{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:AEBARS7LLI5ENXGII5IKFNNF5A","short_pith_number":"pith:AEBARS7L","canonical_record":{"source":{"id":"2307.01969","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-07-05T00:40:40Z","cross_cats_sorted":[],"title_canon_sha256":"8562130f873a4637e0a7245ae434630e660e06b493a674d10301e50d42629562","abstract_canon_sha256":"c7563a147a0b045550401d4bbb3c7ff46174e8d934d7a7900274ba96535f9a54"},"schema_version":"1.0"},"canonical_sha256":"010208cbeb5a3a46dcc84750a2b5a5e838b73eadfc9725a94d8a9a30fd8c2e90","source":{"kind":"arxiv","id":"2307.01969","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.01969","created_at":"2026-07-05T06:28:03Z"},{"alias_kind":"arxiv_version","alias_value":"2307.01969v1","created_at":"2026-07-05T06:28:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.01969","created_at":"2026-07-05T06:28:03Z"},{"alias_kind":"pith_short_12","alias_value":"AEBARS7LLI5E","created_at":"2026-07-05T06:28:03Z"},{"alias_kind":"pith_short_16","alias_value":"AEBARS7LLI5ENXGI","created_at":"2026-07-05T06:28:03Z"},{"alias_kind":"pith_short_8","alias_value":"AEBARS7L","created_at":"2026-07-05T06:28:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:AEBARS7LLI5ENXGII5IKFNNF5A","target":"record","payload":{"canonical_record":{"source":{"id":"2307.01969","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-07-05T00:40:40Z","cross_cats_sorted":[],"title_canon_sha256":"8562130f873a4637e0a7245ae434630e660e06b493a674d10301e50d42629562","abstract_canon_sha256":"c7563a147a0b045550401d4bbb3c7ff46174e8d934d7a7900274ba96535f9a54"},"schema_version":"1.0"},"canonical_sha256":"010208cbeb5a3a46dcc84750a2b5a5e838b73eadfc9725a94d8a9a30fd8c2e90","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:28:03.351621Z","signature_b64":"Pi+K10Mun6R05/pzs7M52/VuifwWxoyMcqmShlEXKMSt9xZfpJHhxfwBDUWWzWsAIwzmg9txpnfqetsSp9KtBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"010208cbeb5a3a46dcc84750a2b5a5e838b73eadfc9725a94d8a9a30fd8c2e90","last_reissued_at":"2026-07-05T06:28:03.351192Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:28:03.351192Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2307.01969","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:28:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1U2CE5pdo6N2MI627UQu54fUQAEdKkiKvqikZhpNzBZRV/7WvxP96hum4+ISpOGGaBCKvYIXA8AiPtwBsZJ4Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-19T09:45:09.380771Z"},"content_sha256":"779bdc8b0de38beff9eb2bc7dd9c69a52cdc9fbe6cf305c7b5c5681e0aa2e6e5","schema_version":"1.0","event_id":"sha256:779bdc8b0de38beff9eb2bc7dd9c69a52cdc9fbe6cf305c7b5c5681e0aa2e6e5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:AEBARS7LLI5ENXGII5IKFNNF5A","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multimodal Prompt Learning for Product Title Generation with Extremely Limited Labels","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bang Yang, Bing Yin, Chenyu You, Fenglin Liu, Qingyu Yin, Yuexian Zou, Zheng Li","submitted_at":"2023-07-05T00:40:40Z","abstract_excerpt":"Generating an informative and attractive title for the product is a crucial task for e-commerce. Most existing works follow the standard multimodal natural language generation approaches, e.g., image captioning, and employ the large scale of human-labelled datasets to train desirable models. However, for novel products, especially in a different domain, there are few existing labelled data. In this paper, we propose a prompt-based approach, i.e., the Multimodal Prompt Learning framework, to accurately and efficiently generate titles for novel products with limited labels. We observe that the c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.01969","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/2307.01969/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:28:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d06T/sFUxBlo6TxIJW4JMe06tD9+uDs6Ex6gexlPMvrIYgxgktM2ehIRmoPjToJeZwYbgzTb4BRDUCLv2FJwCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-19T09:45:09.381150Z"},"content_sha256":"01ac549318f58dbe6299d16ea0e8b485ed9d8f182b9d67a8425a0d47f830e4be","schema_version":"1.0","event_id":"sha256:01ac549318f58dbe6299d16ea0e8b485ed9d8f182b9d67a8425a0d47f830e4be"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AEBARS7LLI5ENXGII5IKFNNF5A/bundle.json","state_url":"https://pith.science/pith/AEBARS7LLI5ENXGII5IKFNNF5A/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AEBARS7LLI5ENXGII5IKFNNF5A/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-19T09:45:09Z","links":{"resolver":"https://pith.science/pith/AEBARS7LLI5ENXGII5IKFNNF5A","bundle":"https://pith.science/pith/AEBARS7LLI5ENXGII5IKFNNF5A/bundle.json","state":"https://pith.science/pith/AEBARS7LLI5ENXGII5IKFNNF5A/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AEBARS7LLI5ENXGII5IKFNNF5A/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:AEBARS7LLI5ENXGII5IKFNNF5A","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":"c7563a147a0b045550401d4bbb3c7ff46174e8d934d7a7900274ba96535f9a54","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-07-05T00:40:40Z","title_canon_sha256":"8562130f873a4637e0a7245ae434630e660e06b493a674d10301e50d42629562"},"schema_version":"1.0","source":{"id":"2307.01969","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.01969","created_at":"2026-07-05T06:28:03Z"},{"alias_kind":"arxiv_version","alias_value":"2307.01969v1","created_at":"2026-07-05T06:28:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.01969","created_at":"2026-07-05T06:28:03Z"},{"alias_kind":"pith_short_12","alias_value":"AEBARS7LLI5E","created_at":"2026-07-05T06:28:03Z"},{"alias_kind":"pith_short_16","alias_value":"AEBARS7LLI5ENXGI","created_at":"2026-07-05T06:28:03Z"},{"alias_kind":"pith_short_8","alias_value":"AEBARS7L","created_at":"2026-07-05T06:28:03Z"}],"graph_snapshots":[{"event_id":"sha256:01ac549318f58dbe6299d16ea0e8b485ed9d8f182b9d67a8425a0d47f830e4be","target":"graph","created_at":"2026-07-05T06:28:03Z","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/2307.01969/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Generating an informative and attractive title for the product is a crucial task for e-commerce. Most existing works follow the standard multimodal natural language generation approaches, e.g., image captioning, and employ the large scale of human-labelled datasets to train desirable models. However, for novel products, especially in a different domain, there are few existing labelled data. In this paper, we propose a prompt-based approach, i.e., the Multimodal Prompt Learning framework, to accurately and efficiently generate titles for novel products with limited labels. We observe that the c","authors_text":"Bang Yang, Bing Yin, Chenyu You, Fenglin Liu, Qingyu Yin, Yuexian Zou, Zheng Li","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-07-05T00:40:40Z","title":"Multimodal Prompt Learning for Product Title Generation with Extremely Limited Labels"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.01969","kind":"arxiv","version":1},"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":"sha256:779bdc8b0de38beff9eb2bc7dd9c69a52cdc9fbe6cf305c7b5c5681e0aa2e6e5","target":"record","created_at":"2026-07-05T06:28:03Z","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":"c7563a147a0b045550401d4bbb3c7ff46174e8d934d7a7900274ba96535f9a54","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-07-05T00:40:40Z","title_canon_sha256":"8562130f873a4637e0a7245ae434630e660e06b493a674d10301e50d42629562"},"schema_version":"1.0","source":{"id":"2307.01969","kind":"arxiv","version":1}},"canonical_sha256":"010208cbeb5a3a46dcc84750a2b5a5e838b73eadfc9725a94d8a9a30fd8c2e90","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"010208cbeb5a3a46dcc84750a2b5a5e838b73eadfc9725a94d8a9a30fd8c2e90","first_computed_at":"2026-07-05T06:28:03.351192Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:28:03.351192Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Pi+K10Mun6R05/pzs7M52/VuifwWxoyMcqmShlEXKMSt9xZfpJHhxfwBDUWWzWsAIwzmg9txpnfqetsSp9KtBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:28:03.351621Z","signed_message":"canonical_sha256_bytes"},"source_id":"2307.01969","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:779bdc8b0de38beff9eb2bc7dd9c69a52cdc9fbe6cf305c7b5c5681e0aa2e6e5","sha256:01ac549318f58dbe6299d16ea0e8b485ed9d8f182b9d67a8425a0d47f830e4be"],"state_sha256":"2136b04ace4b25870d3821d62f7f91a43422307914afc5c486314928f47c06c0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nij4hWxuE11jNJUNxK3c5ubFQKvX0VdFbZBg68LH/pS5VqyjieV2u8jSufPmkQTmw2zhqoErcqwuR70HXg3ZBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-19T09:45:09.383839Z","bundle_sha256":"e8442e18196115ee792a0049221882f10ab9fbe2efce0498cb2bd1425bf46e2e"}}