{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:MRFQ3LGBECOG52JSLC3MKZDMEY","short_pith_number":"pith:MRFQ3LGB","schema_version":"1.0","canonical_sha256":"644b0dacc1209c6ee93258b6c5646c262b6797ca76100c1dff3653430d6daa21","source":{"kind":"arxiv","id":"2306.06085","version":1},"attestation_state":"computed","paper":{"title":"Trapping LLM Hallucinations Using Tagged Context Prompts","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"James R. Foulds, Philip Feldman, Shimei Pan","submitted_at":"2023-06-09T17:48:54Z","abstract_excerpt":"Recent advances in large language models (LLMs), such as ChatGPT, have led to highly sophisticated conversation agents. However, these models suffer from \"hallucinations,\" where the model generates false or fabricated information. Addressing this challenge is crucial, particularly with AI-driven platforms being adopted across various sectors. In this paper, we propose a novel method to recognize and flag instances when LLMs perform outside their domain knowledge, and ensuring users receive accurate information.\n  We find that the use of context combined with embedded tags can successfully comb"},"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":"2306.06085","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-06-09T17:48:54Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"71b42f9ec48bdb451e7f01cb01e8aad90eef0207d568375a499f55687717f8ab","abstract_canon_sha256":"27eb5623703f18242b8ea785fd8f7a2c18a199ac7364f8f98f5bcc882efc1ca9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:19:13.591097Z","signature_b64":"dX9k+d3zOqFUEvlKEtfrkVD6zezANZlEPpL7NlkniwI8sSqkfd6DpUPcOZz18NVE18tmY2BuzePm332U+HqdAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"644b0dacc1209c6ee93258b6c5646c262b6797ca76100c1dff3653430d6daa21","last_reissued_at":"2026-07-05T06:19:13.590768Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:19:13.590768Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Trapping LLM Hallucinations Using Tagged Context Prompts","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"James R. Foulds, Philip Feldman, Shimei Pan","submitted_at":"2023-06-09T17:48:54Z","abstract_excerpt":"Recent advances in large language models (LLMs), such as ChatGPT, have led to highly sophisticated conversation agents. However, these models suffer from \"hallucinations,\" where the model generates false or fabricated information. Addressing this challenge is crucial, particularly with AI-driven platforms being adopted across various sectors. In this paper, we propose a novel method to recognize and flag instances when LLMs perform outside their domain knowledge, and ensuring users receive accurate information.\n  We find that the use of context combined with embedded tags can successfully comb"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.06085","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/2306.06085/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":"2306.06085","created_at":"2026-07-05T06:19:13.590822+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.06085v1","created_at":"2026-07-05T06:19:13.590822+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.06085","created_at":"2026-07-05T06:19:13.590822+00:00"},{"alias_kind":"pith_short_12","alias_value":"MRFQ3LGBECOG","created_at":"2026-07-05T06:19:13.590822+00:00"},{"alias_kind":"pith_short_16","alias_value":"MRFQ3LGBECOG52JS","created_at":"2026-07-05T06:19:13.590822+00:00"},{"alias_kind":"pith_short_8","alias_value":"MRFQ3LGB","created_at":"2026-07-05T06:19:13.590822+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.01301","citing_title":"Med-HEAL: Analyzing and Mitigating Hallucinations in Medical LLMs with Hallucination-Aware In-Context Learning","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2410.02091","citing_title":"The Impact of Generative AI on Collaborative Open-Source Software Development: Evidence from GitHub Copilot","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2505.09246","citing_title":"Autofocus Retrieval: An Effective Pipeline for Multi-Hop Question Answering With Semi-Structured Knowledge","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2406.15927","citing_title":"Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs","ref_index":22,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MRFQ3LGBECOG52JSLC3MKZDMEY","json":"https://pith.science/pith/MRFQ3LGBECOG52JSLC3MKZDMEY.json","graph_json":"https://pith.science/api/pith-number/MRFQ3LGBECOG52JSLC3MKZDMEY/graph.json","events_json":"https://pith.science/api/pith-number/MRFQ3LGBECOG52JSLC3MKZDMEY/events.json","paper":"https://pith.science/paper/MRFQ3LGB"},"agent_actions":{"view_html":"https://pith.science/pith/MRFQ3LGBECOG52JSLC3MKZDMEY","download_json":"https://pith.science/pith/MRFQ3LGBECOG52JSLC3MKZDMEY.json","view_paper":"https://pith.science/paper/MRFQ3LGB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.06085&json=true","fetch_graph":"https://pith.science/api/pith-number/MRFQ3LGBECOG52JSLC3MKZDMEY/graph.json","fetch_events":"https://pith.science/api/pith-number/MRFQ3LGBECOG52JSLC3MKZDMEY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MRFQ3LGBECOG52JSLC3MKZDMEY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MRFQ3LGBECOG52JSLC3MKZDMEY/action/storage_attestation","attest_author":"https://pith.science/pith/MRFQ3LGBECOG52JSLC3MKZDMEY/action/author_attestation","sign_citation":"https://pith.science/pith/MRFQ3LGBECOG52JSLC3MKZDMEY/action/citation_signature","submit_replication":"https://pith.science/pith/MRFQ3LGBECOG52JSLC3MKZDMEY/action/replication_record"}},"created_at":"2026-07-05T06:19:13.590822+00:00","updated_at":"2026-07-05T06:19:13.590822+00:00"}