{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:ZLYM6RHYBPZFF3VADU67RQLWIK","short_pith_number":"pith:ZLYM6RHY","schema_version":"1.0","canonical_sha256":"caf0cf44f80bf252eea01d3df8c17642a70a44757c23f56d6a660e7efc2b61c9","source":{"kind":"arxiv","id":"2210.04185","version":4},"attestation_state":"computed","paper":{"title":"Controllable Dialogue Simulation with In-Context Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Hong Wang, Jing Qian, Shiyang Li, Wenhu Chen, Xifeng Yan, Zekun Li","submitted_at":"2022-10-09T06:32:58Z","abstract_excerpt":"Building dialogue systems requires a large corpus of annotated dialogues. Such datasets are usually created via crowdsourcing, which is expensive and time-consuming. In this paper, we propose \\textsc{Dialogic}, a novel dialogue simulation method based on large language model in-context learning to automate dataset creation. Seeded with a few annotated dialogues, \\textsc{Dialogic} automatically selects in-context examples for demonstration and prompts GPT-3 to generate new dialogues and annotations in a controllable way. Our method can rapidly expand a small set of dialogue data with minimum or"},"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":"2210.04185","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-10-09T06:32:58Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"639a24eaec8add8de6d6ef6f30cde9f8fe3e9bb58994ffbc12e1f7a8c6805b0e","abstract_canon_sha256":"a1fe1dcfc57a30cb7c02236fdef9c96f7a3ce6ec7014b12ceb4bc4ab554024e1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:17:40.453471Z","signature_b64":"NYYY6INwop1n9+0LJ/SNjn3RVMVkAHRtd+Njh0bo2/F6RaooZhkf2NsojYgzDJ8cEaq9Mmsp9w/h7GGj2HUJAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"caf0cf44f80bf252eea01d3df8c17642a70a44757c23f56d6a660e7efc2b61c9","last_reissued_at":"2026-07-05T06:17:40.452970Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:17:40.452970Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Controllable Dialogue Simulation with In-Context Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Hong Wang, Jing Qian, Shiyang Li, Wenhu Chen, Xifeng Yan, Zekun Li","submitted_at":"2022-10-09T06:32:58Z","abstract_excerpt":"Building dialogue systems requires a large corpus of annotated dialogues. Such datasets are usually created via crowdsourcing, which is expensive and time-consuming. In this paper, we propose \\textsc{Dialogic}, a novel dialogue simulation method based on large language model in-context learning to automate dataset creation. Seeded with a few annotated dialogues, \\textsc{Dialogic} automatically selects in-context examples for demonstration and prompts GPT-3 to generate new dialogues and annotations in a controllable way. Our method can rapidly expand a small set of dialogue data with minimum or"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.04185","kind":"arxiv","version":4},"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/2210.04185/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":"2210.04185","created_at":"2026-07-05T06:17:40.453020+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.04185v4","created_at":"2026-07-05T06:17:40.453020+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.04185","created_at":"2026-07-05T06:17:40.453020+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZLYM6RHYBPZF","created_at":"2026-07-05T06:17:40.453020+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZLYM6RHYBPZFF3VA","created_at":"2026-07-05T06:17:40.453020+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZLYM6RHY","created_at":"2026-07-05T06:17:40.453020+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.09255","citing_title":"RPO-PDT: Demonstrating Role-Play-Based Knowledge Adaptation for Student Support Dialogue (Demonstration System)","ref_index":38,"is_internal_anchor":false},{"citing_arxiv_id":"2303.17760","citing_title":"CAMEL: Communicative Agents for \"Mind\" Exploration of Large Language Model Society","ref_index":67,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZLYM6RHYBPZFF3VADU67RQLWIK","json":"https://pith.science/pith/ZLYM6RHYBPZFF3VADU67RQLWIK.json","graph_json":"https://pith.science/api/pith-number/ZLYM6RHYBPZFF3VADU67RQLWIK/graph.json","events_json":"https://pith.science/api/pith-number/ZLYM6RHYBPZFF3VADU67RQLWIK/events.json","paper":"https://pith.science/paper/ZLYM6RHY"},"agent_actions":{"view_html":"https://pith.science/pith/ZLYM6RHYBPZFF3VADU67RQLWIK","download_json":"https://pith.science/pith/ZLYM6RHYBPZFF3VADU67RQLWIK.json","view_paper":"https://pith.science/paper/ZLYM6RHY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.04185&json=true","fetch_graph":"https://pith.science/api/pith-number/ZLYM6RHYBPZFF3VADU67RQLWIK/graph.json","fetch_events":"https://pith.science/api/pith-number/ZLYM6RHYBPZFF3VADU67RQLWIK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZLYM6RHYBPZFF3VADU67RQLWIK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZLYM6RHYBPZFF3VADU67RQLWIK/action/storage_attestation","attest_author":"https://pith.science/pith/ZLYM6RHYBPZFF3VADU67RQLWIK/action/author_attestation","sign_citation":"https://pith.science/pith/ZLYM6RHYBPZFF3VADU67RQLWIK/action/citation_signature","submit_replication":"https://pith.science/pith/ZLYM6RHYBPZFF3VADU67RQLWIK/action/replication_record"}},"created_at":"2026-07-05T06:17:40.453020+00:00","updated_at":"2026-07-05T06:17:40.453020+00:00"}