{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:VPRNWF6BIATZREELFF5M5CAMMX","short_pith_number":"pith:VPRNWF6B","schema_version":"1.0","canonical_sha256":"abe2db17c1402798908b297ace880c65ec0d33c30275a5167f2b916380637992","source":{"kind":"arxiv","id":"2311.07424","version":1},"attestation_state":"computed","paper":{"title":"Hallucination Augmented Recitations for Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Abdullatif K\\\"oksal, Chung-Ching Chang, Renat Aksitov","submitted_at":"2023-11-13T15:58:18Z","abstract_excerpt":"Attribution is a key concept in large language models (LLMs) as it enables control over information sources and enhances the factuality of LLMs. While existing approaches utilize open book question answering to improve attribution, factual datasets may reward language models to recall facts that they already know from their pretraining data, not attribution. In contrast, counterfactual open book QA datasets would further improve attribution because the answer could only be grounded in the given text. We propose Hallucination Augmented Recitations (HAR) for creating counterfactual datasets by u"},"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":"2311.07424","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-11-13T15:58:18Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"dfa57fffc618fefbb27c7c3b5fa587233c2e49477a17bb62a0e4d93fbeda7fce","abstract_canon_sha256":"11a20c775e4a9e720fcab0a9cf0360b5040573e4e623fe42b0814b33060c7739"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:12:12.411198Z","signature_b64":"nGzy3qVrsbre2Zy14C3weckHOBxrFP2lNc090E4HuhemVoVpyWyClUt6dGk1T81rIhZkLdeTuWYuUpyAZcVkAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"abe2db17c1402798908b297ace880c65ec0d33c30275a5167f2b916380637992","last_reissued_at":"2026-07-05T07:12:12.410731Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:12:12.410731Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hallucination Augmented Recitations for Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Abdullatif K\\\"oksal, Chung-Ching Chang, Renat Aksitov","submitted_at":"2023-11-13T15:58:18Z","abstract_excerpt":"Attribution is a key concept in large language models (LLMs) as it enables control over information sources and enhances the factuality of LLMs. While existing approaches utilize open book question answering to improve attribution, factual datasets may reward language models to recall facts that they already know from their pretraining data, not attribution. In contrast, counterfactual open book QA datasets would further improve attribution because the answer could only be grounded in the given text. We propose Hallucination Augmented Recitations (HAR) for creating counterfactual datasets by u"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.07424","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/2311.07424/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":"2311.07424","created_at":"2026-07-05T07:12:12.410788+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.07424v1","created_at":"2026-07-05T07:12:12.410788+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.07424","created_at":"2026-07-05T07:12:12.410788+00:00"},{"alias_kind":"pith_short_12","alias_value":"VPRNWF6BIATZ","created_at":"2026-07-05T07:12:12.410788+00:00"},{"alias_kind":"pith_short_16","alias_value":"VPRNWF6BIATZREEL","created_at":"2026-07-05T07:12:12.410788+00:00"},{"alias_kind":"pith_short_8","alias_value":"VPRNWF6B","created_at":"2026-07-05T07:12:12.410788+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/VPRNWF6BIATZREELFF5M5CAMMX","json":"https://pith.science/pith/VPRNWF6BIATZREELFF5M5CAMMX.json","graph_json":"https://pith.science/api/pith-number/VPRNWF6BIATZREELFF5M5CAMMX/graph.json","events_json":"https://pith.science/api/pith-number/VPRNWF6BIATZREELFF5M5CAMMX/events.json","paper":"https://pith.science/paper/VPRNWF6B"},"agent_actions":{"view_html":"https://pith.science/pith/VPRNWF6BIATZREELFF5M5CAMMX","download_json":"https://pith.science/pith/VPRNWF6BIATZREELFF5M5CAMMX.json","view_paper":"https://pith.science/paper/VPRNWF6B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.07424&json=true","fetch_graph":"https://pith.science/api/pith-number/VPRNWF6BIATZREELFF5M5CAMMX/graph.json","fetch_events":"https://pith.science/api/pith-number/VPRNWF6BIATZREELFF5M5CAMMX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VPRNWF6BIATZREELFF5M5CAMMX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VPRNWF6BIATZREELFF5M5CAMMX/action/storage_attestation","attest_author":"https://pith.science/pith/VPRNWF6BIATZREELFF5M5CAMMX/action/author_attestation","sign_citation":"https://pith.science/pith/VPRNWF6BIATZREELFF5M5CAMMX/action/citation_signature","submit_replication":"https://pith.science/pith/VPRNWF6BIATZREELFF5M5CAMMX/action/replication_record"}},"created_at":"2026-07-05T07:12:12.410788+00:00","updated_at":"2026-07-05T07:12:12.410788+00:00"}