{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:VTIDPPGH5W7F7DZXFJ63JX4QQC","short_pith_number":"pith:VTIDPPGH","schema_version":"1.0","canonical_sha256":"acd037bcc7edbe5f8f372a7db4df908088d890a93e45c67fc81478a897b90008","source":{"kind":"arxiv","id":"2311.00953","version":1},"attestation_state":"computed","paper":{"title":"Blending Reward Functions via Few Expert Demonstrations for Faithful and Accurate Knowledge-Grounded Dialogue Generation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Wanyu Du, Yangfeng Ji","submitted_at":"2023-11-02T02:42:41Z","abstract_excerpt":"The development of trustworthy conversational information-seeking systems relies on dialogue models that can generate faithful and accurate responses based on relevant knowledge texts. However, two main challenges hinder this task. Firstly, language models may generate hallucinations due to data biases present in their pretraining corpus. Secondly, knowledge texts often contain redundant and irrelevant information that distracts the model's attention from the relevant text span. Previous works use additional data annotations on the knowledge texts to learn a knowledge identification module in "},"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":"2311.00953","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-02T02:42:41Z","cross_cats_sorted":[],"title_canon_sha256":"2289cf407c10c2489e2117ae534e3679015a536097dc3c42ba6437798818ebf6","abstract_canon_sha256":"5ce940a699ac20e13a7d2c710514d3ec55f244f5ad9fb732c5f0326a9f91171f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:08:18.210616Z","signature_b64":"cWekltTXzje4ixIVp+Vor2I8vQ/+Ir3+Q2R/MsG0USZKpR+Eu/Ynuo0pYfDVf1nwpzNbhjEBfoyQWkfi+FxxDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"acd037bcc7edbe5f8f372a7db4df908088d890a93e45c67fc81478a897b90008","last_reissued_at":"2026-07-05T07:08:18.210119Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:08:18.210119Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Blending Reward Functions via Few Expert Demonstrations for Faithful and Accurate Knowledge-Grounded Dialogue Generation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Wanyu Du, Yangfeng Ji","submitted_at":"2023-11-02T02:42:41Z","abstract_excerpt":"The development of trustworthy conversational information-seeking systems relies on dialogue models that can generate faithful and accurate responses based on relevant knowledge texts. However, two main challenges hinder this task. Firstly, language models may generate hallucinations due to data biases present in their pretraining corpus. Secondly, knowledge texts often contain redundant and irrelevant information that distracts the model's attention from the relevant text span. Previous works use additional data annotations on the knowledge texts to learn a knowledge identification module in "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.00953","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/2311.00953/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":"2311.00953","created_at":"2026-07-05T07:08:18.210179+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.00953v1","created_at":"2026-07-05T07:08:18.210179+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.00953","created_at":"2026-07-05T07:08:18.210179+00:00"},{"alias_kind":"pith_short_12","alias_value":"VTIDPPGH5W7F","created_at":"2026-07-05T07:08:18.210179+00:00"},{"alias_kind":"pith_short_16","alias_value":"VTIDPPGH5W7F7DZX","created_at":"2026-07-05T07:08:18.210179+00:00"},{"alias_kind":"pith_short_8","alias_value":"VTIDPPGH","created_at":"2026-07-05T07:08:18.210179+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/VTIDPPGH5W7F7DZXFJ63JX4QQC","json":"https://pith.science/pith/VTIDPPGH5W7F7DZXFJ63JX4QQC.json","graph_json":"https://pith.science/api/pith-number/VTIDPPGH5W7F7DZXFJ63JX4QQC/graph.json","events_json":"https://pith.science/api/pith-number/VTIDPPGH5W7F7DZXFJ63JX4QQC/events.json","paper":"https://pith.science/paper/VTIDPPGH"},"agent_actions":{"view_html":"https://pith.science/pith/VTIDPPGH5W7F7DZXFJ63JX4QQC","download_json":"https://pith.science/pith/VTIDPPGH5W7F7DZXFJ63JX4QQC.json","view_paper":"https://pith.science/paper/VTIDPPGH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.00953&json=true","fetch_graph":"https://pith.science/api/pith-number/VTIDPPGH5W7F7DZXFJ63JX4QQC/graph.json","fetch_events":"https://pith.science/api/pith-number/VTIDPPGH5W7F7DZXFJ63JX4QQC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VTIDPPGH5W7F7DZXFJ63JX4QQC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VTIDPPGH5W7F7DZXFJ63JX4QQC/action/storage_attestation","attest_author":"https://pith.science/pith/VTIDPPGH5W7F7DZXFJ63JX4QQC/action/author_attestation","sign_citation":"https://pith.science/pith/VTIDPPGH5W7F7DZXFJ63JX4QQC/action/citation_signature","submit_replication":"https://pith.science/pith/VTIDPPGH5W7F7DZXFJ63JX4QQC/action/replication_record"}},"created_at":"2026-07-05T07:08:18.210179+00:00","updated_at":"2026-07-05T07:08:18.210179+00:00"}