{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:PSKIHMDPUALOA6IADNDG7XBDXC","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":"02f93cae7d328d0ca3ceafe7c336c4ea94e7d99d103c53988076c5aab3f61ad8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-19T07:39:00Z","title_canon_sha256":"8a38e85e75413e89c3089b86bfee75df5641606458605af322859be70e442631"},"schema_version":"1.0","source":{"id":"2310.12537","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.12537","created_at":"2026-07-05T09:09:17Z"},{"alias_kind":"arxiv_version","alias_value":"2310.12537v5","created_at":"2026-07-05T09:09:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.12537","created_at":"2026-07-05T09:09:17Z"},{"alias_kind":"pith_short_12","alias_value":"PSKIHMDPUALO","created_at":"2026-07-05T09:09:17Z"},{"alias_kind":"pith_short_16","alias_value":"PSKIHMDPUALOA6IA","created_at":"2026-07-05T09:09:17Z"},{"alias_kind":"pith_short_8","alias_value":"PSKIHMDP","created_at":"2026-07-05T09:09:17Z"}],"graph_snapshots":[{"event_id":"sha256:9ed1a3e21be070f911032e1793ed422ecc43867751f2ef744986e87a3d587b40","target":"graph","created_at":"2026-07-05T09:09:17Z","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/2310.12537/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"E-commerce platforms require structured product data in the form of attribute-value pairs to offer features such as faceted product search or attribute-based product comparison. However, vendors often provide unstructured product descriptions, necessitating the extraction of attribute-value pairs from these texts. BERT-based extraction methods require large amounts of task-specific training data and struggle with unseen attribute values. This paper explores using large language models (LLMs) as a more training-data efficient and robust alternative. We propose prompt templates for zero-shot and","authors_text":"Alexander Brinkmann, Christian Bizer, Roee Shraga","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-19T07:39:00Z","title":"ExtractGPT: Exploring the Potential of Large Language Models for Product Attribute Value Extraction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.12537","kind":"arxiv","version":5},"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":"sha256:22f062c3a1d60f4beea65fc59cf7011e27f0c1f36467dadbaefa224a4c09fe22","target":"record","created_at":"2026-07-05T09:09:17Z","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":"02f93cae7d328d0ca3ceafe7c336c4ea94e7d99d103c53988076c5aab3f61ad8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-19T07:39:00Z","title_canon_sha256":"8a38e85e75413e89c3089b86bfee75df5641606458605af322859be70e442631"},"schema_version":"1.0","source":{"id":"2310.12537","kind":"arxiv","version":5}},"canonical_sha256":"7c9483b06fa016e079001b466fdc23b8927d58654da3b9032a47f4e69c8d8e80","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7c9483b06fa016e079001b466fdc23b8927d58654da3b9032a47f4e69c8d8e80","first_computed_at":"2026-07-05T09:09:17.956036Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:09:17.956036Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ebMYAbYH8zSdSJtWT9njxOwqnXAZAEJrfpemIhsa4mj/0BzZ9dSE37uqUSyeoW7bG3CH+epQzhe7yhVHvondDw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:09:17.956715Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.12537","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:22f062c3a1d60f4beea65fc59cf7011e27f0c1f36467dadbaefa224a4c09fe22","sha256:9ed1a3e21be070f911032e1793ed422ecc43867751f2ef744986e87a3d587b40"],"state_sha256":"5c30490483acfc73dc976a5021b1bd21bd96d6c63b7765e85a1b204b8c74bb34"}