.CL","cs.CR","cs.SI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2023-09-25T20:23:51Z","title_canon_sha256":"64901b3353e2682e1445ace96b5752f873a343e9c1245f3d3f1d5236a1eb0840"},"schema_version":"1.0","source":{"id":"2309.14517","kind":"arxiv","version":2}},"canonical_sha256":"58999a3b8f0441986277f145b60b935e60b5d1e48665a1bed5eec9a6aaa286b9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"58999a3b8f0441986277f145b60b935e60b5d1e48665a1bed5eec9a6aaa286b9","first_computed_at":"2026-07-05T07:34:35.550993Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:34:35.550993Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XzOR+2ypA8p1j+QGP2pYf9CEAXBQz7h4bf0GiWpdfyHceYtgPh/aoVAQb8Be21gWfarP/XJkaBQ3R4CgWGzbCg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:34:35.551500Z","signed_message":"canonical_sha256_bytes"},"source_id":"2309.14517","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f409f471a2de04c03632367b767ca257eccf94c831de72ac512fa2af8a432716","sha256:0aac64c53da5460b29d34bc5661782cf3a88e2eb09da82eb15de6f5b6d8a9b6e"],"state_sha256":"6d57fdf06c1388c673bf06c722e7e870ffce952e810fd022591951905ea614ff"}